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Team:Edinburgh/Modelling/Bacterial
From 2010.igem.org
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| - | Red light sensor - signal transduction pathway involving Cph8 (which can be either 'on' or 'off' 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 (BioBricks <a href="http://partsregistry.org/Part:BBa_R0082">BBa_R0082</a> and <a href="http://partsregistry.org/Part:BBa_R0084">BBa_R0084</a>). Assumption - static amount of Cph8 and OmpR within the system. Assumptions also regarding the balance between on and off Cph8 / OmpR rates. | + | Red light sensor - signal transduction pathway involving Cph8 (which can be either 'on' or 'off' 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 (BioBricks <a href="http://partsregistry.org/Part:BBa_R0082">BBa_R0082</a> and <a href="http://partsregistry.org/Part:BBa_R0084">BBa_R0084</a>). Assumption - static amount of Cph8 and OmpR within the system. Assumptions also regarding the balance between on and off Cph8 / OmpR rates, OmpR can only change state when not bound. |
When red light is not present in the system, balance between 'on' and 'off' Cph8 and 'phosphorylated' and 'unphosphorylated' OmpR. When red light is present, Cph8 is almost all turned 'off', which leads to OmpR almost fully unphosphorylated. | When red light is not present in the system, balance between 'on' and 'off' Cph8 and 'phosphorylated' and 'unphosphorylated' OmpR. When red light is present, Cph8 is almost all turned 'off', which leads to OmpR almost fully unphosphorylated. | ||
OmpC promoter is activated by 'phosphorylated' OmpR, and stimulates the production of TetR. OmpF promoter is activated by small amounts of 'phosphorylated' OmpR, but inhibited by large amounts; 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) | OmpC promoter is activated by 'phosphorylated' OmpR, and stimulates the production of TetR. OmpF promoter is activated by small amounts of 'phosphorylated' OmpR, but inhibited by large amounts; 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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| - | Blue light sensor - signal transduction pathway composed of the hybrid LovTAP protein. LovTAP can either be in a 'light' state or a 'dark' state, and can bind to the Trp promoter BioBricked as <a href="http://partsregistry.org/Part:BBa_K191007">BBa_K191007</a>. Assumptions regarding the balance between on and off LovTAP rates. | + | Blue light sensor - signal transduction pathway composed of the hybrid LovTAP protein. LovTAP can either be in a 'light' state or a 'dark' state, and can bind to the Trp promoter BioBricked as <a href="http://partsregistry.org/Part:BBa_K191007">BBa_K191007</a>. Assumptions regarding the balance between on and off LovTAP rates, LovTAP can only change state when not bound. |
When blue light is not present in the system, LovTAP remains in the 'dark' state; when blue light is present, LovTAP changes configuration to the 'light' state, which allows it to bind to the Trp promoter and thus inhibit the production of lambda-CI. Assumptions - rate. | When blue light is not present in the system, LovTAP remains in the 'dark' state; when blue light is present, LovTAP changes configuration to the 'light' state, which allows it to bind to the Trp promoter and thus inhibit the production of lambda-CI. Assumptions - rate. | ||
| - | + | Thus, in standard conditions, large amounts of lambda-CI are produced; when blue light activates the signal transduction pathway, concentration of activated LovTAP increases, which represses the production of lambda-CI in the system until the effect wears off. | |
| - | Thus, in standard conditions, large amounts of | + | |
| - | + | ||
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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| - | <!-- <center><br><br><p><img src="https://static.igem.org/mediawiki/2010/ | + | <!-- <center><br><br><p><img src="https://static.igem.org/mediawiki/2010/7/73/Ed10-BlueLightResults.png"></p><br> |
| - | <p><b>Figure | + | <p><b>Figure 4:</b> Results of simulating the blue 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/2010/7/7e/Ed10-BlueLightWildtype.png"></p><br> | ||
| + | <p><b>Figure 5:</b> The same pathway without introducing a burst of blue light into the system.</p><br><br></center> --> | ||
</div> | </div> | ||
Revision as of 14:57, 31 August 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.
