Team:Southampton/Modeling

From 2009.igem.org

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         <td bgcolor="#5D5D5D"><h3><span style="text-align: center">To
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         <td bgcolor="#5D5D5D"><h3>In modelling our cell networks, we found it helpful to  visualize our machines at a higher level of abstraction than the ubiquitous  plasmid map. Flow charts were chosen to efficiently represent the dynamics of  the system, borrowing from the engineering concept of finite state-machines.</h3>
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          give the team some indication of the expected behaviour of our
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           <p>&nbsp;</p>
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          completed systems, a simulation environment was made to allow the
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           <h3>In our Game of Life system, there is only one &lsquo;machine&rsquo; type  and two diffusing molecules, IPTG and LuxI. As this is slightly simpler than  Rock-Paper-Scissors, with its three machine types and molecules, we implemented  the Game of Life model first.</h3>
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          models to interact with each other. More details of the simulation
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           <p>&nbsp;</p>
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          source code are to be released at a later date. The program is written
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           <h3>A simulator was designed to interpret the flow charts and  model the system to produce spatio-temporal patterns. The simulation  environment is defined by an adjustable square grid, typically 100 x 100 for  our simulations. The environment is divided into several layers, starting with  the cell layer. Each location on the grid can be occupied by a single cell of a  predefined type and state. As the simulation progresses cells can change state  in response to the diffusion of molecules in the layers below them, as defined  by their state transition rules. Below the cell layer reside the molecule  layers, with each molecule occupying its own layer. Each grid location holds a  number representing the total number of molecules present in that space. A  diffusion algorithm runs constantly during simulations, allowing a high  concentration of molecules in one area to diffuse to an area of lower  concentration. As each molecule resides on its own layer they never interact  with each other.          </h3>
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          in Tcl [link http://wiki.tcl.tk/] and is designed to be easily modified
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           <h3>&nbsp;</h3>
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          to model any system involving several cells interacting with each other
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          through the diffusion of chemical species.<br />
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           <br />
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              <td><img src="https://static.igem.org/mediawiki/2009/4/42/Layers2.png" width="758" height="524" /></td>
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           The results of an initial simulation of our Game of Life system are
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          presented below.<br />
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           <br />
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           <p>&nbsp;</p>
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          <img alt=""
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           <h3>The simulator is written in <a href="http://wiki.tcl.tk/">Tcl</a>             and relies heavily on the <a href="http://tcl-nap.sourceforge.net/">NAP</a>&nbsp;extension for fast array  processing. Simulation durations are defined by the number of times the diffusion algorithm is run and it typically requires about 2 x 10^5 iterations  for an initial drop of a molecule in the centre of the grid to diffuse to the  edges (for a 100 x 100 grid). On the ageing laboratory P4 it took about 90  minutes to run the simulation described above.</h3>
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src="http://img211.imageshack.us/img211/6494/iptg.gif" /><br />
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           <p>&nbsp;</p>
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           <br />
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           <h3>After generating several interesting results from the Game  of Life simulation, we looked to modifying the system to simulate our  Rock-Paper-Scissors models. Much of the source code was the same for both  systems leading us to wonder, &lsquo;What if there was a program that could simulate  any system with mechanisms similar to ours?&rsquo; The simulator was generalized to  allow it to be used with any system involving the production and diffusion of  chemical products between cellular machines. A user interface was also added to allow non-programmers to enter their models and run simulations of their own.</h3>
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          The animation above shows an initial drop of IPTG being added to the
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           <p>&nbsp;</p>
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          system that activates the cells in its immediate vicinity. Note that
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           <h3>The results of our simulations can be at the <a href="https://2009.igem.org/Team:Southampton/Modeling/GoL">Game of  Life</a>&nbsp;and <a href="https://2009.igem.org/Team:Southampton/Modeling/RPS">Rock-Paper-Scissors</a> pages.</h3>
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          the IPTG does not diffuse throughout the system, not causing any
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          <p>&nbsp;</p>
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          effects other than the initial &lsquo;jump start&rsquo;.<br />
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          <h3>The simulator package is available for download from the <a href="https://2009.igem.org/Team:Southampton/Modeling/Open">Open  Access</a>&nbsp;page. Although it  currently has all of the features required to model our systems and those  similar to them, there is much more that can be done. The program has been  open-sourced and instructions on how to acquire the source can also be found there.</h3>
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           <br />
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          <h3><br />
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           <img alt=""
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          </h3></td>
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src="http://img211.imageshack.us/img211/5158/luxi.gif" /><br />
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          <br />
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           This animation shows the activated cells producing LuxI which then
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          diffuses to the rest of the system, activating further cells.<br />
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           <br />
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           <img alt=""
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src="http://img376.imageshack.us/img376/6968/cells.gif" /><br />
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          <br />
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          The final animation models the actual bacteria, randomly distributed
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          throughout the system. Blue for those in the 'off' state and red for
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          those in the 'on' state.<br />
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           <br />
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          An&nbsp; IPTG local density of 250 is required to switch a cell to
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          it's 'on' state. 'On' cells produce LuxI, turning on other cells when
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           the local density of LuxI is greater than 50. When the local density of
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          LuxI is greater than 300, the cell switches to the 'off' state,
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          producing the characteristic ring. Note, all of the values above are
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          arbitrary values and will be translated to molecule counts when
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          empirical results are available.<br />
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           <br />
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           The figure below shows all three layers together, allowing easy
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          comparison. To view the animations again, refresh this page.<br />
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          <br />
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          <img style="width: 400px; height: 107px;"
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alt=""
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src="http://img35.imageshack.us/img35/2626/cellsiptgluxi400x.gif" /></span><br />
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        </h3></td>
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Revision as of 18:19, 20 October 2009

<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> University of Southampton Wiki

Modelling

 

 


In modelling our cell networks, we found it helpful to visualize our machines at a higher level of abstraction than the ubiquitous plasmid map. Flow charts were chosen to efficiently represent the dynamics of the system, borrowing from the engineering concept of finite state-machines.

 

In our Game of Life system, there is only one ‘machine’ type and two diffusing molecules, IPTG and LuxI. As this is slightly simpler than Rock-Paper-Scissors, with its three machine types and molecules, we implemented the Game of Life model first.

 

A simulator was designed to interpret the flow charts and model the system to produce spatio-temporal patterns. The simulation environment is defined by an adjustable square grid, typically 100 x 100 for our simulations. The environment is divided into several layers, starting with the cell layer. Each location on the grid can be occupied by a single cell of a predefined type and state. As the simulation progresses cells can change state in response to the diffusion of molecules in the layers below them, as defined by their state transition rules. Below the cell layer reside the molecule layers, with each molecule occupying its own layer. Each grid location holds a number representing the total number of molecules present in that space. A diffusion algorithm runs constantly during simulations, allowing a high concentration of molecules in one area to diffuse to an area of lower concentration. As each molecule resides on its own layer they never interact with each other.

 

 

The simulator is written in Tcl and relies heavily on the NAP extension for fast array processing. Simulation durations are defined by the number of times the diffusion algorithm is run and it typically requires about 2 x 10^5 iterations for an initial drop of a molecule in the centre of the grid to diffuse to the edges (for a 100 x 100 grid). On the ageing laboratory P4 it took about 90 minutes to run the simulation described above.

 

After generating several interesting results from the Game of Life simulation, we looked to modifying the system to simulate our Rock-Paper-Scissors models. Much of the source code was the same for both systems leading us to wonder, ‘What if there was a program that could simulate any system with mechanisms similar to ours?’ The simulator was generalized to allow it to be used with any system involving the production and diffusion of chemical products between cellular machines. A user interface was also added to allow non-programmers to enter their models and run simulations of their own.

 

The results of our simulations can be at the Game of Life and Rock-Paper-Scissors pages.

 

The simulator package is available for download from the Open Access page. Although it currently has all of the features required to model our systems and those similar to them, there is much more that can be done. The program has been open-sourced and instructions on how to acquire the source can also be found there.


 


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