Team:IIT Madras/Dry lab/Modelling

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<h1 align="right"><b><u> In-Silico - Comparative Growth Analysis of Wild type vs PR Transformed cells </u></b></h1>
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<br/><br/><br/><br/><br/>
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<h3 align="right"> (Metabolic Modeling using Constraint Based Reconstruction and Analysis)</h3>
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<img src="https://static.igem.org/mediawiki/2011/b/b1/2162309526_9869d37c77.jpg" width="70" height="70"ALIGN="LEFT"/>
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<p><img src="https://static.igem.org/mediawiki/2011/9/94/KRaman.jpg" align="middle" width="600" height="635" align="center" style="float:left;"/>
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<h3><b><u>Abstract</u></b></h3>
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<h2 style="text-align:center;"><font color="#008000"><b><u>GREEN LANTERN</u></b></font></h2>     
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<p style="float: left;">
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<font face="calibri" color="#080000"> One of the challenging task was to establish the lighting setup which would power the proteorhodopsin  in presence of retinal to carry its H+ pumping activity in the carbon deficient condition. On rigorous searching  we came across a research paper (reference and relevant extract mentioned below) which clearly defined the wavelenght and intensity to maintain the proton motive force (pmf).<br/>
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<h3><font face="arial" color="#980000 "><b><u>Source</u></b></font></h3>
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<b>Light-powering Escherichia coli with proteorhodopsin  Jessica M. Walter, Derek Greenfield, Carlos Bustamante, and Jan Liphardt . Contributed by Carlos Bustamante, December 13, 2006 </b><br/><br/>
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Prior to this paper the role of light in powering cells containing proteorhodopsin and participation in ocean energy fluxes remained largely unclear. This paper makes an attempt to show that when cellular respiration is inhibited by depleting oxygen or by the respiratory poison azide, Escherichia coli cells expressing PR become light-powered.
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The paper clearly highlighted the following main points:-<br/>
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<ol>
<ol>
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<li>The rotation rate was clearly stimulated even at the lowest light intensity studied (<font color="#00CC33"><b>5 mW/cm2</b></font>).</li>
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An extensive Reconstruction and Flux Balance Analysis study of metabolic pathways in E.coli at the genome scale, considering 1668 metabolites and 2383 reactions and their respective stoichiometry matrices was carried out using a Constraint Based approach. This model was validated with negative regulation of reactions by comparing with literature available for Oxidative Phosphorylation inhibitors. By including variations in substrate (glucose) concentrations under limiting conditions, we analyzed the global effects of Proteorhodopsin a.k.a. PR, (light-dependent proton pump) activity on the host system. Such a model which analyzes global effects on metabolic pathways is a novel addition to pre-existing kinetic models (at the protein level) of PR action.
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<li>The rate increased rapidly with intensity up to <font color="#00CC33"><b>10 mW/cm2</b></font> (<font color="#00CC33"><b>15 mM</b></font> azide) or <font color="#00CC33"><b>20 mW/cm2</b></font> (<font color="#00CC33"><b>60 mM</b></font> azide) , where      the effect saturated.</li>
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</ol><br/>
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<li>At <font color="#00CC33"><b>50 mW/cm2</b></font>, there was no detectable benefit of increased illumination. <b>(fig.2)</b></li>
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<h3><b><u>Hypothesis</u></b></h3>
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<li>The maximum potential PR can generate by using the free energy from photon absorption (Vpr) is similar to the potential generated by E. Coli respiration. Only when the pmf falls below the maximum potential (Vpr) during respiratory stress does PR begin to pump, and the proton flux through PR increases as the pmf falls. PR is able to maintain E. Coli cellular pmf near this maximum potential (<font color="#00CC33"><b>Vpr = -0.2V</b></font>) with sufficiently bright illumination ( <font color="#00CC33"><b>60mW/cm2</b></font>).</li>
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<ol>
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Increase in cell growth rate due to the proton efflux generated by Proteorhodopsin in minimal carbon media.
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</ol> <br/>
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<h3><b><u>Model Design</u></b></h3>
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Reconstruction and Mathematical Modeling of E.coli K12-MG1655 pathway with Proteorhodopsin.
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Literature data:<br/>
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<ol>  
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<li>Genome scale metabolic model thermodynamic data for genome scale <b>E.coli K-12 MG1655</b> was derived. This was done by alignment with genomic annotation and the metabolic content of EcoCyc, characterization and quantification of biomass components and maintenance requirements of cell required for growth of the cell and thermodynamic data for reactions <sup>[1]</sup>.</li> <br/>
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<li>Reconstruction of the pathway was carried out to suit our project, hence involving the effects due to Proteorhodpsin pumping activity. Data for pH gradient <sup>[2]</sup>, the delta [H+] <sup>[3]</sup> was taken from literature and hence flux was calculated to formulate a comprehensive model.</li> <br/>
</ol>
</ol>
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<h3><font face="arial" color="#980000 "><b><u>Instrumentation</u></b></font></h3>
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<br/>
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Power density values for <font color="#00CC33"><b>green light</b></font> refer to the power density passed by a D540/25ϫ filter originating at a 175 W Xenon bulb .The sample chamber  was periodically illuminated with bright green light (160mW/cm2) coinciding with the maximum of PR’s absorption spectrum, 525 nm. At 525 nm the sample was observed to show maximum absorption (fig.1).<br/>
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<align="center"><i><b>(Click on the links below for more details on the methods and simulations)</b></i></align>
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Based on the above fact we decided to use green LED of dominant wavelenght of 525 nm.<br/>
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<div id="protocol1" style="cursor:pointer;"><h3><b><u>Model Construction</u></b></h3></div>
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<div id="protocol1_content" style="display:none;" >
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A <b>Systems Biology Markup Language (SBML)</b> file was created for the E.Coli transformed with PR (<b>model_PR</b>) and Wildtype(<b>model_WT</b>). The flux balance studies were done by constraint based reconstruction and analysis FBA computations, which fall into the category of constraint-based reconstruction and analysis <b>(COBRA)</b> methods using the COBRA toolbox. The <b>COBRA Toolbox</b> is a freely available <b>Matlab toolbox</b> that can be used to perform a variety of COBRA methods, including many FBA-based methods.
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In Matlab, the models are structures with fields, such as 'rxns' (a list of all reaction names), 'mets' (a list of all metabolite names) and 'S' (the stoichiometric matrix). The function '<b>optimizeCbModel</b>' is used to perform FBA. Also, gene deletion analysis and their effect on growth rates can also be modeled using COBRA toolbox.
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<h3><font face="arial" color="#980000 "><b><u>Diagrams</u></b></font></h3>
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</div><div id="protocol2" style="cursor:pointer;"><h3><b><u>Protocol for Metabolic Modeling</u></b></h3></div>
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<img src="https://static.igem.org/mediawiki/2011/8/87/H11.png" width="340" height="250"/>
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<img src="https://static.igem.org/mediawiki/2011/4/4f/H12.png" width="340" height="250"/>
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<p>Genome scale models provide us with an ideal platform to study effects of addition or deletion of genes in the system. We were especially interested in quantifying variations in flux rates of various reactions due to the H+ pumping activity of proteorhodopsin. For which we composed a network for the pathway in Systems Biology Markup Language. This was further validated and analysed using COBRA toolbox in MATLAB. This helped us to compare growth rates for wild type model (Model_WT) and Mutant Model with Proteorhodopsin (Model_PR)</p>
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<img src="https://static.igem.org/mediawiki/2011/9/9f/H13.png" width="340" height="250"/>
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<p><img src="https://static.igem.org/mediawiki/2011/4/4e/Modelling1.jpg" align="middle" width="500" height="400" align="center"/></p></div><br/>
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<img src="https://static.igem.org/mediawiki/2011/c/cf/H14.png" width="340" height="250"/><br/>
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<font size=2><u><b>fig(1)</b> & <b>fig(2)</b> Copyright@Light-powering Escherichia coli with proteorhodopsin  Jessica M. Walter, Derek Greenfield, Carlos Bustamante, and Jan Liphardt. Contributed by Carlos Bustamante, December 13, 2006 </u></font><br/>
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<img src="https://static.igem.org/mediawiki/2011/6/65/Download.jpg" width="36px" height="36px"/>  
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<h3><font face="arial" color="#980000 "><b><u>Specifications</u></b></font></h3>
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<a href="https://static.igem.org/mediawiki/2011/0/07/Ec_iAF1260_flux1_PR_IIT_Madras.zip"> Click here to download SBML file for the genome scale e.coli (K-12 MG1665) model including Proteorhodopsin</a> <br/> <br/>
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<h3><b><u> <a href="https://2011.igem.org/Team:IIT_Madras/Dry_lab/Modelling/Validation">Validation of Model</a></u></b></h3><br/>
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<h3><b><u> <a href="https://2011.igem.org/Team:IIT_Madras/Dry_lab/Modelling/Simulations">Simulations for Proof of Concept</a></u></b></h3><br/> <br/>
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<p><b><u> References </u></b></p>
<ol>
<ol>
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<li><font color="#00CC33"><b>LM78M05</b></font> – 3 terminal positive voltage regulator</li>
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<li><b>"A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information"
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<li>LED of wavelength -<font color="#00CC33"><b>525 nm</b></font>( colour- parrot green)</li>
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Adam M Feist, Christopher S Henry, Jennifer L Reed, Markus Krummenacker, Andrew R Joyce, Peter D Karp,Linda J Broadbelt, Vassily Hatzimanikatis and Bernhard Ø Palsson,Molecular Systems Biology-2007</li>
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<li><font color="#00CC33"><b>9 V</b></font> battery</li>
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<li>"Proteorhodopsin photosystem gene expression enables photophosphorylation in a heterologous host"
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<li>100 ohms/51 ohms/ 33 ohms/ 18 ohms resistor</li>
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A. Martinez, A. S. Bradley†, J. R. Waldbauer, R. E. Summons and E. F. DeLong,PNAS-2007</li>
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</ol>
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<li>"Light-powering Escherichia coli with proteorhodopsin"
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<h3><font face="arial" color="#980000 "><b><u>LED:</u></b></font></h3>
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Jessica M. Walter, Derek Greenfield, Carlos Bustamante and Jan Liphardt,PNAS-2007</li></b></p>
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<p>OVLFx3C7 Series (green): <font color="#00CC33"><b>OVLFG3C7</b></font><b>(fig.3)</b><br/></p>
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<ol>
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<li>Characteristics<br/>
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<ul>
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<li>High brightness with well-defined spatial radiation patterns</li>
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<li>UV -resistant epoxy lens</li>
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<li>Round Through-Hole LED Lamp(5 mm)</li>
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</ul>
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<li>Material - <font color="#00CC33"><b>InGaN</b></font></li>
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<li>Typical Intensity - <font color="#00CC33"><b>5200 mcd (millicandela)</b></font></li>
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<li>Lens colour - <font color="#00CC33"><b>water clear</b></font></li>
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<li>Operating Voltage - <font color="#00CC33"><b>(-40 to 85) degree C</b></font></li>
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<li>Continous forward current - <font color="#00CC33"><b>20mA</b></font></li>
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<li>Peak Wavelength - <font color="#00CC33"><b>521 nm</b></font></li>
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<li>Dominant Wavelength - <font color="#00CC33"><b>525 nm</b></font></li>
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<li>Spectra Half-Width - <font color="#00CC33"><b>25 nm</b></font></li><br/>
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<h3><font face="calibri" color="#980000 "><b><u>Circuit Diagram of the Setup</u></b></font></h3><br/>
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<img src="https://static.igem.org/mediawiki/2011/1/11/H15.png" width="300" height="200"/>
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<h3><font face="calibri" color="#980000 "><b><u>Observation</u></b></font></h3><br/>
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<table border="1">
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<tr>
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<td></td>
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<td><b>Reading 1</b></td>
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<td><b>Reading 2</b></td>
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<td><b>Reading 3</b></td>
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<td><b>Reading 4</b></td>
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<tr>
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<td><b>Vss (V)</b></td>
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<td> 5.56</td>
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<td> 5.56</td>
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<td> 5.56</td>
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<td> 5.56</td>
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<tr>
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<td><b>Vr(V)</b></td>
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<td> 0.5</td>
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<td> 1.0</td>
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<td> 0.33</td>
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<td> 0.66</td>
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</tr>
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<tr>
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<td><b>V led(V)</b></td>
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<td> 5.06</td>
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<td> 4.56</td>
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<td> 5.23</td>
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<td> 4.9</td>
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</tr>
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<tr>
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<td><b>I led(mA)</b></td>
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<td> 29.7</td>
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<td> 26.8</td>
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<td> 30.7</td>
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<td> 28.8</td>
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</tr>
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</table><br/>
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<h3><font face="calibri" color="#980000 "><b><u>Calculation</u></b></font></h3><br/>
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<ul>
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<li>LED equivalent efficiency - <font color="#00CC33"><b>40 lumen/W</b></font></li>
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<li>Luminous Intensity - <font color="#00CC33"><b>5.2 cd = 65.52 lumens</b></font></li>
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<li>Power given (excluding heat) by 1 LED - <font color="#00CC33"><b>1.638 W</b></font></li>
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<li>Power by 140 LEDs -<font color="#00CC33"><b>229.32 W</b></font></li>
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<li>Luminous Intensity -<font color="#00CC33"><b>81.105359 mW/cm2</b></font></li>
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<li>Internal Resistance of LED - <font color="#00CC33"><b>3.4 V/ 20 mA = 170 ohms</b></font></li>
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</ul><br/>
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Assuming average current across the LED to be 30 mA. This gives an Luminous Efficiency to be 135% <b>(fig.4)</b>.
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So the luminous Intensity of the entire lighting setup = <font color="#00CC33"><b>109.4922 mW/cm2</b></font>
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The 140 LEDs were fixed in 3 breadboards . The luminous intensity calculated above is a cumulative effect of all The LED's. So depending upon how many breadboards we use, we can choose to establish a luminous intensity of  <font color="#00CC33"><b>37 mW/cm2, 74 mW/cm2</b></font> and <font color="#00CC33"><b>109.4922 mW/cm2</b>.</font>
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The distance of the lighting source from the flask containing Proteorhodopsin and Retinal was strictly maintained at <font color="#00CC33"><b>15cm</b></font>.<br/>
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Latest revision as of 03:42, 29 October 2011

bar iGEM 2011 - Home Page Indian Institute of Technology - Madras



In-Silico - Comparative Growth Analysis of Wild type vs PR Transformed cells

(Metabolic Modeling using Constraint Based Reconstruction and Analysis)

Abstract

    An extensive Reconstruction and Flux Balance Analysis study of metabolic pathways in E.coli at the genome scale, considering 1668 metabolites and 2383 reactions and their respective stoichiometry matrices was carried out using a Constraint Based approach. This model was validated with negative regulation of reactions by comparing with literature available for Oxidative Phosphorylation inhibitors. By including variations in substrate (glucose) concentrations under limiting conditions, we analyzed the global effects of Proteorhodopsin a.k.a. PR, (light-dependent proton pump) activity on the host system. Such a model which analyzes global effects on metabolic pathways is a novel addition to pre-existing kinetic models (at the protein level) of PR action.

Hypothesis

    Increase in cell growth rate due to the proton efflux generated by Proteorhodopsin in minimal carbon media.

Model Design

Reconstruction and Mathematical Modeling of E.coli K12-MG1655 pathway with Proteorhodopsin. Literature data:
  1. Genome scale metabolic model thermodynamic data for genome scale E.coli K-12 MG1655 was derived. This was done by alignment with genomic annotation and the metabolic content of EcoCyc, characterization and quantification of biomass components and maintenance requirements of cell required for growth of the cell and thermodynamic data for reactions [1].

  2. Reconstruction of the pathway was carried out to suit our project, hence involving the effects due to Proteorhodpsin pumping activity. Data for pH gradient [2], the delta [H+] [3] was taken from literature and hence flux was calculated to formulate a comprehensive model.


(Click on the links below for more details on the methods and simulations)

Model Construction

Protocol for Metabolic Modeling


Click here to download SBML file for the genome scale e.coli (K-12 MG1665) model including Proteorhodopsin

Validation of Model


Simulations for Proof of Concept



References

  1. "A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information" Adam M Feist, Christopher S Henry, Jennifer L Reed, Markus Krummenacker, Andrew R Joyce, Peter D Karp,Linda J Broadbelt, Vassily Hatzimanikatis and Bernhard Ø Palsson,Molecular Systems Biology-2007
  2. "Proteorhodopsin photosystem gene expression enables photophosphorylation in a heterologous host" A. Martinez, A. S. Bradley†, J. R. Waldbauer, R. E. Summons and E. F. DeLong,PNAS-2007
  3. "Light-powering Escherichia coli with proteorhodopsin" Jessica M. Walter, Derek Greenfield, Carlos Bustamante and Jan Liphardt,PNAS-2007