Team:Evry/Model/phenol model

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<h1>Phenol sensor model</h1>
<h1>Phenol sensor model</h1>
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<p>
<p>
In this section we model the phenol sensor in Kappa.  
In this section we model the phenol sensor in Kappa.  
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Kappa is a rule based language allowing the expression of protein-protein interactions in order to build executable models of protein networks.
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Kappa is a rule based language allowing the expression of protein-protein interactions in order to build executable models of protein networks. This project is supported by the harvard medical school and can be found in the <a href="http://www.kappalanguage.org/">Kappa homepage</a>.
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according to literature, the phenol sensor is composed of phosphoriled Dmpr dimers forming hexamers and binding on the P0 site as described in the schemas bellow from  
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This model is then simulated using KaSim, an open source stochastic simulator for rule-based models written in Kappa.
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Basically, KaSim takes one or several Kappa files as input and generates stochastic trajectories of various observables.
 +
according to literature, the phenol sensor is composed of phosphoriled Dmpr dimers forming hexamers and binding on the P0 site as described in the schemas below from <b>David Tropel and Jan Roelof van der Meer researches</b>[1].
 +
</p>
<div class="center"><img src="https://static.igem.org/mediawiki/2014/e/ea/Phenol_model.png"></div>
<div class="center"><img src="https://static.igem.org/mediawiki/2014/e/ea/Phenol_model.png"></div>
<br/>
<br/>
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</p>
 
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<p>
<p>
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More precisely we are interested in finding: what is the quantity of compound that is in contact with our bacterium ?</br><br/>
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From this article and various other[2,3,4,5,6], we define a first model in Kappa.
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+
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With this quantity and the number of bacteria in the sponge, we can the connect with the models developed for <a href="https://2014.igem.org/Team:Evry/Model/pcb_model">PCB</a> and <a href="">phenol (TODO) </a> sensing and then relate the sensing capacity to the concentration of compound in the surrounding water.
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</p>
</p>
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<br />
 +
<h3>Phenol-Dmpr model</h3>
<p>
<p>
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To answer this question, we built two different model :
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In this first model, we define the hexamer needed for the phenol sensing to be composed of one Phenol complex and two none phosphoriled Dmpr dimers.
-
 
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We define the Phenol complex as a Dmpr dimer with a phenol binded and next phosphoriled with an ATP in order to binds other Dmpr.
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<ol>
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schematicaly it correspond to the schemas below.
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<li>A simple model where we consider only water flows without trying to take into acount the very specific geometry of the sponge.</li>
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<li>A 2D diffusion model where we take into acount the geometry of the sponge.</li>
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</ol>
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</p>
</p>
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<div class="center"><img src="https://static.igem.org/mediawiki/2014/0/00/Mod_ph_1.png" width="867px"></div>
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<p>
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<p>First (1) two dmpr form a dimer, next, this dimer will recruit a phenol molecule (2), then this complex phosphorize in order to change its conformation (3) finally the complex recruit free Dmpr Dimers in order to form hexamers complexes (4). This hexamer will trigger the P0 site in order to activate the GFP production.
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Throughout this section we base our study on the <i>Spongia Officinalis</i> species, because i) there are evidences that pseudovibrio bacteria live inside (TODO: cite) and ii) its is a quite common type of sponge.<br/>
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</p>
</p>
 +
<br />
 +
<p>The resulting Kappa program describing all thosess steps is the following :</p>
 +
<pre>
 +
#agents
 +
#dmpr considered as a dimer: s : binding between dimers, need to be phosphoriled, x: binding phenol
 +
%agent: dmpr(s,x)
 +
%agent: phenol(x)
 +
%agent: phComplex2(s)
 +
%agent: hexamere(b)
 +
%agent: gfp(b)
 +
%agent: pgfp(b)
 +
#rule
 +
'assos_ph' dmpr(s,x),phenol(x) <-> dmpr(s,x!1),phenol(x!1) @ 'ass','diss'
 +
'recr_atp' dmpr(s,x!1),phenol(x!1) -> phComplex2(s) @ 'ph'
 +
'assos_quadr' phComplex2(s),dmpr(s,x) <-> phComplex2(s!1),dmpr(s!1,x) @ 'ass','diss'
 +
'assos_hexa' phComplex2(s!1),dmpr(s!1,x),dmpr(s,x) <-> hexamere(b) @ 'ass', 'diss'
 +
'gfp_bind' hexamere(b),pgfp(b) -> hexamere(b!1),pgfp(b!1) @ 'ass'
 +
'gfp_prod' hexamere(b!1),pgfp(b!1) -> pgfp(b),gfp(b),dmpr(s,x),dmpr(s,x),dmpr(s,x) @ 0.9
 +
'dmpr_prod' -> dmpr(s,x) @ 10 # TO VARY !!
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<h2>Model 1: Simple Fluxes</h2>
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#var
 +
%var: 'gfp' 0
 +
%var: 'dmpr_qt' 100
 +
%var: 'phenol_qt' 1000
 +
%var: 'gfp_p_qt' 100
 +
%var: 'cell_v' 1E-15
 +
%var: 'cell_av' 1E8
 +
%var: 'ph' 5E-3
 +
%var: 'ratio' 'cell_v'/(2.25E-12)
 +
%var: 'ass' (1E-5)*(1/'ratio')
 +
%var: 'diss' (1E-3)*(1/'ratio')
 +
%init: 'phenol_qt' phenol()
 +
%init: 'gfp_p_qt' pgfp()
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%init: 'dmpr_qt' dmpr()
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%mod: [Tsim] > 1800 do $STOP
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<p>
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#def
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For this model, we make simple computations based on the intake and expeled quantities of water. Our main assumptions are the following :
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%def: "dumpInfluenceMap" "yes"
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%def: "influenceMapFileName" "influenceMap"
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<ol>
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%def: "dotSnapshots" "yes"
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<li>Bacteria are uniformely distributed in the sponge</li>
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%mod: [true] do $FLUX "flux.dot" [true]
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<li>Compounds diffuse instantly inside the sponge (the quantity of compound in the same everywhere inside)</li>
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%mod: repeat [E+]/[E]>0 do $SNAPSHOT until [true]
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</ol>
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These two assumptions imply that each bacterium is in contact with the same quantity of compound.
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</p>
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<h5> Model Formulation </h5>
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 +
#obs
 +
%obs: 'gfp_produced' gfp(b)
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%obs: 'phenol_k' phenol(x)
 +
</pre>
 +
<p>In this model, all the variables are defined according to biologicals values.
 +
For example, the cell_v correspond to E.coli volue and cell_av to the Avogadro number in E.coli. all the association and phosphorilation constants are calculated for general molecules in e.coli or extracted from articles when found.
 +
We choose to use e.coli for our model in order to correspond to the biological results</p>
 +
<br />
 +
<p>The first rule describe the phenol binding, the second describe the phosphorilation of the complex, the two next ones correspond to the formation of the hexamer complex.
 +
The next rule correspond to the binding of the hexamer to the gfp promoter.
 +
Finally the two last rules correspond to the gfp production and the dmpr production.</p>
 +
<br />
 +
<p>after running KaSim for differents values of Dmpr production, we obtain the two next graphics</p>
 +
<div class="center">
 +
<table>
 +
<tr>
 +
    <td><img src="https://static.igem.org/mediawiki/2014/7/72/Gfp_phenol.png" /></td>
 +
    <td><img src="https://static.igem.org/mediawiki/2014/4/4e/Phenol_concentration.png" /></td>
 +
</tr>
 +
</table>
 +
</div>
 +
<p>The first one represent the gfp concentration over time for different Dmpr production. The second represent the the phenol concentration over time for five different Dmpr production (1E2, 1E1, 1E-1, 1E-2,1E-4,1E-5).
 +
those differents curves show us that in this model we reach a level where the gfp produced become null due to the fact that all Dmpr are binded with phenol and so can't form hexamer. Indeed the Phenol complexe need two FREE dmpr in order to create an hexamer. Simply speaking, all the Dmpr try to recruit a phenol and in the slow production case, they quickly are all binded, so they can't form hexamer. In the hight speed production, all the phenol are quickly binded to Dmpr and, next all the dmpr produced are recruited in order to form hexamers.
<p>
<p>
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We call &phi;<sub>in</sub> (in ml/h/cm<sup>3</sup>) the quantity of compounds (<= 0.2 &mu;m) filtered by a sponge of volume V (cm<sup>3</sup>). The compound is present at concentration C (mol.ml<sup>-1</sup>). Then the quantity of compound in contact with the bacteria, Q (mol.h<sup>-1</sup>), is:<br/>
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this model also correspond to the static influence map and flux balance map below representing all the influence and flux evolution of each rules on the others, and not far to the regulation network of the biological construction.
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<center>
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Q = &phi;<sub>in</sub>VC
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</center>
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</p>
</p>
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<div class="center">
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<h5> Parameters </h5>
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<table>
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+
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<p>
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-
<table border="1">
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<tr>
<tr>
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  <th>Name</th>
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    <td><img src="https://static.igem.org/mediawiki/2014/d/db/Flux.png" /></td>
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  <th>Value</th>
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<tr></td>  
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  <th>Unit</th>
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<td><img src="https://static.igem.org/mediawiki/2014/c/ce/InfluenceMap.png" /></td>
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  <th>Ref</th>
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</tr>
</tr>
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<tr>
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</table>
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    <td>&phi;<sub>in</sub></td>
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</div>
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    <td>[0.1-0.3]</td>
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    <td>cm<sup>3</sup>(water).cm<sup>-3</sup>(sponge).s<sup>-1</sup></td>
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    <td>WATER TRANSPORT, RESPIRATION AND ENERGETICS OF THREE TROPICAL MARINE SPONGES </td>
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</tr>
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<tr>
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    <td>V</td>
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    <td>[66.8-116]</td>
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    <td>cm<sup>3</sup></td>
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    <td>Filtering activity of Spongia officinalis var. adriatica (Schmidt) (Porifera, Demospongiae) on bacterioplankton: Implications for bioremediation of polluted seawater</td>
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-
</tr>
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-
</table>  
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-
</p>
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<h5> Results </h5>
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<h3>Phenol Complex Hexamer</h3>
 +
the second model is similar to the first one, but it acess that the hexamer is constituted of three Dmpr_dimer-Phenol complex as show in the figure below.
 +
<div class="center"><img src="https://static.igem.org/mediawiki/2014/8/88/Mod_ph_2.png" width="867px"></div>
 +
<br />
 +
<p>this model is traduced into Kappa with the code below where we define a new marker in order to follow the gfp production per second.</p>
 +
<pre>
 +
#agents
 +
#dmpr is in dimer conformation  : s : binding between dimers, x: binding phenol
 +
%agent: dmpr(s,x)
 +
%agent: phenol(x)
 +
%agent: phComplex2(s)
 +
%agent: hexamere(b)
 +
%agent: gfp(b)
 +
%agent: pgfp(b)
-
<p>
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%token: marker
-
We present in <b>Figure1</b> the results obtained for different parameter sets (min, mean and max values). The value of Q increases linearly with the concentration and is in the range of the &mu;mol.
+
#rule
 +
'dmpr_ph_bind' dmpr(x),phenol(x) <-> dmpr(x!1),phenol(x!1) @ 0.3, 0.5
 +
'dmpr_atpase' dmpr(x!1),phenol(x!1) -> phComplex2(s) @ 0.6
 +
'quadmere' phComplex2(s),phComplex2(s) <-> phComplex2(s!1),phComplex2(s!1) @ 0.3, 0.5
 +
'hexamere_formation' phComplex2(s!1),phComplex2(s!1),phComplex2(s) <-> hexamere(b) @ 0.3,0.5
 +
'gfp_bind' hexamere(b),pgfp(b) -> hexamere(b!1),pgfp(b!1) @ 0.3
 +
'gfp_prod' hexamere(b!1),pgfp(b!1) -> pgfp(b),gfp(b),dmpr(s,x),dmpr(s,x),dmpr(s,x),phenol(x),phenol(x),phenol(x)|1:marker @ 1
 +
'dmpr_prod' -> dmpr(s,x) @ 0.1
 +
'phenol_leek' phenol(x) -> @ 1E-4
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<div class="center">
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#var
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<div class="thumb tnone">
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%var: 'dmpr_qt' 500
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  <div class="thumbinner" style="width:502px;">
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%var: 'phenol_qt' 5000
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  <a href="https://static.igem.org/mediawiki/2014/c/ce/Simple_model_concs.png" class="image">
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%var: 'gfp_p_qt' 500
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    <img alt="IMAGE" src="https://static.igem.org/mediawiki/2014/c/ce/Simple_model_concs.png" width="500px;" class="thumbimage"/>
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%init: 'phenol_qt' phenol()
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  </a>
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%init: 'gfp_p_qt' pgfp()
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  <div class="thumbcaption">
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%init: 'dmpr_qt' dmpr()
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    <div class="magnify">
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%init:marker <- 0
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    <a href="https://static.igem.org/mediawiki/2014/c/ce/Simple_model_concs.png" class="internal" title="Enlarge">
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      <img src="/wiki/skins/common/images/magnify-clip.png" width="15" height="11" alt="Symbol"/>
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#def
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    </a>
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%def: "dumpInfluenceMap" "yes"
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    </div>
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%def: "influenceMapFileName" "influenceMap"
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    <center>Figure 1: Quantity of compound in contact with the bacteria inside the sponge (Q) as a function of the external compound concentration. Three different parameter sets used: red minimal parameter values; gree: mean parameter values; blue: max parameter values.</center>
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%def: "dotSnapshots" "yes"
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  </div>
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%mod: [true] do $FLUX "flux.dot" [true]
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  </div>
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%mod: repeat [E+]/[E]>0 do $SNAPSHOT until [true]
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</div>
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</div>
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</p>
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 +
#obs
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%obs: 'gfp_produced' |marker|/[E+]
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%obs: 'gfp_p' gfp(b)
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%obs: 'phenol_k' phenol(x)
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%obs: 'unbinded_ph_complex' phComplex2(s)
 +
</pre>
 +
<br />
<p>
<p>
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The equations for the three lines in <b>Figure1</b> are the following:
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We also add a leakage of the phenol in order to corelate the biologicals results.
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With thos modification we obtain the following curves.
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<ul>
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<li>Red: y = 0.00668x
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<li>Green: y = 0.01828x
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<li>Blue: y = 0.0348x
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-
</ul>
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</p>
</p>
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<br />
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<h2>Model 2: 2D diffusion</h2>
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<b><u>References :</u></b>
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<p>
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For this second model we want to take into account the geometry of the sponge. For the equations and geometry to be tractable we will consider a 2D slice of a sponge. Our assumptions are the following :
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<ol>
<ol>
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<li>Bacteria are uniformely distributed in the sponge (as for model 1)</li>
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<li><b>TRANSCRIPTIONAL REGULATORS FOR AROMATIC DEGRADATION</b>, David Tropel and Jan Roelof van der Meer, <i>Microbiol. Mol. Biol.</i> Rev. 2004</li>
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<li>Sponge geometry is approximated as a sphere</li>
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<li><b>Biodegradation of phenol</b>, C. Indu Nair, K. Jayachandran and Shankar Shashidhar, <i>African Journal of Biotechnology Vol. 7</i>, pp. 4951-4958, 29 December, 2008</li>
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<li>The interior of the sponge is a uniform medium in which the compound diffuse isotropically with a coefficient D</li>
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<li><b>Bacterial promoters triggering biodegradation of aromatic pollutants</b>,Eduardo Díaz* and María A Prieto, <i>Current Opinion in Biotechnology</i> 2000,</li>
 +
<li><b>Genetics and biochemistry of phenol degradation by Pseudomonas sp. CF600</b>, Justin Powlowski & Victoria Shingler, <i>Biodegradation v5</i> 1994</li>
 +
<li><b>Role of the DmpR-Mediated Regulatory Circuit in Bacterial Biodegradation Properties in Methylphenol-Amended Soils</b>, Inga Sarand, Eleonore Skärfstad, Mats Forsman, Martin Romantschuk and Victoria Shingler, <i>Appl. Environ. Microbiol</i>, 2001</li>
 +
<li><b>An Effective Strategy for a Whole-Cell Biosensor Based on Putative Effector Interaction Site of the Regulatory DmpR Protein</b>, Saurabh Gupta, Mritunjay Saxena , Neeru Saini , Mahmooduzzafar , Rita Kumar, Anil Kumar, <i>PLoS ONE 7(8): e43527</i> 2012</li>
 +
<li><b>Sensing of aromatic compounds by the DmpR transcriptional activator of phenol-catabolizing Pseudomonas sp. strain CF600</b>, V Shingler and T Moore, <i>J. Bacteriol.</i> 1994</li>
</ol>
</ol>
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</p>
 
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<p>
 
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We emphasize that assumption 2 may not be a pure mathematician's idealization, some <i>spongia officinalis</i> sponges have approximately spherical shapes as presented in Figure2a (Although others do not, a wide variety of shapes depending on the environment are exhibited). Some articles also represent some species of sponges as ellipsoidal, see Figure2 b (TODO cite).
 
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<div class="center">
 
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<div class="thumb tnone">
 
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  <div class="thumbinner" style="width:502px;">
 
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  <a href="https://static.igem.org/mediawiki/2014/4/4a/Ellipse_sponges.png" class="image">
 
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    <img alt="IMAGE" src="https://static.igem.org/mediawiki/2014/4/4a/Ellipse_sponges.png" width="500px;" class="thumbimage"/>
 
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  </a>
 
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  <div class="thumbcaption">
 
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    <div class="magnify">
 
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    <a href="https://static.igem.org/mediawiki/2014/4/4a/Ellipse_sponges.png" class="internal" title="Enlarge">
 
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      <img src="/wiki/skins/common/images/magnify-clip.png" width="15" height="11" alt="Symbol"/>
 
-
    </a>
 
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    </div>
 
-
    <center>Figure 2: Sponge morphology and functionning. a) Illustration of different sponge morphology of <i>S. officinalis</i>. b) Sponge morphology approximated as an ellipsoid and functionning of the sponge. Sources: a)Corriero et al., aquaculture (2004); b) Webster, Environmental Microbiology (2007)</center>
 
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  </div>
 
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  </div>
 
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</div>
 
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</div>
 
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</p>
 
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<h5> Geometry </h5>
 
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For the simplicity of the simulations, we represent only a slice of a spherical sponge, the slice going through its center. We thus have the following geometry presented in Figure3.
 
-
 
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<div class="center">
 
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<div class="thumb tnone">
 
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  <div class="thumbinner" style="width:502px;">
 
-
  <a href="https://static.igem.org/mediawiki/2014/4/4a/Ellipse_sponges.png" class="image">
 
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    <img alt="IMAGE" src="https://static.igem.org/mediawiki/2014/4/4a/Ellipse_sponges.png" width="500px;" class="thumbimage"/>
 
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  </a>
 
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  <div class="thumbcaption">
 
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    <div class="magnify">
 
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    <a href="https://static.igem.org/mediawiki/2014/4/4a/Ellipse_sponges.png" class="internal" title="Enlarge">
 
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      <img src="/wiki/skins/common/images/magnify-clip.png" width="15" height="11" alt="Symbol"/>
 
-
    </a>
 
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    </div>
 
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    <center>Figure 2: Sponge morphology and functionning. a) Illustration of different sponge morphology of <i>S. officinalis</i>. b) Sponge morphology approximated as an ellipsoid and functionning of the sponge. Sources: a)Corriero et al., aquaculture (2004); b) Webster, Environmental Microbiology (2007)</center>
 
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  </div>
 
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  </div>
 
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</div>
 
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</div>
 
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<h5> Model Formulation </h5>
 
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<h5> Parameters </h5>
 
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<h5> Results </h5>
 
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</div>
 
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</div>
 
</div>
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{{:Team:Evry/Template:HomeFooter}}
{{:Team:Evry/Template:HomeFooter}}

Latest revision as of 03:27, 18 October 2014

IGEM Evry 2014

Phenol Model

Phenol sensor model


Introduction

In this section we model the phenol sensor in Kappa. Kappa is a rule based language allowing the expression of protein-protein interactions in order to build executable models of protein networks. This project is supported by the harvard medical school and can be found in the Kappa homepage. This model is then simulated using KaSim, an open source stochastic simulator for rule-based models written in Kappa. Basically, KaSim takes one or several Kappa files as input and generates stochastic trajectories of various observables. according to literature, the phenol sensor is composed of phosphoriled Dmpr dimers forming hexamers and binding on the P0 site as described in the schemas below from David Tropel and Jan Roelof van der Meer researches[1].


From this article and various other[2,3,4,5,6], we define a first model in Kappa.


Phenol-Dmpr model

In this first model, we define the hexamer needed for the phenol sensing to be composed of one Phenol complex and two none phosphoriled Dmpr dimers. We define the Phenol complex as a Dmpr dimer with a phenol binded and next phosphoriled with an ATP in order to binds other Dmpr. schematicaly it correspond to the schemas below.

First (1) two dmpr form a dimer, next, this dimer will recruit a phenol molecule (2), then this complex phosphorize in order to change its conformation (3) finally the complex recruit free Dmpr Dimers in order to form hexamers complexes (4). This hexamer will trigger the P0 site in order to activate the GFP production.


The resulting Kappa program describing all thosess steps is the following :

#agents
#dmpr considered as a dimer: s : binding between dimers, need to be phosphoriled, x: binding phenol
%agent: dmpr(s,x)
%agent: phenol(x)
%agent: phComplex2(s)
%agent: hexamere(b)
%agent: gfp(b)
%agent: pgfp(b)
#rule
'assos_ph' dmpr(s,x),phenol(x) <-> dmpr(s,x!1),phenol(x!1) @ 'ass','diss'
'recr_atp' dmpr(s,x!1),phenol(x!1) -> phComplex2(s) @ 'ph'
'assos_quadr' phComplex2(s),dmpr(s,x) <-> phComplex2(s!1),dmpr(s!1,x) @ 'ass','diss'
'assos_hexa' phComplex2(s!1),dmpr(s!1,x),dmpr(s,x) <-> hexamere(b) @ 'ass', 'diss'
'gfp_bind' hexamere(b),pgfp(b) -> hexamere(b!1),pgfp(b!1) @ 'ass'
'gfp_prod' hexamere(b!1),pgfp(b!1) -> pgfp(b),gfp(b),dmpr(s,x),dmpr(s,x),dmpr(s,x) @ 0.9
'dmpr_prod' -> dmpr(s,x) @ 10 # TO VARY !!

#var 
%var: 'gfp' 0
%var: 'dmpr_qt' 100
%var: 'phenol_qt' 1000
%var: 'gfp_p_qt' 100
%var: 'cell_v' 1E-15
%var: 'cell_av' 1E8
%var: 'ph' 5E-3
%var: 'ratio' 'cell_v'/(2.25E-12)
%var: 'ass' (1E-5)*(1/'ratio')
%var: 'diss' (1E-3)*(1/'ratio')
%init: 'phenol_qt' phenol()
%init: 'gfp_p_qt' pgfp()
%init: 'dmpr_qt' dmpr()
%mod: [Tsim] > 1800 do $STOP

#def
%def: "dumpInfluenceMap" "yes"
%def: "influenceMapFileName" "influenceMap"	
%def: "dotSnapshots" "yes"	
%mod: [true] do $FLUX "flux.dot" [true]
%mod: repeat [E+]/[E]>0 do $SNAPSHOT until [true]

#obs
%obs: 'gfp_produced' gfp(b)
%obs: 'phenol_k' phenol(x)

In this model, all the variables are defined according to biologicals values. For example, the cell_v correspond to E.coli volue and cell_av to the Avogadro number in E.coli. all the association and phosphorilation constants are calculated for general molecules in e.coli or extracted from articles when found. We choose to use e.coli for our model in order to correspond to the biological results


The first rule describe the phenol binding, the second describe the phosphorilation of the complex, the two next ones correspond to the formation of the hexamer complex. The next rule correspond to the binding of the hexamer to the gfp promoter. Finally the two last rules correspond to the gfp production and the dmpr production.


after running KaSim for differents values of Dmpr production, we obtain the two next graphics

The first one represent the gfp concentration over time for different Dmpr production. The second represent the the phenol concentration over time for five different Dmpr production (1E2, 1E1, 1E-1, 1E-2,1E-4,1E-5). those differents curves show us that in this model we reach a level where the gfp produced become null due to the fact that all Dmpr are binded with phenol and so can't form hexamer. Indeed the Phenol complexe need two FREE dmpr in order to create an hexamer. Simply speaking, all the Dmpr try to recruit a phenol and in the slow production case, they quickly are all binded, so they can't form hexamer. In the hight speed production, all the phenol are quickly binded to Dmpr and, next all the dmpr produced are recruited in order to form hexamers.

this model also correspond to the static influence map and flux balance map below representing all the influence and flux evolution of each rules on the others, and not far to the regulation network of the biological construction.

Phenol Complex Hexamer

the second model is similar to the first one, but it acess that the hexamer is constituted of three Dmpr_dimer-Phenol complex as show in the figure below.

this model is traduced into Kappa with the code below where we define a new marker in order to follow the gfp production per second.

#agents
#dmpr is in dimer conformation  : s : binding between dimers, x: binding phenol
%agent: dmpr(s,x)
%agent: phenol(x)
%agent: phComplex2(s)
%agent: hexamere(b)
%agent: gfp(b)
%agent: pgfp(b)

%token: marker
#rule
'dmpr_ph_bind' dmpr(x),phenol(x) <-> dmpr(x!1),phenol(x!1) @ 0.3, 0.5
'dmpr_atpase' dmpr(x!1),phenol(x!1) -> phComplex2(s) @ 0.6
'quadmere' phComplex2(s),phComplex2(s) <-> phComplex2(s!1),phComplex2(s!1) @ 0.3, 0.5
'hexamere_formation' phComplex2(s!1),phComplex2(s!1),phComplex2(s) <-> hexamere(b) @ 0.3,0.5
'gfp_bind' hexamere(b),pgfp(b) -> hexamere(b!1),pgfp(b!1) @ 0.3
'gfp_prod' hexamere(b!1),pgfp(b!1) -> pgfp(b),gfp(b),dmpr(s,x),dmpr(s,x),dmpr(s,x),phenol(x),phenol(x),phenol(x)|1:marker @ 1
'dmpr_prod' -> dmpr(s,x) @ 0.1
'phenol_leek' phenol(x) -> @ 1E-4

#var 
%var: 'dmpr_qt' 500
%var: 'phenol_qt' 5000
%var: 'gfp_p_qt' 500
%init: 'phenol_qt' phenol()
%init: 'gfp_p_qt' pgfp()
%init: 'dmpr_qt' dmpr()
%init:marker <- 0

#def
%def: "dumpInfluenceMap" "yes"
%def: "influenceMapFileName" "influenceMap"	
%def: "dotSnapshots" "yes"	
%mod: [true] do $FLUX "flux.dot" [true]
%mod: repeat [E+]/[E]>0 do $SNAPSHOT until [true]

#obs
%obs: 'gfp_produced' |marker|/[E+]
%obs: 'gfp_p' gfp(b)
%obs: 'phenol_k' phenol(x)
%obs: 'unbinded_ph_complex' phComplex2(s)

We also add a leakage of the phenol in order to corelate the biologicals results. With thos modification we obtain the following curves.


References :
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  3. Bacterial promoters triggering biodegradation of aromatic pollutants,Eduardo Díaz* and María A Prieto, Current Opinion in Biotechnology 2000,
  4. Genetics and biochemistry of phenol degradation by Pseudomonas sp. CF600, Justin Powlowski & Victoria Shingler, Biodegradation v5 1994
  5. Role of the DmpR-Mediated Regulatory Circuit in Bacterial Biodegradation Properties in Methylphenol-Amended Soils, Inga Sarand, Eleonore Skärfstad, Mats Forsman, Martin Romantschuk and Victoria Shingler, Appl. Environ. Microbiol, 2001
  6. An Effective Strategy for a Whole-Cell Biosensor Based on Putative Effector Interaction Site of the Regulatory DmpR Protein, Saurabh Gupta, Mritunjay Saxena , Neeru Saini , Mahmooduzzafar , Rita Kumar, Anil Kumar, PLoS ONE 7(8): e43527 2012
  7. Sensing of aromatic compounds by the DmpR transcriptional activator of phenol-catabolizing Pseudomonas sp. strain CF600, V Shingler and T Moore, J. Bacteriol. 1994