Team:Edinburgh/Modelling/Bacterial

From 2010.igem.org

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<center><br><br><p><img src="https://static.igem.org/mediawiki/2010/6/6e/Ed10-RepressilatorResults.png"></p><br>
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<p><b>Figure 1:</b> Results of simulating Ty Thomson's repressilator model.</p><br><br></center>
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<p><b>Figure 1:</b> Results of simulating Ty Thomson's repressilator model. Time units are arbitrary.</p><br><br></center>
<p>For details of Ty Thomson's repressilator model, readers are directed to the <a href="http://www.cellucidate.com/showcase_books/182350-Rule-Based-Modeling-of-BioBrick-Parts">aforementioned RuleBase link</a> as well as the <a href="https://static.igem.org/mediawiki/2010/f/f8/Ed10-Repressilator.txt">actual Kappa model</a>.</p><br>
<p>For details of Ty Thomson's repressilator model, readers are directed to the <a href="http://www.cellucidate.com/showcase_books/182350-Rule-Based-Modeling-of-BioBrick-Parts">aforementioned RuleBase link</a> as well as the <a href="https://static.igem.org/mediawiki/2010/f/f8/Ed10-Repressilator.txt">actual Kappa model</a>.</p><br>
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Red light production - red luciferase production gene coupled to lacI promoter (R0010); presence of lacI in system inhibits production of red luciferase.
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<p>The red light production pathway consists of a red luciferase production gene coupled to lacI promoter (R0010); presence of lacI in system inhibits production of red luciferase.
Assume that red luciferase translates directly into red light - luciferin is constitutively expressed, and there's no need to model it.
Assume that red luciferase translates directly into red light - luciferin is constitutively expressed, and there's no need to model it.
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<p><b>Figure 2:</b> Results of simulating the red light sensing pathway as described above.</p><br><br></center> -->
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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.</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.
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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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. Fine-tuning of response was an integral part of the model-building process. Especially difficult since this is a two-stage pathway (as modelled) and hence the response to the stimulus is more complex, and there are two different actions with hopefully the same effect at work here. Analysis of which one is more powerful, whether or not both of them are required, etc.
 
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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><b>Figure 4:</b> Results of simulating the blue light sensing pathway as described above.</p><br><br></center> -->
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<p><b>Figure 4:</b> Results of simulating the blue light sensing pathway as described above. Time units are arbitrary.</p><br><br></center> -->
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<p><b>Figure 5:</b> The same pathway without introducing a burst of blue light into the system.</p><br><br></center> -->
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<p><b>Figure 5:</b> The same pathway without introducing a burst of blue light into the system. Time units are arbitrary.</p><br><br></center> -->
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The graph in Figure 4 shows the results of simulating the blue light sensing pathway, after inducing a short period of blue light expression at t=100. This blue light then represses the production of lambda-CI within the system for a short period of time (in comparison to the control simulation in Figure 5), before the effect wears off and transcription / translation are allowed to occur once more.
The graph in Figure 4 shows the results of simulating the blue light sensing pathway, after inducing a short period of blue light expression at t=100. This blue light then represses the production of lambda-CI within the system for a short period of time (in comparison to the control simulation in Figure 5), before the effect wears off and transcription / translation are allowed to occur once more.
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Similar problems to the above, except the pathway as a whole has a simpler response (single-step, only one effect). 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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Green light sensor - signal transduction pathway composed of a proposed fusion between the CcaS receptor and the PhoB response regulator, similar action to that of the red light sensor. The fusion protein can be either 'on' or 'off'; when 'on', it acts to phosphorylate the response protein (PhoR). The phosphorylated PhoR can then act as an inhibitory transcription factor on a hypothetical promoter possibly based on phoA, phoS, ugpB genes. Assumption - PhoR is an activator rather than a repressor, and thus some form of negation (i.e. a 'NOT' gate) will have to be built into the system, either as part of the pathway (similar to the inverter built into the red transduction pathway) or by creating the hypothetical promoter as modelled.
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Green light sensor - signal transduction pathway composed of a proposed fusion between the CcaS receptor and the PhoR response regulator, similar action to that of the red light sensor. The fusion protein can be either 'on' or 'off'; when 'on', it acts to phosphorylate the response protein (PhoB). The phosphorylated PhoB can then act as an inhibitory transcription factor on a hypothetical promoter possibly based on phoA, phoS, ugpB genes. Assumption - PhoB is an activator rather than a repressor, and thus some form of negation (i.e. a 'NOT' gate) will have to be built into the system, either as part of the pathway (similar to the inverter built into the red transduction pathway) or by creating the hypothetical promoter as modelled.
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When green light is not present in the system, the CcaS-PhoB fusion remains in the 'off' state; when green light is present, it changes configuration to the 'on' state, which then cascades down the transduction pathway. Assumptions - rate.
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When green light is not present in the system, the CcaS-PhoR fusion remains in the 'off' state; when green light is present, it changes configuration to the 'on' state, which then cascades down the transduction pathway. Assumptions - rate.
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Thus, in standard conditions, large amounts of LacI are produced; when blue light activates the signal transduction pathway, concentration of activated CcaS-PhoB (and thus PhoR) increases, which represses the production of LacI in the system until the effect wears off.
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Thus, in standard conditions, large amounts of LacI are produced; when blue light activates the signal transduction pathway, concentration of activated CcaS-PhoR (and thus PhoB) increases, which represses the production of LacI in the system until the effect wears off.
Rates are balanced against one another and against those of the core repressilator to produce clean behaviour.
Rates are balanced against one another and against those of the core repressilator to produce clean behaviour.
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<p><b>Figure 6:</b> Results of simulating the green light sensing pathway as described above.</p><br><br></center> -->
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<p><b>Figure 6:</b> Results of simulating the green light sensing pathway as described above. Time units are arbitrary.</p><br><br></center> -->
<!-- <center><br><br><p><img src="https://static.igem.org/mediawiki/2010/9/9a/Ed10-GreenLightWildtype.png"></p><br>
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<p><b>Figure 7:</b> The same pathway without introducing a burst of green light into the system.</p><br><br></center> -->
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<p><b>Figure 7:</b> The same pathway without introducing a burst of green light into the system. Time units are arbitrary.</p><br><br></center> -->
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The graph in Figure 6 shows the results of simulating the green light sensing pathway, after inducing a short period of green light expression at t=100. This green light then represses the production of LacI within the system for a short period of time (in comparison to the control simulation in Figure 7), before the effect wears off and transcription / translation are allowed to occur once more.
The graph in Figure 6 shows the results of simulating the green light sensing pathway, after inducing a short period of green light expression at t=100. This green light then represses the production of LacI within the system for a short period of time (in comparison to the control simulation in Figure 7), before the effect wears off and transcription / translation are allowed to occur once more.
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<div id="body" style="padding: 0px 60px 10px 60px; height: 1356px">
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<a name="Problems" id="Problems"></a><h2>Problems Encountered and Overcome</h2>
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<p>***</p><br>
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Problems encountered and how they were solved, assumptions made.
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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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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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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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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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<!-- <center><br><br><p><img src="https://static.igem.org/mediawiki/2010/d/df/Ed10-RepressilatingLightsResults.png"></p><br>
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<p><b>Figure 1:</b> A composite agent with four DNA agents joined together at their upstream and downstream sites, representing the <i>cat-sacB</i> construct and surrounding DNA.</p><br><br></center>
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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>
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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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Results of the entire system.
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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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Revision as of 09:29, 10 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


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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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