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Team:Edinburgh/Modelling/Kappa
From 2010.igem.org
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| - | <p>In order to model the core repressilator system and its attached signal transduction pathways, we have | + | <p>In order to model the core repressilator system and its attached signal transduction pathways, we have made use of a stochastic, agent- and rule-based language called Kappa.<p> |
| - | <center><br><br><p><img src="https://static.igem.org/mediawiki/2010/5/5c/Ed10-Agent.jpg" | + | <center><br><br><p><img src="https://static.igem.org/mediawiki/2010/5/5c/Ed10-Agent.jpg"></p><br><br></center> |
<p>In Kappa, biological entities such as proteins, DNA, and RNA are represented as agents, which are essentially named sets of sites that can be used to hold state or bind and interact with other agents. The example shown shows how a promoter BioBrick can be represented within Kappa – as a three-agent-long piece of DNA, connected via upstream and downstream sites, with binding sites for transcription factors and RNA polymerase, and a type site to keep track of its Registry code.</p> | <p>In Kappa, biological entities such as proteins, DNA, and RNA are represented as agents, which are essentially named sets of sites that can be used to hold state or bind and interact with other agents. The example shown shows how a promoter BioBrick can be represented within Kappa – as a three-agent-long piece of DNA, connected via upstream and downstream sites, with binding sites for transcription factors and RNA polymerase, and a type site to keep track of its Registry code.</p> | ||
| - | <center><br><br><p><img src="https://static.igem.org/mediawiki/2010/9/9e/Ed10-Rule.JPG" | + | <center><br><br><p><img src="https://static.igem.org/mediawiki/2010/9/9e/Ed10-Rule.JPG"></p><br><br></center> |
<p>Interactions are represented by rules in the form of precondition and effect, with an associated rate of reaction that governs how frequently the interaction occurs. The example shown describes a repressor binding to an open binding site upon a promoter; note the preconditions that both promoter and repressor binding sites must be empty beforehand, and the effect that they are now bound together. In this case, the reaction is reversible; that is, there are both forward and backward reaction rates associated with binding and dissociation of the repressor upon the promoter. By combining agents with an appropriate set of rules and rates, a Kappa model can be used to simulate systems of varying complexity, from a simple MAPK cascade to the oscillating rhythm of a circadian clock.</p> | <p>Interactions are represented by rules in the form of precondition and effect, with an associated rate of reaction that governs how frequently the interaction occurs. The example shown describes a repressor binding to an open binding site upon a promoter; note the preconditions that both promoter and repressor binding sites must be empty beforehand, and the effect that they are now bound together. In this case, the reaction is reversible; that is, there are both forward and backward reaction rates associated with binding and dissociation of the repressor upon the promoter. By combining agents with an appropriate set of rules and rates, a Kappa model can be used to simulate systems of varying complexity, from a simple MAPK cascade to the oscillating rhythm of a circadian clock.</p> | ||
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| - | <center><br><br><p><img src="https://static.igem.org/mediawiki/2010/9/92/Ed10-V2Results.png" | + | <center><br><br><p><img src="https://static.igem.org/mediawiki/2010/9/92/Ed10-V2Results.png"></p><br><br></center> |
| - | <p>The results of the simulation shown track the discrete counts of lambda-cI (red), TetR (blue), and LacI (yellow) in a slightly-modified version of the Elowitz repressilator | + | <p>The results of the simulation shown track the discrete counts of lambda-cI (red), TetR (blue), and LacI (yellow) in a slightly-modified version of the Elowitz repressilator, for one particular stochastic trajectory (obviously, different runs of the simulation will generate different results, some more variable than others). The modification made was the addition of a red luciferase BioBrick linked to a lacI promoter; high amounts of lacI repress the production of the red luciferase, but as soon as the concentration of lacI falls, the amount of red luciferase in the system rises, as expected.</p> |
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Revision as of 13:43, 17 August 2010
Overview: The Kappa modelling language
In order to model the core repressilator system and its attached signal transduction pathways, we have made use of a stochastic, agent- and rule-based language called Kappa.
In Kappa, biological entities such as proteins, DNA, and RNA are represented as agents, which are essentially named sets of sites that can be used to hold state or bind and interact with other agents. The example shown shows how a promoter BioBrick can be represented within Kappa – as a three-agent-long piece of DNA, connected via upstream and downstream sites, with binding sites for transcription factors and RNA polymerase, and a type site to keep track of its Registry code.
Interactions are represented by rules in the form of precondition and effect, with an associated rate of reaction that governs how frequently the interaction occurs. The example shown describes a repressor binding to an open binding site upon a promoter; note the preconditions that both promoter and repressor binding sites must be empty beforehand, and the effect that they are now bound together. In this case, the reaction is reversible; that is, there are both forward and backward reaction rates associated with binding and dissociation of the repressor upon the promoter. By combining agents with an appropriate set of rules and rates, a Kappa model can be used to simulate systems of varying complexity, from a simple MAPK cascade to the oscillating rhythm of a circadian clock.
The results of the simulation shown track the discrete counts of lambda-cI (red), TetR (blue), and LacI (yellow) in a slightly-modified version of the Elowitz repressilator, for one particular stochastic trajectory (obviously, different runs of the simulation will generate different results, some more variable than others). The modification made was the addition of a red luciferase BioBrick linked to a lacI promoter; high amounts of lacI repress the production of the red luciferase, but as soon as the concentration of lacI falls, the amount of red luciferase in the system rises, as expected.
For more information regarding Kappa and the basics of the language, please see the following resources:
