http://2010.igem.org/wiki/index.php?title=Special:Contributions/Ricky&feed=atom&limit=50&target=Ricky&year=&month=2010.igem.org - User contributions [en]2020-09-19T14:01:42ZFrom 2010.igem.orgMediaWiki 1.16.5http://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-28T02:36:17Z<p>Ricky: /* Kinetic modelling of the ethylene generator */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are outlined in the image Figure 1 (left), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our E.coli cells. Where, in addition to the qualitative representation (figure 2 & 4) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. Figure 2 or Model 1 was designed to include fixed concentrations at a steady state ratio of 1:1:1. This design ensured sensible approximate values of ethylene output were obtained given the correctness of enzyme rates and other parameters used. <br />
<br />
<br />
Adapting on to model 1, quantitative data in silico corresponding to mRNA transcription, protein translation, and degradation were implimented into a more complex design - giving model 2. As a result, more realistic outputs of ethylene could be found. This design could then be used to compare and contrast actual ethylene values obtained experimentally. Hence the effiency of our E.coli cells could then be calculated. Depending on the effiency value, certain factors and variables could be changed in order to attempt to reach our tinkercell output. <br />
<br />
The benefit of using Tinkercell to better understand our system, subsequently also allows us to distinguish the practicality of our project. That is, for example, if low levels of ethylene were predicted to be produced from the model at certain parameters, then efforts into favouring and adapting our system as a plant signalling/communication system could be aimed for in reality under those sorts of conditions. In contrast, if ethylene production was indicated by the model to be high for example, then again, under those certain set of parameters efforts could be re-directed into shaping the objective of the system to be used for plastic production. Hence, by modelling our project using Tinkercell, direction into where the project may lead to in the future can be better defined.<br />
<br />
==<H2> Objectives </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This relatively simple model - as represented in figure 2 - was designed to be the starting point of our kinetic modelling. This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes involved were SAM synthetase, ACC synthase and ACC oxidase - also known as EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. Although basic, this model in essence was able to give reasonable output values for ethylene produced. Furthermore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
<br />
<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation:<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes. <p> </p><br />
- No product inhibition. <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mechanisms in E.coli. <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maximum yield of ethylene. <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet/SAM)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
Using Tinkercell a graphical representation of 'Model 1' was generated (Figure 3) using deterministic stimulation.<br />
In this figure a graphical representation of the rates - generated internally by Tinkercell's mathematical alogarithms - of ethylene production are shown for each fixed enzyme concentration assumed.<br />
As a result not only is production of ethylene shown to be increasing linearly with time, but at a greater rate with increasing concentration of enzyme (SAM synthetase, ACC synthase, EFE) used.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: A visual construction of Model 2 using ''Tinkercell'', where transcription and translation are taken into account ]]<br />
<br />
Model 2 explores the more complicated adaptation of model 1 by incorporating the transcription, translation, and degradation rates of protein and mRNA into the final design. As a result a more realistic model of what would really be happening in the cell leading to ethylene production could be achieved. Of all the sources studied, it was decided that values from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model] would be most sensible and relevant to our system. Therefore the degradation, transcription and translation rates were adapted from this model onto ours to factor in fluctuations in concentrations of our three enzymes, as unlike in model 1, enzyme concentration would not realistically be in a 1:1:1 ratio. Therefore using these values, model 2 was able to more realistically follow the metabolic pathway of ethylene production where fluctuations in enzyme concentration were taken into consideration – rather than having them fixed as in model 1. <br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-------<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Figure 5: Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
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<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Figure 6: Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin the enzymes are produced at a realitively fast rate however as time proceeds they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be depleated. Hence this graph suggests plausibility for approximately the first 10 seconds of production; sfter that the data looks unrealistic and implausible in an E.coli cell.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Figure 7: Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate, however due to the confunding aspects of excess enzyme production, it is unclear if this graph truly represents the total ethylene output. However it can be assumed that the first few seconds accurately portray the ethylene output.</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-28T02:16:19Z<p>Ricky: /* Model 1 */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are outlined in the image Figure 1 (left), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our E.coli cells. Where, in addition to the qualitative representation (figure 2 & 4) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. Figure 2 or Model 1 was designed to include fixed concentrations at a steady state ratio of 1:1:1. This design ensured sensible approximate values of ethylene output were obtained given the correctness of enzyme rates and other parameters used. <br />
<br />
<br />
Adapting on to model 1, quantitative data in silico corresponding to mRNA transcription, protein translation, and degradation were implimented into a more complex design - giving model 2. As a result, more realistic outputs of ethylene could be found. This design could then be used to compare and contrast actual ethylene values obtained experimentally. Hence the effiency of our E.coli cells could then be calculated. Depending on the effiency value, certain factors and variables could be changed in order to attempt to reach our tinkercell output. <br />
<br />
Another use of the tinker cell model is that it would allow us to understand the pathway in which we chose to take our project. That is, with low concnetrations of ethylene produced, possiblity of the use of the system as a form of plant communicating system would be strongly favoured. In contrast if ethylene produced was found to be in high concentration then the system could strongly possibly used in plastic productions. Therefore the tinkercell program also gives an indication of which direction this particular Monash iGEM project could take given the prospects it carries.<br />
<br />
==<H2> Objectives </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This relatively simple model - as represented in figure 2 - was designed to be the starting point of our kinetic modelling. This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes involved were SAM synthetase, ACC synthase and ACC oxidase - also known as EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. Although basic, this model in essence was able to give reasonable output values for ethylene produced. Furthermore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
<br />
<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation:<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes. <p> </p><br />
- No product inhibition. <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mechanisms in E.coli. <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maximum yield of ethylene. <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet/SAM)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
Using Tinkercell a graphical representation of 'Model 1' was generated (Figure 3) using deterministic stimulation.<br />
In this figure a graphical representation of the rates - generated internally by Tinkercell's mathematical alogarithms - of ethylene production are shown for each fixed enzyme concentration assumed.<br />
As a result not only is production of ethylene shown to be increasing linearly with time, but at a greater rate with increasing concentration of enzyme (SAM synthetase, ACC synthase, EFE) used.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: A visual construction of Model 2 using ''Tinkercell'', where transcription and translation are taken into account ]]<br />
<br />
Model 2 explores the more complicated adaptation of model 1 by incorporating the transcription, translation, and degradation rates of protein and mRNA into the final design. As a result a more realistic model of what would really be happening in the cell leading to ethylene production could be achieved. Of all the sources studied, it was decided that values from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model] would be most sensible and relevant to our system. Therefore the degradation, transcription and translation rates were adapted from this model onto ours to factor in fluctuations in concentrations of our three enzymes, as unlike in model 1, enzyme concentration would not realistically be in a 1:1:1 ratio. Therefore using these values, model 2 was able to more realistically follow the metabolic pathway of ethylene production where fluctuations in enzyme concentration were taken into consideration – rather than having them fixed as in model 1. <br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-------<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Figure 5: Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Figure 6: Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin the enzymes are produced at a realitively fast rate however as time proceeds they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be depleated. Hence this graph suggests plausibility for approximately the first 10 seconds of production; sfter that the data looks unrealistic and implausible in an E.coli cell.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Figure 7: Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate, however due to the confunding aspects of excess enzyme production, it is unclear if this graph truly represents the total ethylene output. However it can be assumed that the first few seconds accurately portray the ethylene output.</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-28T02:14:47Z<p>Ricky: /* Model 1 */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are outlined in the image Figure 1 (left), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our E.coli cells. Where, in addition to the qualitative representation (figure 2 & 4) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. Figure 2 or Model 1 was designed to include fixed concentrations at a steady state ratio of 1:1:1. This design ensured sensible approximate values of ethylene output were obtained given the correctness of enzyme rates and other parameters used. <br />
<br />
<br />
Adapting on to model 1, quantitative data in silico corresponding to mRNA transcription, protein translation, and degradation were implimented into a more complex design - giving model 2. As a result, more realistic outputs of ethylene could be found. This design could then be used to compare and contrast actual ethylene values obtained experimentally. Hence the effiency of our E.coli cells could then be calculated. Depending on the effiency value, certain factors and variables could be changed in order to attempt to reach our tinkercell output. <br />
<br />
Another use of the tinker cell model is that it would allow us to understand the pathway in which we chose to take our project. That is, with low concnetrations of ethylene produced, possiblity of the use of the system as a form of plant communicating system would be strongly favoured. In contrast if ethylene produced was found to be in high concentration then the system could strongly possibly used in plastic productions. Therefore the tinkercell program also gives an indication of which direction this particular Monash iGEM project could take given the prospects it carries.<br />
<br />
==<H2> Objectives </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This relatively simple model - as represented in figure 2 - was designed to be the starting point of our kinetic modelling. This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes involved were SAM synthetase, ACC synthase and ACC oxidase - also known as EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. Although basic, this model in essence was able to give reasonable output values for ethylene produced. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
<br />
<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation:<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes. <p> </p><br />
- No product inhibition. <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mechanisms in E.coli. <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maximum yield of ethylene. <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet/SAM)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
Using Tinkercell a graphical representation of 'Model 1' was generated (Figure 3) using deterministic stimulation.<br />
In this figure a graphical representation of the rates - generated internally by Tinkercell's mathematical alogarithms - of ethylene production are shown for each fixed enzyme concentration assumed.<br />
As a result not only is production of ethylene shown to be increasing linearly with time, but at a greater rate with increasing concentration of enzyme (SAM synthetase, ACC synthase, EFE) used.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: A visual construction of Model 2 using ''Tinkercell'', where transcription and translation are taken into account ]]<br />
<br />
Model 2 explores the more complicated adaptation of model 1 by incorporating the transcription, translation, and degradation rates of protein and mRNA into the final design. As a result a more realistic model of what would really be happening in the cell leading to ethylene production could be achieved. Of all the sources studied, it was decided that values from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model] would be most sensible and relevant to our system. Therefore the degradation, transcription and translation rates were adapted from this model onto ours to factor in fluctuations in concentrations of our three enzymes, as unlike in model 1, enzyme concentration would not realistically be in a 1:1:1 ratio. Therefore using these values, model 2 was able to more realistically follow the metabolic pathway of ethylene production where fluctuations in enzyme concentration were taken into consideration – rather than having them fixed as in model 1. <br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-------<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Figure 5: Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Figure 6: Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin the enzymes are produced at a realitively fast rate however as time proceeds they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be depleated. Hence this graph suggests plausibility for approximately the first 10 seconds of production; sfter that the data looks unrealistic and implausible in an E.coli cell.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Figure 7: Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate, however due to the confunding aspects of excess enzyme production, it is unclear if this graph truly represents the total ethylene output. However it can be assumed that the first few seconds accurately portray the ethylene output.</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-28T02:09:45Z<p>Ricky: /* Model 2 */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are outlined in the image Figure 1 (left), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our E.coli cells. Where, in addition to the qualitative representation (figure 2 & 4) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. Figure 2 or Model 1 was designed to include fixed concentrations at a steady state ratio of 1:1:1. This design ensured sensible approximate values of ethylene output were obtained given the correctness of enzyme rates and other parameters used. <br />
<br />
<br />
Adapting on to model 1, quantitative data in silico corresponding to mRNA transcription, protein translation, and degradation were implimented into a more complex design - giving model 2. As a result, more realistic outputs of ethylene could be found. This design could then be used to compare and contrast actual ethylene values obtained experimentally. Hence the effiency of our E.coli cells could then be calculated. Depending on the effiency value, certain factors and variables could be changed in order to attempt to reach our tinkercell output. <br />
<br />
Another use of the tinker cell model is that it would allow us to understand the pathway in which we chose to take our project. That is, with low concnetrations of ethylene produced, possiblity of the use of the system as a form of plant communicating system would be strongly favoured. In contrast if ethylene produced was found to be in high concentration then the system could strongly possibly used in plastic productions. Therefore the tinkercell program also gives an indication of which direction this particular Monash iGEM project could take given the prospects it carries.<br />
<br />
==<H2> Objectives </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This relatively simple model - as represented in figure 2 - was designed so to be the starting point of our kinetic modelling. This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase - also known as EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
<br />
<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation:<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes. <p> </p><br />
- No product inhibition. <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mechanisms in E.coli. <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maximum yield of ethylene. <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet/SAM)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
Using Tinkercell a graphical representation of 'Model 1' was generated (Figure 3) using deterministic stimulation.<br />
In this figure a graphical representation of the rates - generated internally by Tinkercell's mathematical alogarithms - of ethylene production are shown for each fixed enzyme concentration assumed.<br />
As a result not only is production of ethylene shown to be increasing linearly with time, but at a greater rate with increasing concentration of enzyme (SAM synthetase, ACC synthase, EFE) used.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: A visual construction of Model 2 using ''Tinkercell'', where transcription and translation are taken into account ]]<br />
<br />
Model 2 explores the more complicated adaptation of model 1 by incorporating the transcription, translation, and degradation rates of protein and mRNA into the final design. As a result a more realistic model of what would really be happening in the cell leading to ethylene production could be achieved. Of all the sources studied, it was decided that values from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model] would be most sensible and relevant to our system. Therefore the degradation, transcription and translation rates were adapted from this model onto ours to factor in fluctuations in concentrations of our three enzymes, as unlike in model 1, enzyme concentration would not realistically be in a 1:1:1 ratio. Therefore using these values, model 2 was able to more realistically follow the metabolic pathway of ethylene production where fluctuations in enzyme concentration were taken into consideration – rather than having them fixed as in model 1. <br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-------<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Figure 5: Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Figure 6: Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin the enzymes are produced at a realitively fast rate however as time proceeds they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be depleated. Hence this graph suggests plausibility for approximately the first 10 seconds of production; sfter that the data looks unrealistic and implausible in an E.coli cell.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Figure 7: Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate, however due to the confunding aspects of excess enzyme production, it is unclear if this graph truly represents the total ethylene output. However it can be assumed that the first few seconds accurately portray the ethylene output.</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-28T02:05:18Z<p>Ricky: /* Model 2 */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are outlined in the image Figure 1 (left), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our E.coli cells. Where, in addition to the qualitative representation (figure 2 & 4) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. Figure 2 or Model 1 was designed to include fixed concentrations at a steady state ratio of 1:1:1. This design ensured sensible approximate values of ethylene output were obtained given the correctness of enzyme rates and other parameters used. <br />
<br />
<br />
Adapting on to model 1, quantitative data in silico corresponding to mRNA transcription, protein translation, and degradation were implimented into a more complex design - giving model 2. As a result, more realistic outputs of ethylene could be found. This design could then be used to compare and contrast actual ethylene values obtained experimentally. Hence the effiency of our E.coli cells could then be calculated. Depending on the effiency value, certain factors and variables could be changed in order to attempt to reach our tinkercell output. <br />
<br />
Another use of the tinker cell model is that it would allow us to understand the pathway in which we chose to take our project. That is, with low concnetrations of ethylene produced, possiblity of the use of the system as a form of plant communicating system would be strongly favoured. In contrast if ethylene produced was found to be in high concentration then the system could strongly possibly used in plastic productions. Therefore the tinkercell program also gives an indication of which direction this particular Monash iGEM project could take given the prospects it carries.<br />
<br />
==<H2> Objectives </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This relatively simple model - as represented in figure 2 - was designed so to be the starting point of our kinetic modelling. This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase - also known as EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
<br />
<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation:<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes. <p> </p><br />
- No product inhibition. <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mechanisms in E.coli. <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maximum yield of ethylene. <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet/SAM)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
Using Tinkercell a graphical representation of 'Model 1' was generated (Figure 3) using deterministic stimulation.<br />
In this figure a graphical representation of the rates - generated internally by Tinkercell's mathematical alogarithms - of ethylene production are shown for each fixed enzyme concentration assumed.<br />
As a result not only is production of ethylene shown to be increasing linearly with time, but at a greater rate with increasing concentration of enzyme (SAM synthetase, ACC synthase, EFE) used.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: A visual construction of Model 2 using ''Tinkercell'', where transcription and translation are taken into account ]]<br />
<br />
Model 2 explores the more complicated adaptation of model 1 by incorporating the transcription, translation, and degradation rates of protein and mRNA into the final design. As a result a more realistic model of what would really be happening in the cell leading to ethylene production could be achieved. Of all the sources studied, it was decided that values from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model] would be most sensible and relevant to our system. Therefore the degradation, transcription and translation rates were adapted from this model onto ours to factor in fluctuations in concentrations of our three enzymes, as unlike in model 1, enzyme concentration would not realistically be in a 1:1:1 ratio. Therefore using these values, model 2 was able to more realistically follow the metabolic pathway of ethylene production where fluctuations in enzyme concentration were taken into consideration – rather than having them fixed as in model 1. <br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-------<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Figure 5: Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Figure 6: Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin the enzymes are produced at a realitively fast rate however as time proceeds they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be depleated. Hence this graph indicates plausible for approximately the first 10 seconds of production. After that the data looks unrealistic and implausible in an E.coli cell.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Figure 7: Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate, however due to the confunding aspects of excess enzyme production, it is unclear if this graph truly represents the total ethylene output. However it can be assumed that the first few seconds accurately portray the ethylene output.</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-28T01:58:07Z<p>Ricky: /* Model 2 */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are outlined in the image Figure 1 (left), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our E.coli cells. Where, in addition to the qualitative representation (figure 2 & 4) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. Figure 2 or Model 1 was designed to include fixed concentrations at a steady state ratio of 1:1:1. This design ensured sensible approximate values of ethylene output were obtained given the correctness of enzyme rates and other parameters used. <br />
<br />
<br />
Adapting on to model 1, quantitative data in silico corresponding to mRNA transcription, protein translation, and degradation were implimented into a more complex design - giving model 2. As a result, more realistic outputs of ethylene could be found. This design could then be used to compare and contrast actual ethylene values obtained experimentally. Hence the effiency of our E.coli cells could then be calculated. Depending on the effiency value, certain factors and variables could be changed in order to attempt to reach our tinkercell output. <br />
<br />
Another use of the tinker cell model is that it would allow us to understand the pathway in which we chose to take our project. That is, with low concnetrations of ethylene produced, possiblity of the use of the system as a form of plant communicating system would be strongly favoured. In contrast if ethylene produced was found to be in high concentration then the system could strongly possibly used in plastic productions. Therefore the tinkercell program also gives an indication of which direction this particular Monash iGEM project could take given the prospects it carries.<br />
<br />
==<H2> Objectives </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This relatively simple model - as represented in figure 2 - was designed so to be the starting point of our kinetic modelling. This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase - also known as EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
<br />
<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation:<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes. <p> </p><br />
- No product inhibition. <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mechanisms in E.coli. <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maximum yield of ethylene. <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet/SAM)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
Using Tinkercell a graphical representation of 'Model 1' was generated (Figure 3) using deterministic stimulation.<br />
In this figure a graphical representation of the rates - generated internally by Tinkercell's mathematical alogarithms - of ethylene production are shown for each fixed enzyme concentration assumed.<br />
As a result not only is production of ethylene shown to be increasing linearly with time, but at a greater rate with increasing concentration of enzyme (SAM synthetase, ACC synthase, EFE) used.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: A visual construction of Model 2 using ''Tinkercell'', where transcription and translation are taken into account ]]<br />
<br />
Model 2 explores the more complicated adaptation of model 1 by incorporating the transcription, translation, and degradation rates of the cell into the final design. As a result a more realistic model of what would really be happening in the cell leading to ethylene production could be achieved. Of all the sources studied, it was decided that values from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model] would be most sensible and relevant to our system. Therefore the transcription and translation rates were adapted from this model onto ours to factor in fluctuations in concentrations of our three enzymes, as unlike in model 1, enzyme concentration would not realistically be in a 1:1:1 ratio. Therefore using these values, model 2 was able to more realistically follow the metabolic pathway of ethylene production where fluctuations in enzyme concentration were taken into consideration – rather than having them fixed as in model 1. <br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-------<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Figure 5: Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Figure 6: Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin the enzymes are produced at a realitively fast rate however as time proceeds they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be depleated. Hence this graph indicates plausible for approximately the first 10 seconds of production. After that the data looks unrealistic and implausible in an E.coli cell.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Figure 7: Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate, however due to the confunding aspects of excess enzyme production, it is unclear if this graph truly represents the total ethylene output. However it can be assumed that the first few seconds accurately portray the ethylene output.</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-28T01:53:58Z<p>Ricky: /* Model 2 */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are outlined in the image Figure 1 (left), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our E.coli cells. Where, in addition to the qualitative representation (figure 2 & 4) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. Figure 2 or Model 1 was designed to include fixed concentrations at a steady state ratio of 1:1:1. This design ensured sensible approximate values of ethylene output were obtained given the correctness of enzyme rates and other parameters used. <br />
<br />
<br />
Adapting on to model 1, quantitative data in silico corresponding to mRNA transcription, protein translation, and degradation were implimented into a more complex design - giving model 2. As a result, more realistic outputs of ethylene could be found. This design could then be used to compare and contrast actual ethylene values obtained experimentally. Hence the effiency of our E.coli cells could then be calculated. Depending on the effiency value, certain factors and variables could be changed in order to attempt to reach our tinkercell output. <br />
<br />
Another use of the tinker cell model is that it would allow us to understand the pathway in which we chose to take our project. That is, with low concnetrations of ethylene produced, possiblity of the use of the system as a form of plant communicating system would be strongly favoured. In contrast if ethylene produced was found to be in high concentration then the system could strongly possibly used in plastic productions. Therefore the tinkercell program also gives an indication of which direction this particular Monash iGEM project could take given the prospects it carries.<br />
<br />
==<H2> Objectives </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This relatively simple model - as represented in figure 2 - was designed so to be the starting point of our kinetic modelling. This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase - also known as EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
<br />
<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation:<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes. <p> </p><br />
- No product inhibition. <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mechanisms in E.coli. <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maximum yield of ethylene. <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet/SAM)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
Using Tinkercell a graphical representation of 'Model 1' was generated (Figure 3) using deterministic stimulation.<br />
In this figure a graphical representation of the rates - generated internally by Tinkercell's mathematical alogarithms - of ethylene production are shown for each fixed enzyme concentration assumed.<br />
As a result not only is production of ethylene shown to be increasing linearly with time, but at a greater rate with increasing concentration of enzyme (SAM synthetase, ACC synthase, EFE) used.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: A visual construction of Model 2 using ''Tinkercell'', where transcription and translation are taken into account ]]<br />
<br />
Model 2 explores the more complicated adaptation of model 1 by incorporating the transcription, translation, and degradation rates of the cell into the final design. As a result a more realistic model of what would really be happening in the cell leading to ethylene production could be achieved. Of all the sources studied, it was decided that values from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model] would be most sensible and relevant to our system. Therefore the transcription and translation rates were adapted from this model onto ours to factor in fluctuations in concentrations of our three enzymes, as unlike in model 1, enzyme concentration would not be in a 1:1:1 steady state ratio. Therefore using these values, model 2 was able to more realistically follow the metabolic pathway of ethylene production where fluctuations in enzyme concentration were taken into consideration – rather than having them fixed as in model 1. <br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-------<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Figure 5: Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Figure 6: Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin the enzymes are produced at a realitively fast rate however as time proceeds they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be depleated. Hence this graph indicates plausible for approximately the first 10 seconds of production. After that the data looks unrealistic and implausible in an E.coli cell.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Figure 7: Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate, however due to the confunding aspects of excess enzyme production, it is unclear if this graph truly represents the total ethylene output. However it can be assumed that the first few seconds accurately portray the ethylene output.</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-28T01:18:19Z<p>Ricky: /* Model 2 */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are outlined in the image Figure 1 (left), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our E.coli cells. Where, in addition to the qualitative representation (figure 2 & 4) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. Figure 2 or Model 1 was designed to include fixed concentrations at a steady state ratio of 1:1:1. This design ensured sensible approximate values of ethylene output were obtained given the correctness of enzyme rates and other parameters used. <br />
<br />
<br />
Adapting on to model 1, quantitative data in silico corresponding to mRNA transcription, protein translation, and degradation were implimented into a more complex design - giving model 2. As a result, more realistic outputs of ethylene could be found. This design could then be used to compare and contrast actual ethylene values obtained experimentally. Hence the effiency of our E.coli cells could then be calculated. Depending on the effiency value, certain factors and variables could be changed in order to attempt to reach our tinkercell output. <br />
<br />
Another use of the tinker cell model is that it would allow us to understand the pathway in which we chose to take our project. That is, with low concnetrations of ethylene produced, possiblity of the use of the system as a form of plant communicating system would be strongly favoured. In contrast if ethylene produced was found to be in high concentration then the system could strongly possibly used in plastic productions. Therefore the tinkercell program also gives an indication of which direction this particular Monash iGEM project could take given the prospects it carries.<br />
<br />
==<H2> Objectives </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This relatively simple model - as represented in figure 2 - was designed so to be the starting point of our kinetic modelling. This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase - also known as EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
<br />
<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation:<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes. <p> </p><br />
- No product inhibition. <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mechanisms in E.coli. <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maximum yield of ethylene. <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet/SAM)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
Using Tinkercell a graphical representation of 'Model 1' was generated (Figure 3) using deterministic stimulation.<br />
In this figure a graphical representation of the rates - generated internally by Tinkercell's mathematical alogarithms - of ethylene production are shown for each fixed enzyme concentration assumed.<br />
As a result not only is production of ethylene shown to be increasing linearly with time, but at a greater rate with increasing concentration of enzyme (SAM synthetase, ACC synthase, EFE) used.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: A visual construction of Model 2 using ''Tinkercell'', where transcription and translation are taken into account ]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degradation, transcription and translation rates. These values were obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. As a result this model is expected to produce a more accurate representation of the ethylene producing pathway.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-------<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Figure 5: Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Figure 6: Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin the enzymes are produced at a realitively fast rate however as time proceeds they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be depleated. Hence this graph indicates plausible for approximately the first 10 seconds of production. After that the data looks unrealistic and implausible in an E.coli cell.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Figure 7: Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate, however due to the confunding aspects of excess enzyme production, it is unclear if this graph truly represents the total ethylene output. However it can be assumed that the first few seconds accurately portray the ethylene output.</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T18:41:00Z<p>Ricky: /* Kinetic modelling of the ethylene generator */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are outlined in the image Figure 1 (left), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our E.coli cells. Where, in addition to the qualitative representation (figure 2 & 4) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. Figure 2 or Model 1 was designed to include fixed concentrations at a steady state ratio of 1:1:1. This design ensured sensible approximate values of ethylene output were obtained given the correctness of enzyme rates and other parameters used. <br />
<br />
<br />
Adapting on to model 1, quantitative data in silico corresponding to mRNA transcription, protein translation, and degradation were implimented into a more complex design - giving model 2. As a result, more realistic outputs of ethylene could be found. This design could then be used to compare and contrast actual ethylene values obtained experimentally. Hence the effiency of our E.coli cells could then be calculated. Depending on the effiency value, certain factors and variables could be changed in order to attempt to reach our tinkercell output. <br />
<br />
Another use of the tinker cell model is that it would allow us to understand the pathway in which we chose to take our project. That is, with low concnetrations of ethylene produced, possiblity of the use of the system as a form of plant communicating system would be strongly favoured. In contrast if ethylene produced was found to be in high concentration then the system could strongly possibly used in plastic productions. Therefore the tinkercell program also gives an indication of which direction this particular Monash iGEM project could take given the prospects it carries.<br />
<br />
==<H2> Objectives </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This relatively simple model - as represented in figure 2 - was designed so to be the starting point of our kinetic modelling. This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase - also known as EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
<br />
<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation:<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes. <p> </p><br />
- No product inhibition. <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mechanisms in E.coli. <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maximum yield of ethylene. <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet/SAM)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
Using Tinkercell a graphical representation of 'Model 1' was generated (Figure 3) using deterministic stimulation.<br />
In this figure a graphical representation of the rates - generated internally by Tinkercell's mathematical alogarithms - of ethylene production are shown for each fixed enzyme concentration assumed.<br />
As a result not only is production of ethylene shown to be increasing linearly with time, but at a greater rate with increasing concentration of enzyme (SAM synthetase, ACC synthase, EFE) used.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: A visual construction of Model 2 using ''Tinkercell'', where transcription and translation are taken into account ]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degradation, transcription and translation rates. These values were obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. As a result this model is expected to produce a more accurate representation of the ethylene producing pathway.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-------<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Figure 5: Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Figure 6: Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin the enzymes are produced at a realitively fast rate however as time proceeds they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be depleated. Hence this graph indicates plausible for approximately the first 10 seconds of production. After that the data looks unrealistic and implausible in an E.coli cell.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Figure 7: Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate, however due to the confunding aspects of excess enzyme production, it is unclear if this graph truly represents the total ethylene output. However it can be assumed that the first few seconds accurately portray the ethylene output.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T12:41:20Z<p>Ricky: /* Aims of the Kinetic Modelling */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are outlined in the image Figure 1 (left), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our E.coli cells. Where, in addition to the qualitative representation (figure 2 & 4) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. Figure 2 or Model 1 was designed to include fixed concentrations at a steady state ratio of 1:1:1. This design ensured that ballpark ethylene figures were obtained and all parameters and enzyme rates were in correct proportions to achieve a reasonable output. <br />
<br />
<br />
In addition to model 1, quantitative data in silico corresponding to mRNA, protein translation, transcription and degradation were implimented into a more complex design. As a result, more realistic outputs of ethylene could be found. This design, model 2 could then be used to comapre and contrast actual ethylene values obtained via experimental design. Hence the effiency of our E.coli cells could then be calculated. Depending on the effiency value, certainn factors and variables could be changed in order to attempt to reach our tinkercell output. <br />
<br />
Another use of the tinker cell model is that it will allow us to understand the pathway in which we choose to take our project. Low concnetrations of ethylene will allow ethylene to be used in a plant comunicating systems. However if ethylene present in high concentrations then the ethylene could be used in plastic productions. Therefore the tinkercell system gives us an indication of which direction the Monash iGEM team can take in future years.<br />
<br />
However ultimately the tinkercell design is used as a mechanism in which we will be able to used to enhance a future ethylene generator for maximum output.<br />
<br />
==<H2> Objectives </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This relatively simple model - as represented in figure 2 - was designed so to be the starting point of our kinetic modelling. This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase - also known as EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
<br />
<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation:<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes. <p> </p><br />
- No product inhibition. <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mechanisms in E.coli. <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maximum yield of ethylene. <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet/SAM)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
Using Tinkercell a graphical representation of 'Model 1' was generated (Figure 3) using deterministic stimulation.<br />
In this figure a graphical representation of the rates - generated internally by Tinkercell's mathematical alogarithms - of ethylene production are shown for each fixed enzyme concentration assumed.<br />
As a result not only is production of ethylene shown to be increasing linearly with time, but at a greater rate with increasing concentration of enzyme (SAM synthetase, ACC synthase, EFE) used.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: A visual construction of Model 2 using ''Tinkercell'', where transcription and translation are taken into account ]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degradation, transcription and translation rates. These values were obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. As a result this model is expected to produce a more accurate representation of the ethylene producing pathway.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-------<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Figure 5: Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Figure 6: Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin the enzymes are produced at a realitively fast rate however as time proceeds they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be depleated. Hence this graph indicates plausible for approximately the first 10 seconds of production. After that the data looks unrealistic and implausible in an E.coli cell.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Figure 7: Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate, however due to the confunding aspects of excess enzyme production, it is unclear if this graph truly represents the total ethylene output. However it can be assumed that the first few seconds accurately portray the ethylene output.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/TeamTeam:Monash Australia/Team2010-10-27T12:28:09Z<p>Ricky: /* Who we are */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
== '''Who we are''' ==<br />
'''Advisors:'''<br />
<HTML><br />
<Center><br />
<table border="0"><br />
<tr><br />
<td><br />
<center><img src="http://2010.igem.org/wiki/images/6/64/Monash_Australia_Ash.jpg" height="200px" alt="Ash"</center><br />
</td><br />
<td><br />
Assoc. Prof Ashley Buckle currently is running a lab in the Monash STRIP under the Department of Biochemistry and Molecular Biology with research aimed at understanding how the structure and dynamics of proteins dictates their function. More information can be found at <a href="http://2010.igem.org/Team:Monash_Australia/Lab">Buckle Lab</a><br />
</td><br />
</tr><br />
<tr><br />
<td><br />
<center><img src="http://2010.igem.org/wiki/images/d/dc/Andrew_Perry.png" height="200px" alt="andrew"></img></center><br />
</td><br />
<td><br />
Dr. Andrew Perry is a Postdoctoral Fellow in the Whisstock lab, with research focusing on outer membrane pore forming proteins in mitochondria and bacteria. Structural biology and bioinformatics, protein structure by Nuclear Magnetic Resonance and X-ray crystallography.<br />
</td><br />
<tr><br />
<td><br />
<center><img src="http://2010.igem.org/wiki/images/b/b8/Monash_Australia_Llyod.jpg" height="200px" alt="lloyd"></img></center><br />
</td><br />
<td><br />
Dr. Lloyd Low is a Postdoctoral researcher in Peter Boag's lab in the the Department of Biochemistry and Molecular Biology Monash University. He is a former member of the Melbourne University iGEM team, and helped get us started at Monash.<br />
</td><br />
</tr><br />
</table><br />
</HTML><br />
<br />
'''Students:'''<br />
<br />
<HTML><br />
<Center><br />
<table border="0"><br />
<tr><br />
<td><br />
<center><img src="http://2010.igem.org/wiki/images/2/23/Monash_Australia_Team_member_1.png" height="200px" alt="Ben"></img></center><br />
</td><br />
<td><br />
Ben is currently a second year Bachelor of Science student, Majoring in Biochemistry and molecular biology with a minor in Microbiology. Ben aspires to open up his own research and development company after graduating.<br />
</td><br />
</tr><br />
<tr><br />
<td><br />
<center><img src="http://2010.igem.org/wiki/images/6/63/Monash_Australia_will.jpg" height="200px" width="150px" alt="will"></img></center><br />
</td><br />
<td><br />
Will is a final year Bachelor of science student, with majors in 'Biochemistry' and 'Immnology and Human Pathology'. Will is currently seeking honours placement for 2011, and strives to earn a PhD scholarship.<br />
</td><br />
<tr><br />
<td><br />
<center><img src="http://2010.igem.org/wiki/images/6/69/Monash_Australia_Team_member_3.jpg" height="200px" alt="anna"></img></center><br />
</td><br />
<td><br />
Anna is in her final year studying a Bachelor of Science with majors in Biochemistry and Physiology. She intends to further her studies by continuing with honours and pursuing a PhD. <br />
</td><br />
</tr><br />
<tr><br />
<td><br />
<center><img src="http://2010.igem.org/wiki/images/2/2d/Monash_Australia_Team_member_4.png" height="200px"></img></center><br />
</td><br />
<td><br />
Daniel is a first year bachelor of Biomedical science student and on completion of his degree will be a biochemistry or molecuar biology major. Daniel will further his studies with a honours and eventually a PhD, and wishes to travel the world working for various laboratories as a Post doctoral research fellow.<br />
</td><br />
</tr><br />
<tr><br />
<td><br />
<br />
<center><img src="http://2010.igem.org/wiki/images/7/73/Rw.jpg" height="200px"></img></center><br />
</td><br />
<br />
<td><br />
Ricky is a third year Bachelor of Science student majoring in Biochemistry and Molecular biology, and as of the beginning of 2010, also a first year engineering student looking at majoring in Chemical engineering. Upon graduation, with his knowledge of Biochemistry and Molecular biology and specialisation in Chemical engineering he wishes to find a niche between the two disciplines; participating in the igem competition is a great stepping stone.<br />
</td><br />
</tr><br />
<br />
</table><br />
</Center> <br />
</HTML><br />
<br />
== '''What we did''' ==<br />
<br />
For team Monash Australia we are currently balancing iGEM work with semester one and two of university, with the Jamboree around out end of year examinations. Due to this we have equally divided all work between us to help us juggle university study and iGEM project. Much of the iGEM project would have not been possible if it was not for the drive, energy and passion that both Ben and Andrew have given, Thanks guys.</div>Rickyhttp://2010.igem.org/File:Rw.jpgFile:Rw.jpg2010-10-27T12:25:06Z<p>Ricky: </p>
<hr />
<div></div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T11:58:50Z<p>Ricky: /* ACC --> Ethylene */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are outlined in the image Figure 1 (left), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our E.coli cells. Where, in addition to the qualitative representation (figure 2 & 4) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. Figure 2 or Model 1 was designed to include fixed concentrations at a steady state ratio of 1:1:1. This design ensured that ballpark ethylene figures were obtained and all parameters and enzyme rates were in correct proportions to achieve a reasonable output. <br />
<br />
<br />
In addition to model 1, quantitative data in silico corresponding to mRNA, protein translation, transcription and degradation were implimented into a more complex design. As a result, more realistic outputs of ethylene could be found. This design, model 2 could then be used to comapre and contrast actual ethylene values obtained via experimental design. Hence the effiency of our E.coli cells could then be calculated. Depending on the effiency value, certainn factors and variables could be changed in order to attempt to reach our tinkercell output. <br />
<br />
Another use of the tinker cell model is that it will allow us to understand the pathway in which we choose to take our project. Low concnetrations of ethylene will allow ethylene to be used in a plant comunicating systems. However if ethylene present in high concentrations then the ethylene could be used in plastic productions. Therefore the tinkercell system gives us an indication of which direction the Monash iGEM team can take in future years.<br />
<br />
However ultimately the tinkercell design is used as a mechanism in which we will be able to used to enhance a future ethylene generator for maximum output.<br />
<br />
==<H2> Aims of the Kinetic Modelling </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This relatively simple model - as represented in figure 2 - was designed so to be the starting point of our kinetic modelling. This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase - also known as EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
<br />
<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation:<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes. <p> </p><br />
- No product inhibition. <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mechanisms in E.coli. <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maximum yield of ethylene. <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet/SAM)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
Using Tinkercell a graphical representation of 'Model 1' was generated (Figure 3) using deterministic stimulation.<br />
In this figure a graphical representation of the rates - generated internally by Tinkercell's mathematical alogarithms - of ethylene production are shown for each fixed enzyme concentration assumed.<br />
As a result not only is production of ethylene shown to be increasing linearly with time, but at a greater rate with increasing concentration of enzyme (SAM synthetase, ACC synthase, EFE) used.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: A visual construction of Model 2 using ''Tinkercell'', where transcription and translation are taken into account ]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degradation, transcription and translation rates. These values were obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. As a result this model is expected to produce a more accurate representation of the ethylene producing pathway.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-------<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Figure 5: Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Figure 6: Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin the enzymes are produced at a realitively fast rate however as time proceeds they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be depleated. Hence this graph indicates plausible for approximately the first 10 seconds of production. After that the data looks unrealistic and implausible in an E.coli cell.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Figure 7: Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate, however due to the confunding aspects of excess enzyme production, it is unclear if this graph truly represents the total ethylene output. However it can be assumed that the first few seconds accurately portray the ethylene output.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T11:58:30Z<p>Ricky: /* SAM --> ACC */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are outlined in the image Figure 1 (left), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our E.coli cells. Where, in addition to the qualitative representation (figure 2 & 4) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. Figure 2 or Model 1 was designed to include fixed concentrations at a steady state ratio of 1:1:1. This design ensured that ballpark ethylene figures were obtained and all parameters and enzyme rates were in correct proportions to achieve a reasonable output. <br />
<br />
<br />
In addition to model 1, quantitative data in silico corresponding to mRNA, protein translation, transcription and degradation were implimented into a more complex design. As a result, more realistic outputs of ethylene could be found. This design, model 2 could then be used to comapre and contrast actual ethylene values obtained via experimental design. Hence the effiency of our E.coli cells could then be calculated. Depending on the effiency value, certainn factors and variables could be changed in order to attempt to reach our tinkercell output. <br />
<br />
Another use of the tinker cell model is that it will allow us to understand the pathway in which we choose to take our project. Low concnetrations of ethylene will allow ethylene to be used in a plant comunicating systems. However if ethylene present in high concentrations then the ethylene could be used in plastic productions. Therefore the tinkercell system gives us an indication of which direction the Monash iGEM team can take in future years.<br />
<br />
However ultimately the tinkercell design is used as a mechanism in which we will be able to used to enhance a future ethylene generator for maximum output.<br />
<br />
==<H2> Aims of the Kinetic Modelling </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This relatively simple model - as represented in figure 2 - was designed so to be the starting point of our kinetic modelling. This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase - also known as EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
<br />
<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation:<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes. <p> </p><br />
- No product inhibition. <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mechanisms in E.coli. <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maximum yield of ethylene. <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet/SAM)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
Using Tinkercell a graphical representation of 'Model 1' was generated (Figure 3) using deterministic stimulation.<br />
In this figure a graphical representation of the rates - generated internally by Tinkercell's mathematical alogarithms - of ethylene production are shown for each fixed enzyme concentration assumed.<br />
As a result not only is production of ethylene shown to be increasing linearly with time, but at a greater rate with increasing concentration of enzyme (SAM synthetase, ACC synthase, EFE) used.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: A visual construction of Model 2 using ''Tinkercell'', where transcription and translation are taken into account ]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degradation, transcription and translation rates. These values were obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. As a result this model is expected to produce a more accurate representation of the ethylene producing pathway.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-------<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Figure 5: Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Figure 6: Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin the enzymes are produced at a realitively fast rate however as time proceeds they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be depleated. Hence this graph indicates plausible for approximately the first 10 seconds of production. After that the data looks unrealistic and implausible in an E.coli cell.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Figure 7: Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate, however due to the confunding aspects of excess enzyme production, it is unclear if this graph truly represents the total ethylene output. However it can be assumed that the first few seconds accurately portray the ethylene output.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
[[Image:acc_eth.jpg|200px|thumb|left|]]<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T11:58:18Z<p>Ricky: /* Met --> SAM (AdoMet) */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are outlined in the image Figure 1 (left), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our E.coli cells. Where, in addition to the qualitative representation (figure 2 & 4) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. Figure 2 or Model 1 was designed to include fixed concentrations at a steady state ratio of 1:1:1. This design ensured that ballpark ethylene figures were obtained and all parameters and enzyme rates were in correct proportions to achieve a reasonable output. <br />
<br />
<br />
In addition to model 1, quantitative data in silico corresponding to mRNA, protein translation, transcription and degradation were implimented into a more complex design. As a result, more realistic outputs of ethylene could be found. This design, model 2 could then be used to comapre and contrast actual ethylene values obtained via experimental design. Hence the effiency of our E.coli cells could then be calculated. Depending on the effiency value, certainn factors and variables could be changed in order to attempt to reach our tinkercell output. <br />
<br />
Another use of the tinker cell model is that it will allow us to understand the pathway in which we choose to take our project. Low concnetrations of ethylene will allow ethylene to be used in a plant comunicating systems. However if ethylene present in high concentrations then the ethylene could be used in plastic productions. Therefore the tinkercell system gives us an indication of which direction the Monash iGEM team can take in future years.<br />
<br />
However ultimately the tinkercell design is used as a mechanism in which we will be able to used to enhance a future ethylene generator for maximum output.<br />
<br />
==<H2> Aims of the Kinetic Modelling </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This relatively simple model - as represented in figure 2 - was designed so to be the starting point of our kinetic modelling. This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase - also known as EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
<br />
<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation:<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes. <p> </p><br />
- No product inhibition. <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mechanisms in E.coli. <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maximum yield of ethylene. <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet/SAM)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
Using Tinkercell a graphical representation of 'Model 1' was generated (Figure 3) using deterministic stimulation.<br />
In this figure a graphical representation of the rates - generated internally by Tinkercell's mathematical alogarithms - of ethylene production are shown for each fixed enzyme concentration assumed.<br />
As a result not only is production of ethylene shown to be increasing linearly with time, but at a greater rate with increasing concentration of enzyme (SAM synthetase, ACC synthase, EFE) used.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: A visual construction of Model 2 using ''Tinkercell'', where transcription and translation are taken into account ]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degradation, transcription and translation rates. These values were obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. As a result this model is expected to produce a more accurate representation of the ethylene producing pathway.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-------<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Figure 5: Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Figure 6: Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin the enzymes are produced at a realitively fast rate however as time proceeds they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be depleated. Hence this graph indicates plausible for approximately the first 10 seconds of production. After that the data looks unrealistic and implausible in an E.coli cell.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Figure 7: Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate, however due to the confunding aspects of excess enzyme production, it is unclear if this graph truly represents the total ethylene output. However it can be assumed that the first few seconds accurately portray the ethylene output.<br />
<br />
<br />
<br />
<br />
<br />
== SAM --> ACC ==<br />
<br />
[[Image:sam_acc.jpg|200px|thumb|left|]]<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
[[Image:acc_eth.jpg|200px|thumb|left|]]<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T11:58:01Z<p>Ricky: /* Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are outlined in the image Figure 1 (left), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our E.coli cells. Where, in addition to the qualitative representation (figure 2 & 4) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. Figure 2 or Model 1 was designed to include fixed concentrations at a steady state ratio of 1:1:1. This design ensured that ballpark ethylene figures were obtained and all parameters and enzyme rates were in correct proportions to achieve a reasonable output. <br />
<br />
<br />
In addition to model 1, quantitative data in silico corresponding to mRNA, protein translation, transcription and degradation were implimented into a more complex design. As a result, more realistic outputs of ethylene could be found. This design, model 2 could then be used to comapre and contrast actual ethylene values obtained via experimental design. Hence the effiency of our E.coli cells could then be calculated. Depending on the effiency value, certainn factors and variables could be changed in order to attempt to reach our tinkercell output. <br />
<br />
Another use of the tinker cell model is that it will allow us to understand the pathway in which we choose to take our project. Low concnetrations of ethylene will allow ethylene to be used in a plant comunicating systems. However if ethylene present in high concentrations then the ethylene could be used in plastic productions. Therefore the tinkercell system gives us an indication of which direction the Monash iGEM team can take in future years.<br />
<br />
However ultimately the tinkercell design is used as a mechanism in which we will be able to used to enhance a future ethylene generator for maximum output.<br />
<br />
==<H2> Aims of the Kinetic Modelling </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This relatively simple model - as represented in figure 2 - was designed so to be the starting point of our kinetic modelling. This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase - also known as EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
<br />
<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation:<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes. <p> </p><br />
- No product inhibition. <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mechanisms in E.coli. <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maximum yield of ethylene. <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet/SAM)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
Using Tinkercell a graphical representation of 'Model 1' was generated (Figure 3) using deterministic stimulation.<br />
In this figure a graphical representation of the rates - generated internally by Tinkercell's mathematical alogarithms - of ethylene production are shown for each fixed enzyme concentration assumed.<br />
As a result not only is production of ethylene shown to be increasing linearly with time, but at a greater rate with increasing concentration of enzyme (SAM synthetase, ACC synthase, EFE) used.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: A visual construction of Model 2 using ''Tinkercell'', where transcription and translation are taken into account ]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degradation, transcription and translation rates. These values were obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. As a result this model is expected to produce a more accurate representation of the ethylene producing pathway.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-------<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Figure 5: Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Figure 6: Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin the enzymes are produced at a realitively fast rate however as time proceeds they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be depleated. Hence this graph indicates plausible for approximately the first 10 seconds of production. After that the data looks unrealistic and implausible in an E.coli cell.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Figure 7: Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate, however due to the confunding aspects of excess enzyme production, it is unclear if this graph truly represents the total ethylene output. However it can be assumed that the first few seconds accurately portray the ethylene output.<br />
<br />
<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg|200px|thumb|left|]]<br />
<br />
== SAM --> ACC ==<br />
<br />
[[Image:sam_acc.jpg|200px|thumb|left|]]<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
[[Image:acc_eth.jpg|200px|thumb|left|]]<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T11:43:44Z<p>Ricky: /* Model 2 */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are outlined in the image Figure 1 (left), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our E.coli cells. Where, in addition to the qualitative representation (figure 2 & 4) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
==<H2> Aims of the Kinetic Modelling </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This relatively simple model - as represented in figure 2 - was designed so to be the starting point of our kinetic modelling. This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase - also known as EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
<br />
<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation:<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes. <p> </p><br />
- No product inhibition. <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mechanisms in E.coli. <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maximum yield of ethylene. <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet/SAM)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
Using Tinkercell a graphical representation of 'Model 1' was generated (Figure 3) using deterministic stimulation.<br />
In this figure a graphical representation of the rates - generated internally by Tinkercell's mathematical alogarithms - of ethylene production are shown for each fixed enzyme concentration assumed.<br />
As a result not only is production of ethylene shown to be increasing linearly with time, but at a greater rate with increasing concentration of enzyme (SAM synthetase, ACC synthase, EFE) used.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: A visual construction of Model 2 using ''Tinkercell'', where transcription and translation are taken into account ]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degradation, transcription and translation rates. These values were obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. As a result this model is expected to produce a more accurate representation of the ethylene producing pathway.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-------<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Figure 5: Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Figure 6: Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin the enzymes are produced at a realitively fast rate however as time proceeds they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be depleated. Hence this graph indicates plausible for approximately the first 10 seconds of production. After that the data looks unrealistic and implausible in an E.coli cell.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Figure 7: Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate, however due to the confunding aspects of excess enzyme production, it is unclear if this graph truly represents the total ethylene output. However it can be assumed that the first few seconds accurately portray the ethylene output.<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg|200px|thumb|left|]]<br />
<br />
== SAM --> ACC ==<br />
<br />
[[Image:sam_acc.jpg|200px|thumb|left|]]<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
[[Image:acc_eth.jpg|200px|thumb|left|]]<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T11:40:03Z<p>Ricky: /* Model 2 */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are outlined in the image Figure 1 (left), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our E.coli cells. Where, in addition to the qualitative representation (figure 2 & 4) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
==<H2> Aims of the Kinetic Modelling </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This relatively simple model - as represented in figure 2 - was designed so to be the starting point of our kinetic modelling. This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase - also known as EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
<br />
<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation:<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes. <p> </p><br />
- No product inhibition. <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mechanisms in E.coli. <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maximum yield of ethylene. <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet/SAM)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
Using Tinkercell a graphical representation of 'Model 1' was generated (Figure 3) using deterministic stimulation.<br />
In this figure a graphical representation of the rates - generated internally by Tinkercell's mathematical alogarithms - of ethylene production are shown for each fixed enzyme concentration assumed.<br />
As a result not only is production of ethylene shown to be increasing linearly with time, but at a greater rate with increasing concentration of enzyme (SAM synthetase, ACC synthase, EFE) used.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: A visual construction of Model 2 using ''Tinkercell'', where transcription and translation are taken into account ]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degredation,transcription and translation rates. However these values where obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. Therefore this model is designed to produce a more accurate representation of the ethylene producing biological pathway <br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-------<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Figure 5: Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Figure 6: Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin the enzymes are produced at a realitively fast rate however as time proceeds they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be depleated. Hence this graph indicates plausible for approximately the first 10 seconds of production. After that the data looks unrealistic and implausible in an E.coli cell.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Figure 7: Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate, however due to the confunding aspects of excess enzyme production, it is unclear if this graph truly represents the total ethylene output. However it can be assumed that the first few seconds accurately portray the ethylene output.<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg|200px|thumb|left|]]<br />
<br />
== SAM --> ACC ==<br />
<br />
[[Image:sam_acc.jpg|200px|thumb|left|]]<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
[[Image:acc_eth.jpg|200px|thumb|left|]]<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T11:32:23Z<p>Ricky: /* Model 1 */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are outlined in the image Figure 1 (left), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our E.coli cells. Where, in addition to the qualitative representation (figure 2 & 4) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
==<H2> Aims of the Kinetic Modelling </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This relatively simple model - as represented in figure 2 - was designed so to be the starting point of our kinetic modelling. This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase - also known as EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
<br />
<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation:<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes. <p> </p><br />
- No product inhibition. <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mechanisms in E.coli. <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maximum yield of ethylene. <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet/SAM)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
Using Tinkercell a graphical representation of 'Model 1' was generated (Figure 3) using deterministic stimulation.<br />
In this figure a graphical representation of the rates - generated internally by Tinkercell's mathematical alogarithms - of ethylene production are shown for each fixed enzyme concentration assumed.<br />
As a result not only is production of ethylene shown to be increasing linearly with time, but at a greater rate with increasing concentration of enzyme (SAM synthetase, ACC synthase, EFE) used.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: A visual construction of Model 2 using ''Tinkercell'', where transcription and translation are taken into account ]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degredation,transcription and translation rates. However these values where obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. Therefore this model is designed to produce a more accurate representation of the ethylene producing biological pathway <br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-------<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Figure 5: Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Figure 6: Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin the enzymes are produced at a realitively fast rate however as time proceeds they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be depleated. Hence this graph indicates plausible for approximately the first 10 seconds of production. After that the data looks unrealistic and implausible in an E.coli cell.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Figure 7: Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate however due to the confunding aspects of excess enzyme production then it is unclear if this graph truely represents the total ethylene output. However it can be assumed that the first few seconds accurately portray the ethylene output.<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg|200px|thumb|left|]]<br />
<br />
== SAM --> ACC ==<br />
<br />
[[Image:sam_acc.jpg|200px|thumb|left|]]<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
[[Image:acc_eth.jpg|200px|thumb|left|]]<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T11:12:13Z<p>Ricky: /* Kinetic modelling of the ethylene generator */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are outlined in the image Figure 1 (left), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our E.coli cells. Where, in addition to the qualitative representation (figure 2 & 4) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
==<H2> Aims of the Kinetic Modelling </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This model was designed to be the starting point of the our kinetic modelling. Therefore it was decided to begin with a realitivly simple construct.This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase or EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
<br />
<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes <p> </p><br />
- No production inhibition <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mecahnisms in E.coli <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maxiumum yield of ethylene <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
Using Tinkercell a graphical representation of 'Model 1' was generated (Figure 3) using deterministic stimulation.<br />
in this figure a graphical representation of the rates, generated internally by the program, of ethylene production are shown for each fixed enzyme concentration assumed.<br />
as a result not only is production of ethylene shown to be increasing linearly with time, but at a greater rate with increasing concentration of enzyme (SAM synthetase, ACC synthase, EFE) used,.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: A visual construction of Model 2 using ''Tinkercell'', where transcription and translation are taken into account ]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degredation,transcription and translation rates. However these values where obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. Therefore this model is designed to produce a more accurate representation of the ethylene producing biological pathway <br />
-----<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Figure 5: Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Figure 6: Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin with the enzymes are produced at quite a rate however as time goes on they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be completely depleated. Hence this graph might only be plausible for the first few seconds of production. After that the data looks unreliable<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Figure 7: Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate however due to the confunding aspects of the enzyme production graph then it is unclear if this truely represents the total ethylene output. However it can be assumed that the first few seconds would be good<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg|200px|thumb|left|]]<br />
<br />
== SAM --> ACC ==<br />
<br />
[[Image:sam_acc.jpg|200px|thumb|left|]]<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
[[Image:acc_eth.jpg|200px|thumb|left|]]<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T11:11:43Z<p>Ricky: /* Kinetic modelling of the ethylene generator */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are outlined in the image Figure 1 (left), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our E.coli cells. Where, in addition to the qualitative representation (figure 2) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
==<H2> Aims of the Kinetic Modelling </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This model was designed to be the starting point of the our kinetic modelling. Therefore it was decided to begin with a realitivly simple construct.This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase or EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
<br />
<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes <p> </p><br />
- No production inhibition <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mecahnisms in E.coli <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maxiumum yield of ethylene <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
Using Tinkercell a graphical representation of 'Model 1' was generated (Figure 3) using deterministic stimulation.<br />
in this figure a graphical representation of the rates, generated internally by the program, of ethylene production are shown for each fixed enzyme concentration assumed.<br />
as a result not only is production of ethylene shown to be increasing linearly with time, but at a greater rate with increasing concentration of enzyme (SAM synthetase, ACC synthase, EFE) used,.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
-<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: A visual construction of Model 2 using ''Tinkercell'', where transcription and translation are taken into account ]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degredation,transcription and translation rates. However these values where obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. Therefore this model is designed to produce a more accurate representation of the ethylene producing biological pathway <br />
-----<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Figure 5: Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Figure 6: Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin with the enzymes are produced at quite a rate however as time goes on they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be completely depleated. Hence this graph might only be plausible for the first few seconds of production. After that the data looks unreliable<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Figure 7: Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate however due to the confunding aspects of the enzyme production graph then it is unclear if this truely represents the total ethylene output. However it can be assumed that the first few seconds would be good<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg|200px|thumb|left|]]<br />
<br />
== SAM --> ACC ==<br />
<br />
[[Image:sam_acc.jpg|200px|thumb|left|]]<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
[[Image:acc_eth.jpg|200px|thumb|left|]]<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T11:09:37Z<p>Ricky: /* Introduction */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are outlined in the image Figure 1 (left), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our e.coli cell. Where, in addition to the qualitative representation (figure 1) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained using the model to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
==<H2> Aims of the Kinetic Modelling </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This model was designed to be the starting point of the our kinetic modelling. Therefore it was decided to begin with a realitivly simple construct.This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase or EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation<br />
<br />
<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes <p> </p><br />
- No production inhibition <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mecahnisms in E.coli <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maxiumum yield of ethylene <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
Using Tinkercell a graphical representation of 'Model 1' was generated (Figure 3) using deterministic stimulation.<br />
in this figure a graphical representation of the rates, generated internally by the program, of ethylene production are shown for each fixed enzyme concentration assumed.<br />
as a result not only is production of ethylene shown to be increasing linearly with time, but at a greater rate with increasing concentration of enzyme (SAM synthetase, ACC synthase, EFE) used,.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
hello<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: A visual construction of Model 2 using ''Tinkercell'', where transcription and translation are taken into account ]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degredation,transcription and translation rates. However these values where obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. Therefore this model is designed to produce a more accurate representation of the ethylene producing biological pathway <br />
-----<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Figure 5: Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Figure 6: Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin with the enzymes are produced at quite a rate however as time goes on they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be completely depleated. Hence this graph might only be plausible for the first few seconds of production. After that the data looks unreliable<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Figure 7: Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate however due to the confunding aspects of the enzyme production graph then it is unclear if this truely represents the total ethylene output. However it can be assumed that the first few seconds would be good<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg|200px|thumb|left|]]<br />
<br />
== SAM --> ACC ==<br />
<br />
[[Image:sam_acc.jpg|200px|thumb|left|]]<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
[[Image:acc_eth.jpg|200px|thumb|left|]]<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T10:43:05Z<p>Ricky: /* Introduction */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are outlined in the image (Figure 1), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our e.coli cell. Where, in addition to the qualitative representation (figure 1) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained using the model to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
==<H2> Aims of the Kinetic Modelling </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This model was designed to be the starting point of the our kinetic modelling. Therefore it was decided to begin with a realitivly simple construct.This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase or EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation<br />
<br />
<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes <p> </p><br />
- No production inhibition <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mecahnisms in E.coli <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maxiumum yield of ethylene <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
Using Tinkercell a graphical representation of 'Model 1' was generated (Figure 3) using deterministic stimulation.<br />
in this figure a graphical representation of the rates, generated internally by the program, of ethylene production are shown for each fixed enzyme concentration assumed.<br />
as a result not only is production of ethylene shown to be increasing linearly with time, but at a greater rate with increasing concentration of enzyme (SAM synthetase, ACC synthase, EFE) used,.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
hello<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: A visual construction of Model 2 using ''Tinkercell'', where transcription and translation are taken into account ]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degredation,transcription and translation rates. However these values where obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. Therefore this model is designed to produce a more accurate representation of the ethylene producing biological pathway <br />
-----<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Figure 5: Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Figure 6: Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin with the enzymes are produced at quite a rate however as time goes on they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be completely depleated. Hence this graph might only be plausible for the first few seconds of production. After that the data looks unreliable<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Figure 7: Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate however due to the confunding aspects of the enzyme production graph then it is unclear if this truely represents the total ethylene output. However it can be assumed that the first few seconds would be good<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg|200px|thumb|left|]]<br />
<br />
== SAM --> ACC ==<br />
<br />
[[Image:sam_acc.jpg|200px|thumb|left|]]<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
[[Image:acc_eth.jpg|200px|thumb|left|]]<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T08:30:57Z<p>Ricky: /* Model 1 */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are highlighted in the image (Figure 1), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our e.coli cell. Where, in addition to the qualitative representation (figure 1) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained using the model to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
==<H2> Aims of the Kinetic Modelling </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This model was designed to be the starting point of the our kinetic modelling. Therefore it was decided to begin with a realitivly simple construct.This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase or EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation<br />
<br />
<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes <p> </p><br />
- No production inhibition <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mecahnisms in E.coli <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maxiumum yield of ethylene <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
Using Tinkercell a graphical representation of 'Model 1' was generated (Figure 3) using deterministic stimulation.<br />
in this figure a graphical representation of the rates, generated internally by the program, of ethylene production are shown for each fixed enzyme concentration assumed.<br />
as a result not only is production of ethylene shown to be increasing linearly with time, but at a greater rate with increasing concentration of enzyme (SAM synthetase, ACC synthase, EFE) used,.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
hello<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: A visual construction of Model 2 using ''Tinkercell'', where transcription and translation are taken into account ]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degredation,transcription and translation rates. However these values where obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. Therefore this model is designed to produce a more accurate representation of the ethylene producing biological pathway <br />
-----<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Figure 5: Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Figure 6: Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin with the enzymes are produced at quite a rate however as time goes on they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be completely depleated. Hence this graph might only be plausible for the first few seconds of production. After that the data looks unreliable<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Figure 7: Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate however due to the confunding aspects of the enzyme production graph then it is unclear if this truely represents the total ethylene output. However it can be assumed that the first few seconds would be good<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg|200px|thumb|left|]]<br />
<br />
== SAM --> ACC ==<br />
<br />
[[Image:sam_acc.jpg|200px|thumb|left|]]<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
[[Image:acc_eth.jpg|200px|thumb|left|]]<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T08:30:13Z<p>Ricky: /* Model 1 */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are highlighted in the image (Figure 1), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our e.coli cell. Where, in addition to the qualitative representation (figure 1) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained using the model to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
==<H2> Aims of the Kinetic Modelling </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This model was designed to be the starting point of the our kinetic modelling. Therefore it was decided to begin with a realitivly simple construct.This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase or EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes <p> </p><br />
- No production inhibition <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mecahnisms in E.coli <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maxiumum yield of ethylene <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
Using Tinkercell a graphical representation of 'Model 1' was generated (Figure 3) using deterministic stimulation.<br />
in this figure a graphical representation of the rates, generated internally by the program, of ethylene production are shown for each fixed enzyme concentration assumed.<br />
as a result not only is production of ethylene shown to be increasing linearly with time, but at a greater rate with increasing concentration of enzyme (SAM synthetase, ACC synthase, EFE) used,.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
hello<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: A visual construction of Model 2 using ''Tinkercell'', where transcription and translation are taken into account ]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degredation,transcription and translation rates. However these values where obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. Therefore this model is designed to produce a more accurate representation of the ethylene producing biological pathway <br />
-----<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Figure 5: Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Figure 6: Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin with the enzymes are produced at quite a rate however as time goes on they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be completely depleated. Hence this graph might only be plausible for the first few seconds of production. After that the data looks unreliable<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Figure 7: Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate however due to the confunding aspects of the enzyme production graph then it is unclear if this truely represents the total ethylene output. However it can be assumed that the first few seconds would be good<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg|200px|thumb|left|]]<br />
<br />
== SAM --> ACC ==<br />
<br />
[[Image:sam_acc.jpg|200px|thumb|left|]]<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
[[Image:acc_eth.jpg|200px|thumb|left|]]<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T08:27:56Z<p>Ricky: /* Model 1 */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are highlighted in the image (Figure 1), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our e.coli cell. Where, in addition to the qualitative representation (figure 1) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained using the model to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
==<H2> Aims of the Kinetic Modelling </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This model was designed to be the starting point of the our kinetic modelling. Therefore it was decided to begin with a realitivly simple construct.This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase or EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes <p> </p><br />
- No production inhibition <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mecahnisms in E.coli <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maxiumum yield of ethylene <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
Using Tinkercell a graphical representation of 'Model 1' was generated (Figure 3) using deterministic stimulation.<br />
in this figure a graphical representation of the rates, generated by Tinkercell internally, of ethylene production are shown for each fixed enzyme concentration assumed.<br />
as a result not only is production of ethylene shown to be increasing linearly with time, but at a greater rate with increasing concentration of enzyme (SAM synthetase, ACC synthase, EFE) used,.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
hello<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: A visual construction of Model 2 using ''Tinkercell'', where transcription and translation are taken into account ]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degredation,transcription and translation rates. However these values where obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. Therefore this model is designed to produce a more accurate representation of the ethylene producing biological pathway <br />
-----<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Figure 5: Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Figure 6: Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin with the enzymes are produced at quite a rate however as time goes on they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be completely depleated. Hence this graph might only be plausible for the first few seconds of production. After that the data looks unreliable<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Figure 7: Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate however due to the confunding aspects of the enzyme production graph then it is unclear if this truely represents the total ethylene output. However it can be assumed that the first few seconds would be good<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg|200px|thumb|left|]]<br />
<br />
== SAM --> ACC ==<br />
<br />
[[Image:sam_acc.jpg|200px|thumb|left|]]<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
[[Image:acc_eth.jpg|200px|thumb|left|]]<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T08:13:37Z<p>Ricky: /* Model 2 */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are highlighted in the image (Figure 1), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our e.coli cell. Where, in addition to the qualitative representation (figure 1) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained using the model to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
==<H2> Aims of the Kinetic Modelling </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This model was designed to be the starting point of the our kinetic modelling. Therefore it was decided to begin with a realitivly simple construct.This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase or EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes <p> </p><br />
- No production inhibition <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mecahnisms in E.coli <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maxiumum yield of ethylene <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
An explanation of this Graph<br />
<br />
working <br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
hello<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: A visual construction of Model 2 using ''Tinkercell'', where transcription and translation are taken into account ]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degredation,transcription and translation rates. However these values where obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. Therefore this model is designed to produce a more accurate representation of the ethylene producing biological pathway <br />
-----<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Figure 5: Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Figure 6: Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin with the enzymes are produced at quite a rate however as time goes on they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be completely depleated. Hence this graph might only be plausible for the first few seconds of production. After that the data looks unreliable<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Figure 7: Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate however due to the confunding aspects of the enzyme production graph then it is unclear if this truely represents the total ethylene output. However it can be assumed that the first few seconds would be good<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg|200px|thumb|left|]]<br />
<br />
== SAM --> ACC ==<br />
<br />
[[Image:sam_acc.jpg|200px|thumb|left|]]<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
[[Image:acc_eth.jpg|200px|thumb|left|]]<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T08:11:44Z<p>Ricky: /* Model 2 */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are highlighted in the image (Figure 1), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our e.coli cell. Where, in addition to the qualitative representation (figure 1) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained using the model to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
==<H2> Aims of the Kinetic Modelling </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This model was designed to be the starting point of the our kinetic modelling. Therefore it was decided to begin with a realitivly simple construct.This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase or EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes <p> </p><br />
- No production inhibition <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mecahnisms in E.coli <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maxiumum yield of ethylene <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
An explanation of this Graph<br />
<br />
working <br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
hello<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: A visual construction of Model 2 using ''Tinkercell'', where transcription and translation are taken into account ]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degredation,transcription and translation rates. However these values where obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. Therefore this model is designed to produce a more accurate representation of the ethylene producing biological pathway <br />
-----<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin with the enzymes are produced at quite a rate however as time goes on they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be completely depleated. Hence this graph might only be plausible for the first few seconds of production. After that the data looks unreliable<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate however due to the confunding aspects of the enzyme production graph then it is unclear if this truely represents the total ethylene output. However it can be assumed that the first few seconds would be good<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg|200px|thumb|left|]]<br />
<br />
== SAM --> ACC ==<br />
<br />
[[Image:sam_acc.jpg|200px|thumb|left|]]<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
[[Image:acc_eth.jpg|200px|thumb|left|]]<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T08:09:58Z<p>Ricky: /* Model 2 */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are highlighted in the image (Figure 1), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our e.coli cell. Where, in addition to the qualitative representation (figure 1) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained using the model to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
==<H2> Aims of the Kinetic Modelling </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This model was designed to be the starting point of the our kinetic modelling. Therefore it was decided to begin with a realitivly simple construct.This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase or EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes <p> </p><br />
- No production inhibition <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mecahnisms in E.coli <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maxiumum yield of ethylene <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
An explanation of this Graph<br />
<br />
working <br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
hello<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 4: Model 2]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degredation,transcription and translation rates. However these values where obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. Therefore this model is designed to produce a more accurate representation of the ethylene producing biological pathway <br />
-----<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin with the enzymes are produced at quite a rate however as time goes on they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be completely depleated. Hence this graph might only be plausible for the first few seconds of production. After that the data looks unreliable<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate however due to the confunding aspects of the enzyme production graph then it is unclear if this truely represents the total ethylene output. However it can be assumed that the first few seconds would be good<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg|200px|thumb|left|]]<br />
<br />
== SAM --> ACC ==<br />
<br />
[[Image:sam_acc.jpg|200px|thumb|left|]]<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
[[Image:acc_eth.jpg|200px|thumb|left|]]<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T08:09:32Z<p>Ricky: /* Model 1 */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are highlighted in the image (Figure 1), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our e.coli cell. Where, in addition to the qualitative representation (figure 1) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained using the model to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
==<H2> Aims of the Kinetic Modelling </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This model was designed to be the starting point of the our kinetic modelling. Therefore it was decided to begin with a realitivly simple construct.This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase or EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes <p> </p><br />
- No production inhibition <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mecahnisms in E.coli <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maxiumum yield of ethylene <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 3: Ethylene Production at different steady state concentration]]<br />
<br />
An explanation of this Graph<br />
<br />
working <br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
hello<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 1: Model 2]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degredation,transcription and translation rates. However these values where obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. Therefore this model is designed to produce a more accurate representation of the ethylene producing biological pathway <br />
-----<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin with the enzymes are produced at quite a rate however as time goes on they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be completely depleated. Hence this graph might only be plausible for the first few seconds of production. After that the data looks unreliable<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate however due to the confunding aspects of the enzyme production graph then it is unclear if this truely represents the total ethylene output. However it can be assumed that the first few seconds would be good<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg|200px|thumb|left|]]<br />
<br />
== SAM --> ACC ==<br />
<br />
[[Image:sam_acc.jpg|200px|thumb|left|]]<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
[[Image:acc_eth.jpg|200px|thumb|left|]]<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T08:08:49Z<p>Ricky: /* Model 1 */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are highlighted in the image (Figure 1), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our e.coli cell. Where, in addition to the qualitative representation (figure 1) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained using the model to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
==<H2> Aims of the Kinetic Modelling </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 2: A visual construction of Model 1 using ''Tinkercell'']]<br />
This model was designed to be the starting point of the our kinetic modelling. Therefore it was decided to begin with a realitivly simple construct.This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase or EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes <p> </p><br />
- No production inhibition <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mecahnisms in E.coli <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maxiumum yield of ethylene <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 2: Ethylene Production at different steady state concentration]]<br />
<br />
An explanation of this Graph<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
hello<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 1: Model 2]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degredation,transcription and translation rates. However these values where obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. Therefore this model is designed to produce a more accurate representation of the ethylene producing biological pathway <br />
-----<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin with the enzymes are produced at quite a rate however as time goes on they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be completely depleated. Hence this graph might only be plausible for the first few seconds of production. After that the data looks unreliable<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate however due to the confunding aspects of the enzyme production graph then it is unclear if this truely represents the total ethylene output. However it can be assumed that the first few seconds would be good<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg|200px|thumb|left|]]<br />
<br />
== SAM --> ACC ==<br />
<br />
[[Image:sam_acc.jpg|200px|thumb|left|]]<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
[[Image:acc_eth.jpg|200px|thumb|left|]]<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T08:08:17Z<p>Ricky: /* Introduction */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model ''in silico''.]]<br />
<br />
The three key enzymes we require are highlighted in the image (Figure 1), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our e.coli cell. Where, in addition to the qualitative representation (figure 1) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained using the model to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
==<H2> Aims of the Kinetic Modelling </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 1: A visual construction of Model 1 using ''Tinkercell'']]<br />
This model was designed to be the starting point of the our kinetic modelling. Therefore it was decided to begin with a realitivly simple construct.This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase or EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes <p> </p><br />
- No production inhibition <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mecahnisms in E.coli <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maxiumum yield of ethylene <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 2: Ethylene Production at different steady state concentration]]<br />
<br />
An explanation of this Graph<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
hello<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 1: Model 2]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degredation,transcription and translation rates. However these values where obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. Therefore this model is designed to produce a more accurate representation of the ethylene producing biological pathway <br />
-----<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin with the enzymes are produced at quite a rate however as time goes on they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be completely depleated. Hence this graph might only be plausible for the first few seconds of production. After that the data looks unreliable<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate however due to the confunding aspects of the enzyme production graph then it is unclear if this truely represents the total ethylene output. However it can be assumed that the first few seconds would be good<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg|200px|thumb|left|]]<br />
<br />
== SAM --> ACC ==<br />
<br />
[[Image:sam_acc.jpg|200px|thumb|left|]]<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
[[Image:acc_eth.jpg|200px|thumb|left|]]<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T08:07:47Z<p>Ricky: /* Introduction */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - the reaction we wish to model in silico.]]<br />
<br />
The three key enzymes we require are highlighted in the image (Figure 1), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our e.coli cell. Where, in addition to the qualitative representation (figure 1) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained using the model to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
==<H2> Aims of the Kinetic Modelling </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 1: A visual construction of Model 1 using ''Tinkercell'']]<br />
This model was designed to be the starting point of the our kinetic modelling. Therefore it was decided to begin with a realitivly simple construct.This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase or EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes <p> </p><br />
- No production inhibition <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mecahnisms in E.coli <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maxiumum yield of ethylene <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 2: Ethylene Production at different steady state concentration]]<br />
<br />
An explanation of this Graph<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
hello<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 1: Model 2]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degredation,transcription and translation rates. However these values where obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. Therefore this model is designed to produce a more accurate representation of the ethylene producing biological pathway <br />
-----<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin with the enzymes are produced at quite a rate however as time goes on they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be completely depleated. Hence this graph might only be plausible for the first few seconds of production. After that the data looks unreliable<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate however due to the confunding aspects of the enzyme production graph then it is unclear if this truely represents the total ethylene output. However it can be assumed that the first few seconds would be good<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg|200px|thumb|left|]]<br />
<br />
== SAM --> ACC ==<br />
<br />
[[Image:sam_acc.jpg|200px|thumb|left|]]<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
[[Image:acc_eth.jpg|200px|thumb|left|]]<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T08:07:07Z<p>Ricky: /* Introduction */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Figure 1: Yang cycle. In red - our reaction of interest.]]<br />
<br />
The three key enzymes we require are highlighted in the image (Figure 1), <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our e.coli cell. Where, in addition to the qualitative representation (figure 1) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained using the model to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
==<H2> Aims of the Kinetic Modelling </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 1: A visual construction of Model 1 using ''Tinkercell'']]<br />
This model was designed to be the starting point of the our kinetic modelling. Therefore it was decided to begin with a realitivly simple construct.This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase or EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes <p> </p><br />
- No production inhibition <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mecahnisms in E.coli <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maxiumum yield of ethylene <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 2: Ethylene Production at different steady state concentration]]<br />
<br />
An explanation of this Graph<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
hello<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 1: Model 2]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degredation,transcription and translation rates. However these values where obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. Therefore this model is designed to produce a more accurate representation of the ethylene producing biological pathway <br />
-----<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin with the enzymes are produced at quite a rate however as time goes on they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be completely depleated. Hence this graph might only be plausible for the first few seconds of production. After that the data looks unreliable<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate however due to the confunding aspects of the enzyme production graph then it is unclear if this truely represents the total ethylene output. However it can be assumed that the first few seconds would be good<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg|200px|thumb|left|]]<br />
<br />
== SAM --> ACC ==<br />
<br />
[[Image:sam_acc.jpg|200px|thumb|left|]]<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
[[Image:acc_eth.jpg|200px|thumb|left|]]<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T08:05:29Z<p>Ricky: /* Model 1 */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Yang cycle]]<br />
<br />
The three key enzymes we require are highlighted in the image, <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our e.coli cell. Where, in addition to the qualitative representation (figure 1) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained using the model to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
==<H2> Aims of the Kinetic Modelling </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 1: A visual construction of Model 1 using ''Tinkercell'']]<br />
This model was designed to be the starting point of the our kinetic modelling. Therefore it was decided to begin with a realitivly simple construct.This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase or EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes <p> </p><br />
- No production inhibition <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mecahnisms in E.coli <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maxiumum yield of ethylene <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 2: Ethylene Production at different steady state concentration]]<br />
<br />
An explanation of this Graph<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
hello<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 1: Model 2]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degredation,transcription and translation rates. However these values where obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. Therefore this model is designed to produce a more accurate representation of the ethylene producing biological pathway <br />
-----<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin with the enzymes are produced at quite a rate however as time goes on they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be completely depleated. Hence this graph might only be plausible for the first few seconds of production. After that the data looks unreliable<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate however due to the confunding aspects of the enzyme production graph then it is unclear if this truely represents the total ethylene output. However it can be assumed that the first few seconds would be good<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg|200px|thumb|left|]]<br />
<br />
== SAM --> ACC ==<br />
<br />
[[Image:sam_acc.jpg|200px|thumb|left|]]<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
[[Image:acc_eth.jpg|200px|thumb|left|]]<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T08:05:13Z<p>Ricky: /* Model 1 */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Yang cycle]]<br />
<br />
The three key enzymes we require are highlighted in the image, <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our e.coli cell. Where, in addition to the qualitative representation (figure 1) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained using the model to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
==<H2> Aims of the Kinetic Modelling </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Figure 1: A visual construction of Model 1 using ''Tinkcell'']]<br />
This model was designed to be the starting point of the our kinetic modelling. Therefore it was decided to begin with a realitivly simple construct.This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase or EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes <p> </p><br />
- No production inhibition <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mecahnisms in E.coli <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maxiumum yield of ethylene <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|Figure 2: Ethylene Production at different steady state concentration]]<br />
<br />
An explanation of this Graph<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
hello<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 1: Model 2]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degredation,transcription and translation rates. However these values where obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. Therefore this model is designed to produce a more accurate representation of the ethylene producing biological pathway <br />
-----<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Enzymegraph.png|300px|thumb|left|Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin with the enzymes are produced at quite a rate however as time goes on they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be completely depleated. Hence this graph might only be plausible for the first few seconds of production. After that the data looks unreliable<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[Image:Ethylenegraph.jpg|300px|thumb|left|Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate however due to the confunding aspects of the enzyme production graph then it is unclear if this truely represents the total ethylene output. However it can be assumed that the first few seconds would be good<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg|200px|thumb|left|]]<br />
<br />
== SAM --> ACC ==<br />
<br />
[[Image:sam_acc.jpg|200px|thumb|left|]]<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
[[Image:acc_eth.jpg|200px|thumb|left|]]<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-27T07:57:11Z<p>Ricky: /* Model 1 */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Yang cycle]]<br />
<br />
The three key enzymes we require are highlighted in the image, <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Kinetic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our e.coli cell. Where, in addition to the qualitative representation (figure 1) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained using the model to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
==<H2> Aims of the Kinetic Modelling </H2>==<br />
<br />
-To determine the maximum output of ethylene <p> </p><br />
-To estimate HCN levels and assess potential damage to cell<p> </p><br />
-Start with a simple model and progress to a more complex one. This allows us to more accurately predict ethylene production <p> </p><br />
<br />
== <H2>Model 1</H2> ==<br />
<H4> Introduction to Model 1 </H4><br />
<br />
[[Image:Cell12.jpg|300px|thumb|left|Model 1]]<br />
This model was designed to be the starting point of the our kinetic modelling. Therefore it was decided to begin with a realitivly simple construct.This uncomplicated system could then be used to obtain an ethylene output. This design comprised only of three enzymes and lacked the transcription and translation stages. The enzymes invloved were SAM synthetase, ACC synthase and ACC oxidase or EFE (ethylene forming enzyme). These enzymes were present at fixed concentrations (10, 100, 1000 and 3000 uM) and in a 1:1:1 ratio. This ensured that the model was plain yet ultimately obtained a resonable yet simple ethylene output. Therefore with fixed enzyme concentrations the rate at which ethylene is produced depends on the Vmax of the specific enzymes.<br />
<br />
This specific rate is defined by the Michaelis-Menton Equation<br />
<br />
<br />
kcat*[substrate]*[enzyme]<br />
Rate = -------------------------<br />
km + [substrate]<br />
<br />
----<br />
<H4>Assumptions for Model 1</H4><br />
<br />
- A 1:1:1 steady state of enzymes <p> </p><br />
- No production inhibition <p> </p><br />
-Substrates (ATP, L-Methionine etc) kept at a constant value due to homeostatic mecahnisms in E.coli <p> </p><br />
- The best value (km, kcat etc) was always chosen to ensure maxiumum yield of ethylene <p> </p><br />
<br />
----<br />
<H4>Parameters and Values used</H4><br />
<br />
<b>Met concentration </b> - 150 uM<p> </p><br />
<br />
<b>ATP concentration </b>- 9600 uM <p> </p><br />
<br />
<b>O2 concentration </b>- 442 uM<p> </p><br />
<br />
<b>SAMsynth Km(Met)</b>- 92 uM<p> </p><br />
<br />
<b>ACCsynth Km(AdoMet)</b>- 37 uM <p> </p><br />
<br />
<b>EFE Km(ACC)</b>- 12 uM <p> </p><br />
<br />
<b>SAMsynth turnover rate (kcat)</b>- 1.5 per second<p> </p><br />
<br />
<b>ACCsynth turnover rate (kcat)</b>- 18 per second<p> </p><br />
<br />
<b>EFE turnover rate (kcat) </b>- 5.9 per second<p> </p><br />
<br />
<br />
-----<br />
<H4> Graphs and results</H4><br />
[[Image:graph1.jpg|300px|thumb|left|EThylene Production at different steady state concentration]]<br />
<br />
An explanation of this Graph<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
hello<br />
<br />
== <H2>Model 2</H2>==<br />
<br />
[[Image:Monash_modeling.png|300px|thumb|left|Figure 1: Model 2]]<br />
<br />
Model 2 explores the more complicated version of our kinetic modelling. It aims to incorporate the transcription and translation rates of the cell into the final design. This calculation begins to become more difficult due to problomatic degredation,transcription and translation rates. However these values where obtained from the [http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012 Elowitz repressilator model]. Of the many sources browsed it was determined that this source seemed to be the most accurate and hence its values were used. Therefore the transcription and translation rates of mRNA and proteins constantly changes the final concentration of the three enzymes. This changing concentration of enzymes then produces ethylene at a more reliable rate. Therefore this model is designed to produce a more accurate representation of the ethylene producing biological pathway <br />
-----<br />
<H3> Elowitz Degradation Values </H3><br />
<br />
<b>mRNA degradation rate</b> - 0.00577 mRNA per second <p></p><br />
<br />
<b>Enzyme degradation rate </b> - 0.001155 enzymes per second<p></p><br />
<b>R0011 PoPs or transcription rate</b> - 0.5 mRNA per second<p></p><br />
<b>Translation rate</b> -0.117 enzymes per second <p></p><br />
<br />
-----<br />
<H3> Graphs and results </H3><br />
[[Image:Mrnagraph.png|300px|thumb|left|Production of mRNA in the cell]] The rate at which mRNA is produced can be seen to start off at a rapid rate. However as time goes on the mRNA can be seen to reach a steady state.<p></p><br />
<br />
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<br />
[[Image:Enzymegraph.png|300px|thumb|left|Production rate of the three different enzymes]] Due to the polycystronic translation of the mRNA all the three enzymes are produced in a 1:1:1 ratio. To begin with the enzymes are produced at quite a rate however as time goes on they fail to reach a steady state. In E.coli it is known that all enzymes eventually reach a steady state otherwise energy and resources would be completely depleated. Hence this graph might only be plausible for the first few seconds of production. After that the data looks unreliable<br />
<br />
<br />
<br />
<br />
<br />
<br />
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[[Image:Ethylenegraph.jpg|300px|thumb|left|Ethylene production at floating enzyme concentration]]<p></p><br />
The production of ethylene in this graph looks realitively accurate however due to the confunding aspects of the enzyme production graph then it is unclear if this truely represents the total ethylene output. However it can be assumed that the first few seconds would be good<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg|200px|thumb|left|]]<br />
<br />
== SAM --> ACC ==<br />
<br />
[[Image:sam_acc.jpg|200px|thumb|left|]]<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
[[Image:acc_eth.jpg|200px|thumb|left|]]<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-25T04:51:35Z<p>Ricky: /* ACC --> Ethylene */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Yang cycle]]<br />
<br />
The three key enzymes we require are highlighted in the image, <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Metabolic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our e.coli cell. Where, in addition to the qualitative representation (figure 1) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained using the model to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
[[Image:Monash_modeling.png|200px|thumb|left|Figure 1: network diagram of the desired reactions in an '<i>E. coli</i>' cell needed for ethylene production - created on Tinkercell]]<br />
<br />
== Assumptions ==<br />
<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg|200px|thumb|left|]]<br />
<br />
== SAM --> ACC ==<br />
<br />
[[Image:sam_acc.jpg|200px|thumb|left|]]<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
[[Image:acc_eth.jpg|200px|thumb|left|]]<br />
<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/File:Acc_eth.jpgFile:Acc eth.jpg2010-10-25T04:51:06Z<p>Ricky: </p>
<hr />
<div></div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-25T04:47:43Z<p>Ricky: /* SAM --> ACC */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Yang cycle]]<br />
<br />
The three key enzymes we require are highlighted in the image, <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Metabolic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our e.coli cell. Where, in addition to the qualitative representation (figure 1) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained using the model to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
[[Image:Monash_modeling.png|200px|thumb|left|Figure 1: network diagram of the desired reactions in an '<i>E. coli</i>' cell needed for ethylene production - created on Tinkercell]]<br />
<br />
== Assumptions ==<br />
<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg|200px|thumb|left|]]<br />
<br />
== SAM --> ACC ==<br />
<br />
[[Image:sam_acc.jpg|200px|thumb|left|]]<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-25T04:47:24Z<p>Ricky: /* SAM --> ACC */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Yang cycle]]<br />
<br />
The three key enzymes we require are highlighted in the image, <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Metabolic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our e.coli cell. Where, in addition to the qualitative representation (figure 1) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained using the model to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
[[Image:Monash_modeling.png|200px|thumb|left|Figure 1: network diagram of the desired reactions in an '<i>E. coli</i>' cell needed for ethylene production - created on Tinkercell]]<br />
<br />
== Assumptions ==<br />
<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg|200px|thumb|left|]]<br />
<br />
== SAM --> ACC ==<br />
<br />
[[Image:sam_acc.jpg]]<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/File:Sam_acc.jpgFile:Sam acc.jpg2010-10-25T04:46:56Z<p>Ricky: </p>
<hr />
<div></div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-25T04:43:46Z<p>Ricky: /* Met --> SAM (AdoMet) */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Yang cycle]]<br />
<br />
The three key enzymes we require are highlighted in the image, <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Metabolic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our e.coli cell. Where, in addition to the qualitative representation (figure 1) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained using the model to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
[[Image:Monash_modeling.png|200px|thumb|left|Figure 1: network diagram of the desired reactions in an '<i>E. coli</i>' cell needed for ethylene production - created on Tinkercell]]<br />
<br />
== Assumptions ==<br />
<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg|200px|thumb|left|]]<br />
<br />
== SAM --> ACC ==<br />
<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-25T04:42:56Z<p>Ricky: /* Met --> SAM (AdoMet) */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Yang cycle]]<br />
<br />
The three key enzymes we require are highlighted in the image, <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Metabolic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our e.coli cell. Where, in addition to the qualitative representation (figure 1) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained using the model to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
[[Image:Monash_modeling.png|200px|thumb|left|Figure 1: network diagram of the desired reactions in an '<i>E. coli</i>' cell needed for ethylene production - created on Tinkercell]]<br />
<br />
== Assumptions ==<br />
<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
[[Image:met_sam.jpg]]<br />
<br />
== SAM --> ACC ==<br />
<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/File:Met_sam.jpgFile:Met sam.jpg2010-10-25T03:27:03Z<p>Ricky: </p>
<hr />
<div></div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-24T17:37:52Z<p>Ricky: /* Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Yang cycle]]<br />
<br />
The three key enzymes we require are highlighted in the image, <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Metabolic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our e.coli cell. Where, in addition to the qualitative representation (figure 1) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained using the model to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
[[Image:Monash_modeling.png|200px|thumb|left|Figure 1: network diagram of the desired reactions in an '<i>E. coli</i>' cell needed for ethylene production - created on Tinkercell]]<br />
<br />
== Assumptions ==<br />
<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
[[Image:tran.jpg|200px|thumb|left|]]<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
<br />
== SAM --> ACC ==<br />
<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/File:Tran.jpgFile:Tran.jpg2010-10-24T17:33:02Z<p>Ricky: </p>
<hr />
<div></div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-24T17:05:07Z<p>Ricky: /* Metabolic modelling of the ethylene generator */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Yang cycle]]<br />
<br />
The three key enzymes we require are highlighted in the image, <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Metabolic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our e.coli cell. Where, in addition to the qualitative representation (figure 1) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained using the model to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies of the 'ethylene generator'.<br />
<br />
[[Image:Monash_modeling.png|200px|thumb|left|Figure 1: network diagram of the desired reactions in an '<i>E. coli</i>' cell needed for ethylene production - created on Tinkercell]]<br />
<br />
== Assumptions ==<br />
<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
<br />
<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
<br />
== SAM --> ACC ==<br />
<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-24T17:03:42Z<p>Ricky: </p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Yang cycle]]<br />
<br />
The three key enzymes we require are highlighted in the image, <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Metabolic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our e.coli cell. Where, in addition to the qualitative representation (figure 1) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. In addition to experimental data, with a model, theoretical data can be obtained using the model to extrapolate quantitative data corresponding to mRNA, protein and ethylene outputs under set parameters and assumptions in silico. As a result, using the given theoretical model outputs as a benchmark, manipulations to the parameters and assumptions set can be made in vitro/in vivo in order to achieve higher outputs in future studies.<br />
<br />
[[Image:Monash_modeling.png|200px|thumb|left|Figure 1: network diagram of the desired reactions in an '<i>E. coli</i>' cell needed for ethylene production - created on Tinkercell]]<br />
<br />
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<br />
<br />
== Assumptions ==<br />
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== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
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== Met --> SAM (AdoMet) ==<br />
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== SAM --> ACC ==<br />
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== ACC --> Ethylene ==<br />
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More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-24T16:07:16Z<p>Ricky: /* Metabolic modelling of the ethylene generator */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Yang cycle]]<br />
<br />
The three key enzymes we require are highlighted in the image, <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Metabolic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our e.coli cell. Where, in addition to the qualitative representation (figure 1) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. As a result, with a model, under certain parameters and assumptions made, quantitative data representing the theoretical: protein, mRNA and most importantly, ethylene outputs could be obtained for further analysis/optimization with respect to experimental data obtained.<br />
<br />
[[Image:Monash_modeling.png|200px|thumb|left|Figure 1: network diagram of the desired reactions in an '<i>E. coli</i>' cell needed for ethylene production - created on Tinkercell]]<br />
<br />
<br />
<br />
<br />
== Assumptions ==<br />
<br />
<br />
== Construct --> mRNA --> enzymes (SAMsynth, ACCS, EFE) ==<br />
<br />
<br />
<br />
== Met --> SAM (AdoMet) ==<br />
<br />
<br />
<br />
== SAM --> ACC ==<br />
<br />
<br />
== ACC --> Ethylene ==<br />
<br />
<br />
<br />
<br />
More to come soon!</div>Rickyhttp://2010.igem.org/Team:Monash_Australia/ModellingTeam:Monash Australia/Modelling2010-10-24T10:46:55Z<p>Ricky: /* Metabolic modelling of the ethylene generator */</p>
<hr />
<div>{{Template:Team:Monash_Australia/Nav2}}<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Image:Monash_Australia_Yang-cycle.png|200px|thumb|left|Yang cycle]]<br />
<br />
The three key enzymes we require are highlighted in the image, <b>SAM synthase</b>, <b>ACC synthase</b> and <b>ACC oxidase</b>. SAM synthase converts methionine into S-Adenosyl-L-Methionine (SAM), using ATP for an adensoyl group. The second step involves ACC synthase, which cleaves the amino butyrate from SAM, releasing 1-aminocyclopropane-1-carboxylic acid (ACC). Released ACC is then processed by ACC Oxidase which converts ACC to ethylene by cleaving the carboxylic acid off as carbon dioxide and its neighboring carbon with the amino group as cyanide gas. By using such a system to produce ethyene gas we can potentially reduce costs involved with current production methods by reducing temperature requirements by 30 fold.<br />
<br />
== Metabolic modelling of the ethylene generator ==<br />
<br />
<br />
The program tinkercell was used to model the synthesis of ethylene in our e.coli cell. Where, in addition to the qualitative representation (figure 1) the program allowed, simulation of the model could be exercised inorder to measure the flux of metabolites – with respect to time - in the system given the input of values pertaining to substrate/enzyme concentration, and reaction rates were inputted. As a result, with a model, under certain parameters and assumptions made, quantitative data representing the theoretical: protein, mRNA and most importantly, ethylene outputs could be obtained for further analysis/optimization with respect to experimental data obtained.<br />
<br />
[[Image:Monash_modeling.png|200px|thumb|left|Figure 1: network diagram of the desired reactions in an '<i>E. coli</i>' cell needed for ethylene production - created on Tinkercell]]<br />
<br />
<br />
More to come soon!</div>Ricky