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Team:Edinburgh/Modelling/Bacterial
From 2010.igem.org
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| - | Blue light production - blue luciferase production gene coupled to tetR promoter (R0040); presence of | + | Blue light production - blue luciferase production gene coupled to tetR promoter (R0040); presence of tetR in system inhibits production of blue luciferase. |
Assume that blue luciferase translates directly into blue light, without need to model LuxCDE and lumazine. | Assume that blue luciferase translates directly into blue light, without need to model LuxCDE and lumazine. | ||
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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 | + | 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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<p>***</p><br> | <p>***</p><br> | ||
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| + | Green light production - green luciferase production gene coupled to lambda-CI promoter (R0051); presence of lambda-CI in system inhibits production of green luciferase. | ||
| + | Assume that green luciferase translates directly into green light, without need to model any helper proteins such as LuxC, LuxD, LuxE. | ||
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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. | ||
| + | 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. | ||
| + | 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. | ||
| + | Rates are balanced against one another and against those of the core repressilator to produce clean behaviour. | ||
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| + | <!-- <center><br><br><p><img src="https://static.igem.org/mediawiki/2010/c/cd/Ed10-GreenLightResults.png"></p><br> | ||
| + | <p><b>Figure 6:</b> Results of simulating the green light sensing pathway as described above.</p><br><br></center> --> | ||
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| + | <!-- <center><br><br><p><img src="https://static.igem.org/mediawiki/2010/9/9a/Ed10-GreenLightWildtype.png"></p><br> | ||
| + | <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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| + | 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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| + | Problems encountered and how they were solved, assumptions made. | ||
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Revision as of 13:25, 7 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.
For details of Ty Thomson's repressilator model, readers are directed to the aforementioned RuleBase link as well as the actual Kappa model.
