Team:Edinburgh/Modelling/Bacterial

From 2010.igem.org

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<p>The red light production pathway consists of a red luciferase production gene coupled to the <i>lacI</i> promoter (<a href="http://partsregistry.org/Part:BBa_R0010">BBa_R0010</a>); the presence of LacI in the system inhibits the production of red luciferase. The assumption is then made that red luciferase translates directly into red light - essentially, that substrates such as luciferin are constitutively expressed, and there's no need to include it within the model.</p>
<p>The red light production pathway consists of a red luciferase production gene coupled to the <i>lacI</i> promoter (<a href="http://partsregistry.org/Part:BBa_R0010">BBa_R0010</a>); the presence of LacI in the system inhibits the production of red luciferase. The assumption is then made that red luciferase translates directly into red light - essentially, that substrates such as luciferin are constitutively expressed, and there's no need to include it within the model.</p>
<p>The red light sensor pathway is a signal transduction pathway involving Cph8 (which can have either 'on' or 'off' state and can bind to OmpR) and OmpR (which can be either phosphorylated or unphosphorylated and can bind to either Cph8 or one of the <a href="http://partsregistry.org/Part:BBa_R0082"><i>ompC</i></a> and <a href="http://partsregistry.org/Part:BBa_R0084"><i>ompF</i></a> promoters). The assumption is made that there is a relatively static amount of Cph8 and OmpR within the system, and that there is no need to model their creation via transcription and translation or their degradation. Assumptions are also made regarding the balance between the concentration of on / off Cph8 and phosphorylated / unphosphorylated OmpR when the system is stable, as well as the fact that OmpR can only change phosphorylation state when not bound to any of Cph8, <i>ompC</i>, or <i>ompF</i>.<p>
<p>The red light sensor pathway is a signal transduction pathway involving Cph8 (which can have either 'on' or 'off' state and can bind to OmpR) and OmpR (which can be either phosphorylated or unphosphorylated and can bind to either Cph8 or one of the <a href="http://partsregistry.org/Part:BBa_R0082"><i>ompC</i></a> and <a href="http://partsregistry.org/Part:BBa_R0084"><i>ompF</i></a> promoters). The assumption is made that there is a relatively static amount of Cph8 and OmpR within the system, and that there is no need to model their creation via transcription and translation or their degradation. Assumptions are also made regarding the balance between the concentration of on / off Cph8 and phosphorylated / unphosphorylated OmpR when the system is stable, as well as the fact that OmpR can only change phosphorylation state when not bound to any of Cph8, <i>ompC</i>, or <i>ompF</i>.<p>
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<center><br><br><p><img src="https://static.igem.org/mediawiki/2010/8/8a/Ed10-RedLightSensorPathway.png" width="600px"></p><br>
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<p><b>Figure 2:</b> A simplified diagram of the red light sensor, showing the mechanism of action of red light on the Cph8-OmpR pathway.</p><br><br></center>
<p>When red light is not present in the system, an equilibrium exists between 'on' and 'off' Cph8 (heavily biased towards the 'on' state) and phosphorylated and unphosphorylated OmpR (heavily biased towards the phosphorylated state). When the red light sensing pathway is activated by bursts of photons at the correct wavelength, the Cph8 sensor is almost all turned 'off', which leads to OmpR almost fully unphosphorylated.</p>
<p>When red light is not present in the system, an equilibrium exists between 'on' and 'off' Cph8 (heavily biased towards the 'on' state) and phosphorylated and unphosphorylated OmpR (heavily biased towards the phosphorylated state). When the red light sensing pathway is activated by bursts of photons at the correct wavelength, the Cph8 sensor is almost all turned 'off', which leads to OmpR almost fully unphosphorylated.</p>
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<p>The <a href="http://partsregistry.org/Part:BBa_R0082"><i>ompC</i></a> promoter is activated by phosphorylated OmpR in sufficient quantity, and is coupled to the BioBricked protein coding sequence <a href="http://partsregistry.org/Part:BBa_C0040"><i>BBa_C0040</i></a> that produces TetR. The <a href="http://partsregistry.org/Part:BBa_R0084"><i>ompF</i></a> promoter is activated by minimal amounts of phosphorylated OmpR (and is inhibited by its presence in large quantities); when active, it stimulates production of LacI. TetR is also inhibited by presence of LacI in the system, as per standard repressilator. Assumptions regarding mechanism of action of OmpF and OmpC promoters (cumulative, individual)
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<p>The <a href="http://partsregistry.org/Part:BBa_R0082"><i>ompC</i></a> promoter is activated by phosphorylated OmpR in sufficient quantity, and is coupled to the BioBricked protein coding sequence <a href="http://partsregistry.org/Part:BBa_C0040">BBa_C0040</a> that produces TetR. The <a href="http://partsregistry.org/Part:BBa_R0084"><i>ompF</i></a> promoter is activated by minimal amounts of phosphorylated OmpR (and is inhibited by its presence in large quantities); when active, it stimulates production of <a href="http://partsregistry.org/Part:BBa_C0012">LacI></a>. The presence of increased amounts of LacI in the system will act to inhibit the <a href="http://partsregistry.org/Part:BBa_R0010"><i>lacI</i></a> promoter, which also controls expression of TetR as per the standard repressilator. Minor assumptions are made regarding the kinetic rates related to the <i>ompF</i> and <i>ompC</i> promoters within the model.</p>
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Thus, in standard conditions, large amounts of TetR are produced, while production of LacI is inhibited, due to presence of 'phosphorylated' OmpR. When red light activates the signal transduction pathway, concentration of unphosphorylated OmpR increases, which allows greater amounts of LacI to be produced to inhibit the production of TetR.
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Rates are balanced against one another and against those of the core repressilator to produce clean behaviour.
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<p>Thus, in standard conditions involving the isolated pathway, large amounts of TetR are produced due to the action of the phosphorylated OmpR promoter, whilst similarly the production of LacI is inhibited. When red light activates the signal transduction pathway, however, the concentration of unphosphorylated OmpR increases, which allows greater amounts of LacI to be produced, which in turn inhibits the production of TetR.</p>
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<p><b>Figure 2:</b> Results of simulating the red light sensing pathway as described above. Time units are arbitrary.</p><br><br></center> -->
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<p><b>Figure 3:</b> The same pathway without introducing a burst of red light into the system. Time units are arbitrary.</p><br><br></center> -->
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The graph in Figure 2 shows the results of simulating the red light sensing pathway, after inducing a short period of red light expression at t=100. This red light then stimulates an increased level of LacI within the system (in comparison to the control simulation in Figure 3), which acts to repress the amount of TetR present.
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<center><br><br><p><img src="https://static.igem.org/mediawiki/2010/e/ec/Ed10-RedLightResults.png"></p><br>
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<p><b>Figure 3:</b> Results of simulating the red light sensing pathway as described above. Time units are arbitrary.</p><br><br></center>
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<center><br><br><p><img src="https://static.igem.org/mediawiki/2010/7/7a/Ed10-RedLightWildtype.png"></p><br>
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<p><b>Figure 4:</b> The same pathway without introducing a burst of red light into the system. Time units are arbitrary.</p><br><br></center>
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<p>The graph in Figure 2 shows the results of simulating the red light sensing pathway, after inducing a short period of red light expression at t=100. This red light then stimulates an increased level of LacI within the system (in comparison to the control simulation in Figure 3), which acts to repress the amount of TetR present. The time units in the simulation are arbitrary but controlled by the kinetic rates used, which means that with further characterisation data, it would be possible to optimise the response of the pathway to actual conditions.</p>
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Use of arbitrary rates for creation of red light, etc., how to balance them off against one another such that the desired interactions occur at the desired frequency. Arbitrary time units a result of this, without accurate parameters to tie things to. Given characterisation data, would begin to be able to adjust rates to, for example, response times.
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<p>Use of arbitrary rates for creation of red light, etc., how to balance them off against one another such that the desired interactions occur at the desired frequency. Arbitrary time units a result of this, without accurate parameters to tie things to. Given characterisation data, would begin to be able to adjust rates to, for example, response times.</p>
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Fine-tuning of response was an integral part of the model-building process. Especially difficult since both red and green pathways are two-stage pathways (as modelled) and hence the response to the stimulus is more complex. Furthermore, red pathway involves a number of complex interactions that are not necessarily obvious on paper, as well as potentially involving two different genetic interactions with hopefully the same effect. Analysis of which one is more powerful, whether or not both of them are required, etc.
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<p>Fine-tuning of response was an integral part of the model-building process. Especially difficult since both red and green pathways are two-stage pathways (as modelled) and hence the response to the stimulus is more complex. Furthermore, red pathway involves a number of complex interactions that are not necessarily obvious on paper, as well as potentially involving two different genetic interactions with hopefully the same effect. Analysis of which one is more powerful, whether or not both of them are required, etc.</p>
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Assumptions and justifications thereof. Not enough is fully understood and clearly documented of the action of these systems, and individual interpretations cloud the issue even further.
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<p>Assumptions and justifications thereof. Not enough is fully understood and clearly documented of the action of these systems, and individual interpretations cloud the issue even further.</p>
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Debugging - the number of different proteins involved makes it difficult to predict what effect one stray bug might have on system as a whole. Identified issue in which transcription factors deactivate whilst bound to gene and cannot be unbound, thus permanently preventing the gene from transcribing - highlights need to be careful when specifying rules.
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<p>Debugging - the number of different proteins involved makes it difficult to predict what effect one stray bug might have on system as a whole. Identified issue in which transcription factors deactivate whilst bound to gene and cannot be unbound, thus permanently preventing the gene from transcribing - highlights need to be careful when specifying rules.</p>
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<p><b>Figure 8:</b> Modelled emission results for the light production pathways coupled to the repressilator. The assumption is made that any required substrates are constitutively expressed and readily available, and that the cell experiences no ill effects by diverting resources and pathways to light sensing and production. Time units are arbitrary.</p><br><br></center>
<p><b>Figure 8:</b> Modelled emission results for the light production pathways coupled to the repressilator. The assumption is made that any required substrates are constitutively expressed and readily available, and that the cell experiences no ill effects by diverting resources and pathways to light sensing and production. Time units are arbitrary.</p><br><br></center>
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As expected, linking the light production pathways as described in the previous sections to the core repressilator produced clean oscillations of different colours. The assumptions made during the modelling process mean that it is highly unlikely that this can be expected <i>in vivo</i>, even given the usual difficulties in translating an <i>in silico</i> model to an <i>E. coli</i> host. For example, ensuring that all substrates required for the production of light (i.e. luxCDE, lumP, etc.) are constitutively available at all times may prove to be difficult in a cellular environment without disrupting the natural processes. On the other hand, it is useful verification of the hope that the core system will perform correctly, and that the proposed design will be able to produce a visible and easily discernible output.
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<p>As expected, linking the light production pathways as described in the previous sections to the core repressilator produced clean oscillations of different colours. The assumptions made during the modelling process mean that it is highly unlikely that this can be expected <i>in vivo</i>, even given the usual difficulties in translating an <i>in silico</i> model to an <i>E. coli</i> host. For example, ensuring that all substrates required for the production of light (i.e. luxCDE, lumP, etc.) are constitutively available at all times may prove to be difficult in a cellular environment without disrupting the natural processes. On the other hand, it is useful verification of the hope that the core system will perform correctly, and that the proposed design will be able to produce a visible and easily discernible output.</p>
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The complexity of the model becomes more apparent when applying perturbation analysis to determine the model's response to light sensed from another organism, i.e. activation of the light sensing pathways by applying short bursts of external light, and observation of the reaction.
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<p>The complexity of the model becomes more apparent when applying perturbation analysis to determine the model's response to light sensed from another organism, i.e. activation of the light sensing pathways by applying short bursts of external light, and observation of the reaction.</p>
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Revision as of 09:54, 14 September 2010







Overview: Modelling bacterial BRIDGEs


The second Kappa model created for the project attempted to realise the original vision we held for the system: a composite device based on the tried and tested Elowitz repressilator, combined with three different light-producing and light-sensing pathways. The primary objective of the modelling would then be to confirm that the three systems interacted with one another in roughly the manner we expect, without undue interference or trouble. We would also try to use the model to analyse the structure of the system and possibly to compare different proposed subsystems against one another, to analyse which one would work better.

The following sections describe, in turn: the repressilator model that forms the core of the system, the red light production and signal transduction pathways, the blue light production and signal transduction pathways, the green light production and signal transduction pathways, the results obtained by running the simulation, and finally the analysis of the results obtained.




The Repressilator


The core of the model is formed by the Elowitz repressilator designed by Ty Thomson in 2009 (available to view here). This was one of the first to incorporate the concept of standardised biological parts (i.e. BioBricks) into a modelling context, attempting to "introduce a modular framework for modelling BioBrick parts and systems using rule-based modelling". The idea was to model at the level of individual parts, such that systems could be constructed using different components by paying a cost upfront with the construction of models of the parts, and thus making modular construction of specific models practically effort free - similar, in fact, to the idea of characterised and composable BioBricks used in the design and construction of synthetic circuits.

The framework as described by Thomson establishes a concise set of Kappa rules necessary to incorporate new BioBricks into such a model, by dividing them into four wide-ranging categories - promoter sequences, coding sequences, ribosome binding sites, and terminators. For example, a promoter sequence must define how repressor proteins and RNA polymerases bind with it, how transcription is initiated, and what happens when readthrough occurs and the promoter sequence is transcribed. A coding sequence must define its transcription, translation initiation and actual translation, and degradation of the translated protein (the action of the protein itself is not necessary, with the exception of its repressor activity which would be described in the corresponding promoter sequence). Finally, a ribosome binding site must define how a ribosome may bind with the site and how the RBS is transcribed, and a terminator must define how termination occurs, and what happens if termination fails (i.e. terminator readthrough).

The framework also describes what rates are necessary for the complete characterisation of the model. These roughly correspond to the rules given above, and include: promoter binding affinities and rate of RNAP recruitment; rate of transcription and rate of recruitment for ribosome binding sites; rates of transcription, translation, and degradation for protein coding sequences; and terminator percentage of successful termination. Although very few, if any, of the BioBricks in the Registry are characterised to this extent of modelling utility, such a framework at least provides something that we can be aiming for.

Thomson's model of the Elowitz repressilator was created as a working example of this framework, and is capable of fully simulating the interactions that occur within the system. The rules within fully satisfy the above framework for the repressilating reactions involving lacI, lambda-cI, and tetR and their associated BioBricks: BBa_B0034, BBa_R0051, BBa_R0040, BBa_R0010, BBa_C0051, BBa_C0040, BBa_C0012, and BBa_B0011.




Figure 1: Results of simulating Ty Thomson's repressilator model. Time units are arbitrary.



For details of Ty Thomson's repressilator model, readers are directed to the aforementioned RuleBase link as well as the actual Kappa model.





The Red Light Pathway


The red light production pathway consists of a red luciferase production gene coupled to the lacI promoter (BBa_R0010); the presence of LacI in the system inhibits the production of red luciferase. The assumption is then made that red luciferase translates directly into red light - essentially, that substrates such as luciferin are constitutively expressed, and there's no need to include it within the model.

The red light sensor pathway is a signal transduction pathway involving Cph8 (which can have either 'on' or 'off' state and can bind to OmpR) and OmpR (which can be either phosphorylated or unphosphorylated and can bind to either Cph8 or one of the ompC and ompF promoters). The assumption is made that there is a relatively static amount of Cph8 and OmpR within the system, and that there is no need to model their creation via transcription and translation or their degradation. Assumptions are also made regarding the balance between the concentration of on / off Cph8 and phosphorylated / unphosphorylated OmpR when the system is stable, as well as the fact that OmpR can only change phosphorylation state when not bound to any of Cph8, ompC, or ompF.




Figure 2: A simplified diagram of the red light sensor, showing the mechanism of action of red light on the Cph8-OmpR pathway.



When red light is not present in the system, an equilibrium exists between 'on' and 'off' Cph8 (heavily biased towards the 'on' state) and phosphorylated and unphosphorylated OmpR (heavily biased towards the phosphorylated state). When the red light sensing pathway is activated by bursts of photons at the correct wavelength, the Cph8 sensor is almost all turned 'off', which leads to OmpR almost fully unphosphorylated.

The ompC promoter is activated by phosphorylated OmpR in sufficient quantity, and is coupled to the BioBricked protein coding sequence BBa_C0040 that produces TetR. The ompF promoter is activated by minimal amounts of phosphorylated OmpR (and is inhibited by its presence in large quantities); when active, it stimulates production of LacI>. The presence of increased amounts of LacI in the system will act to inhibit the lacI promoter, which also controls expression of TetR as per the standard repressilator. Minor assumptions are made regarding the kinetic rates related to the ompF and ompC promoters within the model.

Thus, in standard conditions involving the isolated pathway, large amounts of TetR are produced due to the action of the phosphorylated OmpR promoter, whilst similarly the production of LacI is inhibited. When red light activates the signal transduction pathway, however, the concentration of unphosphorylated OmpR increases, which allows greater amounts of LacI to be produced, which in turn inhibits the production of TetR.






Figure 3: Results of simulating the red light sensing pathway as described above. Time units are arbitrary.






Figure 4: The same pathway without introducing a burst of red light into the system. Time units are arbitrary.



The graph in Figure 2 shows the results of simulating the red light sensing pathway, after inducing a short period of red light expression at t=100. This red light then stimulates an increased level of LacI within the system (in comparison to the control simulation in Figure 3), which acts to repress the amount of TetR present. The time units in the simulation are arbitrary but controlled by the kinetic rates used, which means that with further characterisation data, it would be possible to optimise the response of the pathway to actual conditions.



The Blue Light Pathway


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The Green Light Pathway


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Problems Encountered and Overcome


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Results


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Analysis


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