Team:Edinburgh/Modelling/Kappa

From 2010.igem.org

(Difference between revisions)
 
(11 intermediate revisions not shown)
Line 11: Line 11:
#body{
#body{
background-image:url(https://static.igem.org/mediawiki/2010/a/a8/Ed10-LargePaper.jpg);
background-image:url(https://static.igem.org/mediawiki/2010/a/a8/Ed10-LargePaper.jpg);
 +
background-repeat:repeat-y;
 +
}
 +
 +
#body2{
 +
background-image:url(https://static.igem.org/mediawiki/2010/9/9e/Ed10-LargePaperRipped.jpg);
background-repeat:repeat-y;
background-repeat:repeat-y;
}
}
Line 20: Line 25:
<body>
<body>
-
<div id="banner"><a href="https://2010.igem.org/Team:Edinburgh"><img src="https://static.igem.org/mediawiki/2010/5/5f/Ed10-Banner.jpg" /></a></div>
+
<div id="banner"><a href="https://2010.igem.org/Team:Edinburgh"><img src="https://static.igem.org/mediawiki/2010/5/5f/Ed10-Banner.jpg"/ ></a></div>
<a href="top"></a>
<a href="top"></a>
Line 46: Line 51:
   <ul>
   <ul>
   <li><a href="https://2010.igem.org/Team:Edinburgh/Project/Protocol">the protocol</a></li>
   <li><a href="https://2010.igem.org/Team:Edinburgh/Project/Protocol">the protocol</a></li>
-
   <li><a href="https://2010.igem.org/Team:Edinburgh/Project/BioBricks">submitted parts</a></li>
+
   <li><a href="https://2010.igem.org/Team:Edinburgh/BioBricks#Genomic">submitted parts</a></li>
-
   <li><a href="https://2010.igem.org/Team:Edinburgh/Project/Results">results</a></li>
+
   <li><a href="https://2010.igem.org/Team:Edinburgh/Results#Genomic">results</a></li>
-
   <li><a href="https://2010.igem.org/Team:Edinburgh/Project/Future">future work</a></li>
+
   <li><a href="https://2010.igem.org/Team:Edinburgh/Project/Future">the future</a></li>
   <li><a href="https://2010.igem.org/Team:Edinburgh/Project/References">references</a></li>
   <li><a href="https://2010.igem.org/Team:Edinburgh/Project/References">references</a></li>
   </ul>
   </ul>
Line 55: Line 60:
  <li><a href="https://2010.igem.org/Team:Edinburgh/Bacterial" class="dir">bacterial BRIDGEs</a>
  <li><a href="https://2010.igem.org/Team:Edinburgh/Bacterial" class="dir">bacterial BRIDGEs</a>
   <ul>
   <ul>
-
   <li><a href="https://2010.igem.org/Team:Edinburgh/Bacterial/Core_repressilator">the repressilator</a></li>
+
   <li><a href="https://2010.igem.org/Team:Edinburgh/Bacterial/Core_repressilator">the project</a></li>
   <li><a href="https://2010.igem.org/Team:Edinburgh/Bacterial/Red_light_producer">red light</a></li>
   <li><a href="https://2010.igem.org/Team:Edinburgh/Bacterial/Red_light_producer">red light</a></li>
   <li><a href="https://2010.igem.org/Team:Edinburgh/Bacterial/Red_light_sensor">red sensor</a></li>
   <li><a href="https://2010.igem.org/Team:Edinburgh/Bacterial/Red_light_sensor">red sensor</a></li>
Line 62: Line 67:
   <li><a href="https://2010.igem.org/Team:Edinburgh/Bacterial/Green_light_producer">green light</a></li>
   <li><a href="https://2010.igem.org/Team:Edinburgh/Bacterial/Green_light_producer">green light</a></li>
   <li><a href="https://2010.igem.org/Team:Edinburgh/Bacterial/Green_light_sensor">green sensor</a></li>
   <li><a href="https://2010.igem.org/Team:Edinburgh/Bacterial/Green_light_sensor">green sensor</a></li>
-
   <li><a href="https://2010.igem.org/Team:Edinburgh/Bacterial/BioBricks">submitted parts</a></li>
+
   <li><a href="https://2010.igem.org/Team:Edinburgh/BioBricks#Bacterial">submitted parts</a></li>
-
   <li><a href="https://2010.igem.org/Team:Edinburgh/Bacterial/Results">results</a></li>
+
   <li><a href="https://2010.igem.org/Team:Edinburgh/Results#Bacterial">results</a></li>
-
   <li><a href="https://2010.igem.org/Team:Edinburgh/Bacterial/Future">future work</a></li>
+
   <li><a href="https://2010.igem.org/Team:Edinburgh/Bacterial/Future">the future</a></li>
   <li><a href="https://2010.igem.org/Team:Edinburgh/Bacterial/References">references</a></li>
   <li><a href="https://2010.igem.org/Team:Edinburgh/Bacterial/References">references</a></li>
   </ul>
   </ul>
Line 75: Line 80:
   <li><a href="https://2010.igem.org/Team:Edinburgh/Modelling/Bacterial">the bacterial model</a></li>
   <li><a href="https://2010.igem.org/Team:Edinburgh/Modelling/Bacterial">the bacterial model</a></li>
   <li><a href="https://2010.igem.org/Team:Edinburgh/Modelling/Signalling">the signalling model</a></li>
   <li><a href="https://2010.igem.org/Team:Edinburgh/Modelling/Signalling">the signalling model</a></li>
-
   <li><a href="https://2010.igem.org/Team:Edinburgh/Modelling/Results">results</a></li>
+
   <li><a href="https://2010.igem.org/Team:Edinburgh/Modelling/Tools">tools</a></li>
-
   <li><a href="https://2010.igem.org/Team:Edinburgh/Modelling/Future">future work</a></li>
+
  <li><a href="https://2010.igem.org/Team:Edinburgh/Results#Modelling">results</a></li>
 +
   <li><a href="https://2010.igem.org/Team:Edinburgh/Modelling/Future">the future</a></li>
   <li><a href="https://2010.igem.org/Team:Edinburgh/Modelling/References">references</a></li>
   <li><a href="https://2010.igem.org/Team:Edinburgh/Modelling/References">references</a></li>
   </ul>
   </ul>
Line 83: Line 89:
  <li><a href="https://2010.igem.org/Team:Edinburgh/Human" class="dir">human BRIDGEs</a>
  <li><a href="https://2010.igem.org/Team:Edinburgh/Human" class="dir">human BRIDGEs</a>
   <ul>
   <ul>
-
   <li><a href="https://2010.igem.org/Team:Edinburgh/Human">human aspects</a></li>
+
   <li><a href="https://2010.igem.org/Team:Edinburgh/Human/Communication">communication of science</a></li>
-
   <li><a href="https://2010.igem.org/Team:Edinburgh/Human">results</a></li>
+
   <li><a href="https://2010.igem.org/Team:Edinburgh/Human/Branding">iGEM survey</a></li>
-
   <li><a href="https://2010.igem.org/Team:Edinburgh/Human">future work</a></li>
+
   <li><a href="https://2010.igem.org/Team:Edinburgh/Human/Conversations">conversations</a></li>
-
   <li><a href="https://2010.igem.org/Team:Edinburgh/Human">references</a></li>
+
  <li><a href="https://2010.igem.org/Team:Edinburgh/Human/Epic">the epic</a></li>
 +
  <li><a href="https://2010.igem.org/Team:Edinburgh/Human/FutureApps">future applications</a></li>
 +
   <li><a href="https://2010.igem.org/Team:Edinburgh/Results#Human">further thoughts</a></li>
 +
  <li><a href="https://2010.igem.org/Team:Edinburgh/Human/References">references</a></li>
   </ul>
   </ul>
  </li>
  </li>
Line 92: Line 101:
  <li><a href="https://2010.igem.org/Team:Edinburgh/Notebook" class="dir">lab notes&nbsp;&nbsp;&nbsp;</a>
  <li><a href="https://2010.igem.org/Team:Edinburgh/Notebook" class="dir">lab notes&nbsp;&nbsp;&nbsp;</a>
   <ul>
   <ul>
-
   <li><a href="https://2010.igem.org/Team:Edinburgh/Notebook">collaboration</a></li>
+
   <li><a href="https://2010.igem.org/Team:Edinburgh/Notebook/Collaboration">collaboration</a></li>
-
   <li><a href="https://2010.igem.org/Team:Edinburgh/Notebook">BRIDGE</a></li>
+
   <li><a href="https://2010.igem.org/Team:Edinburgh/Notebook/Attribution">attribution</a></li>
-
   <li><a href="https://2010.igem.org/Team:Edinburgh/Notebook">red light</a></li>
+
  <li><a href="https://2010.igem.org/Team:Edinburgh/Notebook/BRIDGE">BRIDGE</a></li>
-
   <li><a href="https://2010.igem.org/Team:Edinburgh/Notebook">red sensor</a></li>
+
   <li><a href="https://2010.igem.org/Team:Edinburgh/Notebook/Red_light_producer">red light</a></li>
-
   <li><a href="https://2010.igem.org/Team:Edinburgh/Notebook">blue light</a></li>
+
   <li><a href="https://2010.igem.org/Team:Edinburgh/Notebook/Red_light_sensor">red sensor</a></li>
-
   <li><a href="https://2010.igem.org/Team:Edinburgh/Notebook">blue sensor</a></li>
+
   <li><a href="https://2010.igem.org/Team:Edinburgh/Notebook/Blue_light_producer">blue light</a></li>
-
   <li><a href="https://2010.igem.org/Team:Edinburgh/Notebook">green light</a></li>
+
   <li><a href="https://2010.igem.org/Team:Edinburgh/Notebook/Blue_light_sensor">blue sensor</a></li>
-
   <li><a href="https://2010.igem.org/Team:Edinburgh/Notebook">green sensor</a></li>
+
   <li><a href="https://2010.igem.org/Team:Edinburgh/Notebook/Green_light_producer">green light</a></li>
-
   <li><a href="https://2010.igem.org/Team:Edinburgh/Notebook">safety</a></li>
+
   <li><a href="https://2010.igem.org/Team:Edinburgh/Notebook/Green_light_sensor">green sensor</a></li>
 +
   <li><a href="https://2010.igem.org/Team:Edinburgh/Notebook/Modelling">modelling</a></li>
 +
  <li><a href="https://2010.igem.org/Team:Edinburgh/Notebook/Safety">safety</a></li>
   <li><a href="http://www.openwetware.org/wiki/French_Lab">protocols</a></li>
   <li><a href="http://www.openwetware.org/wiki/French_Lab">protocols</a></li>
   </ul>
   </ul>
Line 123: Line 134:
<br>
<br>
-
<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>
+
<p>In order to <b>model</b> the core repressilator system and its attached signal transduction pathways, we utilised a stochastic agent- and rule-based language called <a href="http://kappalanguage.org/">Kappa</a>.<p>
-
<center><br><br><p><img src="https://static.igem.org/mediawiki/2010/5/5c/Ed10-Agent.jpg"></p><br>
+
<center><br><br><p><img src="https://static.igem.org/mediawiki/2010/5/5c/Ed10-Agent.jpg" width="600px"></p><br>
<p><b>Figure 1:</b> Three Kappa agents, representing DNA, bound together at their respective upstream and downstream sites.</p><br><br></center>
<p><b>Figure 1:</b> Three Kappa agents, representing DNA, bound together at their respective upstream and downstream sites.</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 <b>agents</b>, which are essentially named sets of sites that can be used to hold <b>state</b> or bind and <b>interact</b> with other agents. The example shown in <a href="https://static.igem.org/mediawiki/2010/5/5c/Ed10-Agent.jpg">Figure 1</a> 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"></p><br>
+
<center><br><br><p><img src="https://static.igem.org/mediawiki/2010/9/9e/Ed10-Rule.JPG" width="600px"></p><br>
<p><b>Figure 2:</b> A Kappa rule representing the binding of a repressor protein to the promoter region of a DNA.</p><br><br></center>
<p><b>Figure 2:</b> A Kappa rule representing the binding of a repressor protein to the promoter region of a DNA.</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 in <a href="https://static.igem.org/mediawiki/2010/9/9e/Ed10-Rule.JPG">Figure 2</a> 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 <b>simulate</b> systems of varying complexity, from a simple MAPK cascade to the oscillating rhythm of a circadian clock.</p>
</div>
</div>
-
<div id="body" style="padding: 0px 60px 10px 60px; height: 1356px">
+
<div id="body2" style="padding: 0px 60px 10px 60px; height: 998px">
<br>
<br>
<br>
<br>
-
<center><br><br><p><img src="https://static.igem.org/mediawiki/2010/9/92/Ed10-V2Results.png"></p><br>
+
<center><br><br><p><img src="https://static.igem.org/mediawiki/2010/9/92/Ed10-V2Results.png" width="600px"></p><br>
-
<p><b>Figure 3:</b> A Kappa simulation of a repressilator, with red luciferase protein repressed by the presence of lacI.</p><br><br></center>
+
<p><b>Figure 3:</b> A Kappa simulation of a repressilator, with red luciferase protein repressed by the presence of lacI. Units (time and concentration) are arbitrary.</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, 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>
+
<p>The results of the simulation shown in <a href="https://static.igem.org/mediawiki/2010/9/92/Ed10-V2Results.png">Figure 3</a> track the discrete counts of lambda-cI (red), TetR (blue), and LacI (yellow) in a slightly <b>modified</b> 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 <b>modification</b> 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>
-
<p>Agent- and rule-based modelling allows for the circumvention of one of the primary problems with biological modelling: the fact that molecular entities existing under different conditions (phosphorylation, states of activation, etc.) can result in quantitative combinatorial explosion that greatly complicates traditional modelling methods such as differential equations. Kappa models can be unambiguously reduced to their ODE counterparts, but to do so would likely result in a system too large and too complicated to understand or create from scratch, as each rule in Kappa would equate to a potentially massive number of reactions in an ODE model.</p>
+
<p>Agent- and rule-based modelling allows for the <b>circumvention</b> of one of the primary problems with biological modelling: the fact that molecular entities existing under different conditions (phosphorylation, states of activation, etc.) can result in <b>quantitative combinatorial explosion</b> that greatly <b>complicates</b> traditional modelling methods such as differential equations. Kappa models can be unambiguously reduced to their ODE counterparts, but to do so would likely result in a system too large and too complicated to understand or create from scratch, as each rule in Kappa would equate to a potentially massive number of reactions in an ODE model.</p>
<br>
<br>
Line 164: Line 175:
<center><a href="#top" class="dir"><img width="100" src="https://static.igem.org/mediawiki/2010/9/9f/Ed10-RTT.png"></a></center>
<center><a href="#top" class="dir"><img width="100" src="https://static.igem.org/mediawiki/2010/9/9f/Ed10-RTT.png"></a></center>
 +
</div>
 +
 +
<div id="windowbox" style="border: .2em solid #660000; padding: 5px; position:fixed; top:50%; right:30px; width:8%;">
 +
<span style="color:ivory;">Throughout this wiki there are words in <b>bold</b> that indicate a relevance to <b>human aspects</b>. It will become obvious that <b>human aspects</b> are a part of almost everything in <b>iGEM</b>.</span>
 +
</div>
</div>

Latest revision as of 02:24, 28 October 2010







Overview: The Kappa modelling language


In order to model the core repressilator system and its attached signal transduction pathways, we utilised a stochastic agent- and rule-based language called Kappa.




Figure 1: Three Kappa agents, representing DNA, bound together at their respective upstream and downstream sites.



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 in Figure 1 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.




Figure 2: A Kappa rule representing the binding of a repressor protein to the promoter region of a DNA.



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 in Figure 2 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.






Figure 3: A Kappa simulation of a repressilator, with red luciferase protein repressed by the presence of lacI. Units (time and concentration) are arbitrary.



The results of the simulation shown in Figure 3 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.

Agent- and rule-based modelling allows for the circumvention of one of the primary problems with biological modelling: the fact that molecular entities existing under different conditions (phosphorylation, states of activation, etc.) can result in quantitative combinatorial explosion that greatly complicates traditional modelling methods such as differential equations. Kappa models can be unambiguously reduced to their ODE counterparts, but to do so would likely result in a system too large and too complicated to understand or create from scratch, as each rule in Kappa would equate to a potentially massive number of reactions in an ODE model.


For more information regarding Kappa and the basics of the language, please see the following resources:




Throughout this wiki there are words in bold that indicate a relevance to human aspects. It will become obvious that human aspects are a part of almost everything in iGEM.