Team:Grenoble/Projet/Results

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<h1>Results</h1>
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<h2>JB</h2>
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<h2>Post-transcriptional regulation<h2>
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<p>The RsmA system has a homologous in Escherichia Coli named CsrA. We know these two system are extremely closed on
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structural and functional sides. The most difference between this two regulation systems is on target regulon.
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For example, in Pseudomonas aeruginosa Rsma regulate many virulence genes as type III secretion system
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(anja Brencic and Stephen Lory). In Escherichia coli, RsmA homologous regulate metabolic network.
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Our goal is to integrate this translational regulation system in the toggle switch. We need to know whether it influence
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the bacteria’s life.</p>
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<a href="https://static.igem.org/mediawiki/2011/9/91/Growth_cur_final2.png"><img src="https://static.igem.org/mediawiki/2011/9/91/Growth_cur_final2.png"
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<div class="legend"><strong>Figure 1 RsmA influence on DH5α growth.</strong> Bacteria from overnight culture were reseeding in rich medium supplemented with tetracycline at 20mg/ml and also IPTG at the concentration given in the legend.</div>
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<p>Figure 1 present growth curve of DH5α carries into a plasmid pVLT31 with or without rsmA and Natural RBS cloned downstream
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the Plac promoter. Two triads can be seen. The triad containing the strains with empty plasmid shows an upper growth curve
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compared to the second triad carries pVLT31-rsmA. But it’s important to say that the two groups start their growth not at
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the same value. That explains a time lag between these two groups. After normalization of all curves, no differences
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between these two kind of bacteria could be seen. We conclude that the RsmA overexpression hasn’t effects on the growth of bacteria.</p>
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<h2>Characterisation of the leader sequences </h2>
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<p>We extracted and cloned several leader sequences that comport a ribosome-binding site (RBS). We first wanted to verify whether we could use them with a reporter gene,in order to:
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<li><a href="#lab">Characterise their RBS strength</a></li>
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<li><a href="#lab">Use them for further testtomeasure the effect of rsmAlaterrsmA +rsmY transcription. </a></li>
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</ul>
 
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</p>
 
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<p>We first tested the following constructions with a FACSCalibur flow cytometer:</p>
 
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<a href="https://static.igem.org/mediawiki/2011/f/f2/Figure_2_construction.png"><img
 
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height="150px" src="https://static.igem.org/mediawiki/2011/f/f2/Figure_2_construction.png"
 
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alt="figure2"/></a>
 
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<div classe"legend"><strong>Figure 2:</strong>
 
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:Constructions used to characterise the sequences RBS strength of magA and fha leader sequences.
 
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</div>
 
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<p>We used the brick BBa_K256003 as a reference to compare our leader sequences to. Fha1 and maga leader sequences are cloned to the same components as BBa_K256003, and carried by the same plasmid.
 
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We used two negative controls:
 
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</p>
 
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<ul>
 
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<li><a href="#lab">A brick similar to BBa_K5450010 that differs only by the absence of promoter</a></li>
 
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<li><a href="#lab">A cell culture containing no plasmid </a></li>
 
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</ul>
 
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<p>Cell were incubate one night at 37°C, 250rpm, during 14h.  They were re-suspended tree hours before the test. Optic density at 600 nm was 3± 0,3 for all samples 15mn before the test. 50 μl of LB containing cellsand medium were diluted into 500 μl of filtered in 0,2 nm PBS, and tube were loader into the FACS.</p>
 
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<a href="https://static.igem.org/mediawiki/2011/6/6f/Figure_3_facs.png"><img
 
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height="150px" src="https://static.igem.org/mediawiki/2011/6/6f/Figure_3_facs.png"
 
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alt="figure3"/></a>
 
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<div classe"legend"><strong>Figure 3:</strong>Dot plot of the water tube and one of the sample tubes. The measurements are done with 40000 events. FACS measurements were realised in duplicate (only one his shown).</div>
 
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<p>The cytometer count each particle that pass through the light beam; It is necessary to select an analysis gate that corresponds to the bacteria size.  We can then see what is the natural fluorescence of our control bacteria. This allows defining the windows of basal signal (M1) and positive signal (M2). We look at the average fluorescence within windows that comport the most cells.</p>
 
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<a href="https://static.igem.org/mediawiki/2011/2/27/Figure_4_flow_cytometre2.png"><img
 
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height="150px" src="https://static.igem.org/mediawiki/2011/2/27/Figure_4_flow_cytometre2.png"
 
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alt="figure4"/></a>
 
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<div classe"legend"><strong>Figure 4:</strong> Individuals flow cytometer results for each construction. In red are theGFP’s fluorescence averages, in black the rate of bacteriawithin the M1 or M2 window.</div>
 
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<a href="https://static.igem.org/mediawiki/2011/d/df/Figure_5_flow_courbe.png"><img
 
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height="150px" src="https://static.igem.org/mediawiki/2011/d/df/Figure_5_flow_courbe.png"
 
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alt="figure4"/></a>
 
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<div classe"legend"><strong>Figure 5:</strong> Compilation of the flow cytometer measurements.  1and 2 are the negative controls, 3 :magaleader sequence, 4: fha leader sequence, 5 : strongest biobrick RBS.</div>
 
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<p>The two negatives controls (WT and without promoter) show a very little fluorescent signal as expected. The reference brick BBa_K25003 shows an impressive amount of GFP with about 77 % of the cell population that fluoresce more than the control.Its average fluorescent signal is 1030 vs 2,5(wild type). The signal from the brick containing maga leader sequence (BBA_K545006) doesn’t differ very much from the negative controls: four per cent of the cell population fluoresce more than the controls, at a very low level even. The fluorescence mean is 4,4 vs 2,5 from the WT control (window M1). </p>
 
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<p>Part BBa_K545010 containing the fha leader sequence induce a fluorescence signal higher than the control for 90 % of the cell population.  The average signal (M2 window) is 154 vs 2,5(WT) and 1029 (BBa_K25003).</p>
 
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<a href="https://static.igem.org/mediawiki/2011/2/2c/Figure_6_rbs.png"><img
 
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height="150px" src="https://static.igem.org/mediawiki/2011/2/2c/Figure_6_rbs.png"
 
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alt="figure4"/></a>
 
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<div classe"legend"><strong>Figure 6:</strong> relative strength of maga(BBa_K545006) and fha(BBa_K545005) leader sequences RBS compared to BBa_K25003 RBS. The later is the strongest RBS used to compare others RBS of the registry. Maga has got very week RBS binding site strength,whereas fhaLS is still in the same order level.</div>
 
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<div class="left">
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    <h2>Construction of a Toggle Switch test</h2>
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<div  class="blocbackground" id="Achievements">
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    <p>
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    <h1>Achievements</h1>
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In order to test if our system could work, we construct a toggle switch test based on Gardner's work <a href="#1">[1]</a>.
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    </div>
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</p>
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<div  class="blocbackground" id="Content">
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<img src="https://static.igem.org/mediawiki/2011/1/1d/Toggle_GeoGeo%281%29.png" class="centerwide" style="box-shadow: none"/>
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<h2>Table of content</h2>
<p>
<p>
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For realized this toggle, we used 4 primary bricks :
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We describe here the modeling and experimental results obtained for each network module,  
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<ul>
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in order to validate our model and optimize the device: the toggle switch, the quorum sensing
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<li>pTet : <a href="http://partsregistry.org/Part:BBa_R0040">BBa_R0040</a></li>
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and the translational regulation system RsmA.
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<li>RBS-LacI-oo-pLac : <a href="http://partsregistry.org/Part:BBa_Q04121">BBa_Q04121</a></li>
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<ol>
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<li>RBS-GFP : <a href="http://partsregistry.org/Part:BBa_E0240">BBa_E0240</a></li>
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<li>Validation of the genetic network</li>
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<li>RBS-TetR composed of RBS <a href="http://partsregistry.org/Part:BBa_B0034">BBa_B0034</a> and TetR <a href="http://partsregistry.org/Part:BBa_C0040">BBa_C0040</a></li>
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<li>The essential role of rmsA regulation system</li>
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</ul>
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<li>Device optimization and specifications</li>
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</p>
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</ol>
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<p>
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In each part, we tried to speak about both experiment and modelling, in order to validate our model and optimized
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First we put RBS-GFP behind RBS-TetR, and Q04121 behind pTet: both size are around 1 500 bp. The following gel shows that both constructions
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by simulation the device.<br/><br/>
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were at the expected size. Construction were confirmed by sequencing.
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  <a href="https://2011.igem.org/Team:Grenoble/Projet/Results/Toggle" ><img class="icon" src="https://static.igem.org/mediawiki/2011/2/20/Bouton_toggle.png"/></a>
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</p>
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    <img src="https://static.igem.org/mediawiki/2011/f/fa/1etape.png" class="centerwide" style="box-shadow: none"/>
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      <big><big><a href="https://2011.igem.org/Team:Grenoble/Projet/Results/Toggle" class="menu">Validation of the network</a></big></big><br/>
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    <div class="legend">
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<strong>Figure 1:</strong>
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<a class="menu" href="https://2011.igem.org/Team:Grenoble/Projet/Results/Toggle#TS_QS">Toggle Switch and Quorum sensing behavior</a><br/>
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First step of cloning gel
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<a class="menu" href="https://2011.igem.org/Team:Grenoble/Projet/Results/Toggle#Validation">Validation of the model</a><br/>
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</div>
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<a class="menu" href="https://2011.igem.org/Team:Grenoble/Projet/Results/Toggle#Dynamic">Dynamic study of the stability</a><br/><br/><br/>
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    <p>
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In a last step of cloning, we put RBS-tetR-RBS-GFP behind pTet-Q04121. The size is around 3 000 bp. The following gel shows that
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  <a href="https://2011.igem.org/Team:Grenoble/Projet/Results/rmsA" ><img class="icon" src="https://static.igem.org/mediawiki/2011/9/97/Bouton_regulation.png"/></a>
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constructions was at the expected size. In addition to this test, transformation of bacteria have grown on plate with IPTG to block
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them in the fluorescence way. And some of the bacteria were fluorescent. Construction was also confirmed by sequencing.
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<big><big><a href="https://2011.igem.org/Team:Grenoble/Projet/Results/rmsA" class="menu">The essential role of rsmA regulation system</a></big></big><br/>
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<a class="menu" href="https://2011.igem.org/Team:Grenoble/Projet/Results/rmsA#Necessity">Why do we need this system ?</a><br/>
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<a class="menu" href="https://2011.igem.org/Team:Grenoble/Projet/Results/rmsA#fha">Leader sequence characterization</a><br/>
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<a class="menu" href="https://2011.igem.org/Team:Grenoble/Projet/Results/rmsA#rsma">rsmA characterization</a><br/><br/><br/>
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  <a href="https://2011.igem.org/Team:Grenoble/Projet/Results/Device" ><img class="icon" src="https://static.igem.org/mediawiki/2011/7/70/Bouton_general_modeling.png"/></a>
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<big><big><a href="https://2011.igem.org/Team:Grenoble/Projet/Results/Device" class="menu">Device specificities and optimization</a></big></big><br/>
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<a class="menu" href="https://2011.igem.org/Team:Grenoble/Projet/Results/Device#Optimization">Optimization of the device</a><br/>
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<a class="menu" href="https://2011.igem.org/Team:Grenoble/Projet/Results/Device#Limit">Determination of the limit of quantification</a><br/>
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<a class="menu" href="https://2011.igem.org/Team:Grenoble/Projet/Results/Device#Statistic">Stochastic study for statistic determination of several device specificities</a><br/><br/><br/>
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  <a href="https://2011.igem.org/Team:Grenoble/Projet/Results/Sensitivity" ><img class="icon" src="https://static.igem.org/mediawiki/2011/f/fd/Bouton_model_sensitivity.png"/></a>
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<big><big><a href="https://2011.igem.org/Team:Grenoble/Projet/Results/Sensitivity" class="menu">Sensitivity to parameters study</a></big></big><br/>
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<a class="menu" href="https://2011.igem.org/Team:Grenoble/Projet/Results/Sensitivity#Robustness">Robustness of our system</a><br/>
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<a class="menu" href="https://2011.igem.org/Team:Grenoble/Projet/Results/Sensitivity#Mercury">Applicable to mercury</a><br/><br/><br/><br/>
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    </p>
    </p>
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<table class="noborudre">
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<tr>
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<td><img src="https://static.igem.org/mediawiki/2011/c/c5/Toggletest.png" class="centerwide" style="box-shadow: none"/>
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</div>
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<div class="legend">
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<strong>Figure 2:</strong>
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Last step of cloning gel
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</div></td>
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<td><img src="https://static.igem.org/mediawiki/2011/8/82/Image_2.jpg" class="centerwide" style="box-shadow: none"/>
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<div class="legend">
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<strong>Figure 3:</strong>
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Fluorescence test picture
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</div></td>
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</tr>
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</table>
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<p>
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To test this system, bacterias had grown in an aTc preculture to block them in a nonfluorescent way. This bacteria were put
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in a 96 wells plate with a two dimensionnal gradient (aTc and IPTG). After 10 hours of acquisition, we obtained the following curve :
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</p>
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<img src="https://static.igem.org/mediawiki/2011/2/2d/Fusion.png" class="centerwide" style="box-shadow: none"/>
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<p>
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We can get from this graph that between an aTc concentration of 50ng/mL and 150 ng/mL there is a switch after 3 hours of experiment.
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However, we can see more fluorescence than expected. In fact, the GFP protein has an half life of 10 hours and it was impossible to get
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no fluorescence.
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</p>
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<p>
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We also did an experiment on bacterias which had grown in an IPTG preculture. But we did'nt see a switch because IPTG block bacterias in
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the fluorescence way. Because of the half life of GFP, it was possible to detect a switch only with bacteria which had grown with aTc.
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</p>
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<p>
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To go further, it will be very interesting to put an LVA tag on GFP in order to control its degradation. In this case we will be abble to see
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the switch in both case and with more magnitude. We also construct this toggle with the quorum sensing gene to get the proof of concept. And the construction with mercury repressor.
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</p>
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</div>
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</div>
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    <optgroup label="Table of content">
     
     
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    <option value="#Content" selected="selected">Table of content</option>
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    <optgroup label="Deterministic Modelling" >
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    <optgroup label="Toggle Switch" >
                                  
                                  
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                                     <option value="/Deterministic#Our_EquationsTS" >Our equations - Toggle switch</option>
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                                     <option value="/Toggle#TS_QS" >Toggle Switch and Quorum Sensing</option>
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                                    <option value="/Toggle#Validation" >Validation of the model</option>
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                                    <option value="/Toggle#Dynamic" >Dynamic study of the stability</option>
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                                    <option value="/Deterministic#Our_EquationsQS" >Our equations - Quorum sensing</option>
 
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                                    <option value="/Deterministic#Our_algorithms" >Our algorithms</option>
 
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                                    <option value="/Deterministic#Isoclines">Nullclines and Hysteresis</option>
 
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                             <optgroup label="Stochastic Modelling">
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                             <optgroup label="Post-transcriptional regulation (RsmA)">
                                  
                                  
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                                     <option value="/Stochastic#Geof" selected="selected">Random Aspect of the system</option>
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                                     <option value="/rmsA#Necessity">Necessity of this system</option>
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                                    <option value="/rmsA#fha">Leader sequence characterization</option>
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                                    <option value="/rmsA#rsma">rsmA characterization</option>
                                  
                                  
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                                     <option value="/Stochastic#Gillespie_algorithm">Gillespie algorithm</option>
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                                    <option value="/Stochastic#Stats">Mean, standard deviation and stats</option>
 
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    <option value="/Parameters">Our parameters</option>
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    <option value="/Device#Optimization" >Optimization of the device</option>
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    <option value="/Device#Limit">Determination of the limit of quantification</option>
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    <option value="/Device#Statistic">Statistic study of device specificities</option>
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                            <optgroup label="Sensitivity to parameters">
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    <option value="/Sensitivity#Robustness">Robustness of the system</option>
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    <option value="/Sensitivity#Mercury">Applicable to mercury</option>
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                            <optgroup label="Results">
 
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    <option value="/Results#Validation">Validation of our Network</option>
 
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    <option value="/Results#Device">Device specificities</option>
 
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Latest revision as of 03:20, 29 October 2011

Grenoble 2011, Mercuro-Coli iGEM


Achievements

Table of content

We describe here the modeling and experimental results obtained for each network module, in order to validate our model and optimize the device: the toggle switch, the quorum sensing and the translational regulation system RsmA.

  1. Validation of the genetic network
  2. The essential role of rmsA regulation system
  3. Device optimization and specifications
In each part, we tried to speak about both experiment and modelling, in order to validate our model and optimized by simulation the device.

Validation of the network
Toggle Switch and Quorum sensing behavior
Validation of the model
Dynamic study of the stability


The essential role of rsmA regulation system
Why do we need this system ?
Leader sequence characterization
rsmA characterization


Device specificities and optimization
Optimization of the device
Determination of the limit of quantification
Stochastic study for statistic determination of several device specificities


Sensitivity to parameters study
Robustness of our system
Applicable to mercury



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