Team:IIT Madras/Dry lab/Modelling

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<h1 align="center"><b><u> In-Silico - Comparative Growth Analysis of Wild type vs PR Transformed cells </u></b></h1>
<h1 align="center"><b><u> In-Silico - Comparative Growth Analysis of Wild type vs PR Transformed cells </u></b></h1>
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Revision as of 22:56, 28 October 2011

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



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

Hypothesis

  1. Increase in growth rate due to Proteorhodopsin proton efflux in minimal carbon media
  2. Proton efflux generated by Proteorhodopsin increases ATP production

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.


Model Construction


A Systems Biology Markup Language (SBML) file was created for the E.Coli transformed with PR (model_PR) and Wildtype(model_WT). 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 (COBRA) methods using the COBRA toolbox. The COBRA Toolbox is a freely available Matlab toolbox that can be used to perform a variety of COBRA methods, including many FBA-based methods. 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 'optimizeCbModel' is used to perform FBA. Also, gene deletion analysis and their effect on growth rates can also be modeled using COBRA toolbox.

Protocol for metabolic modeling




Simulation Design for Validation


The validation was done with negative regulation of the cytochrome oxidase reaction by comparing with literature available for inhibition using azide[3]


Table 1: .




Table 2: Validation Simulation: For the same amount of inhibition of oxidative phosphorylation due to azide the growth rate increases in the presence of Proteorhodopsin. At the same time when the glucose concentration is minimal the % increase in growth rate due to Proteorhodopsin is higher.



Figure 1: Plot for % increase in growth due to Proteorhodopsin at varying glucose concentration in the absence of Azide.


According to the model the following reactions showed major flux changes due to Proteorhodopsin :

  1. 'adentylate kinase GTP '
  2. 'adenosine hydrolase'
  3. 'dihydroorotic acid menaquinone 8
  4. '3 isopropylmalate dehydrogenase'
  5. 'psicoselysine transport via proton symport periplasm
  6. 'purine nucleoside phosphorylase Deoxyadenosine '
  7. 'L threonine via sodium symport periplasm '
Reaction Knockout Analysis of '3 isopropylmalate dehydrogenase' was lethal and the other reactions didn’t have major effects on growth rates .



Figure 2: Plot for % increase in growth due to Proteorhodopsin at varying glucose concentration for 70% inhibition of Oxidative phosphorylation (ETC) on addition of azide.


According to the model the following reactions showed major flux changes due to Proteorhodopsin :

  1. 'adentylate kinase GTP '
  2. 'adenosine hydrolase'
  3. 'dihydroorotic acid menaquinone 8 '
  4. 'Glycolate oxidase' Needs to be done
  5. 'psicoselysine transport via proton symport periplasm '
  6. 'purine nucleoside phosphorylase Deoxyadenosine '
  7. 'L threonine via sodium symport periplasm '

Reaction Knockout Analysis of all the reactions didn’t have major effects on growth rates .



Figure 3: Plot for % increase in growth due to Proteorhodopsin at varying glucose concentration for complete inhibition of Oxidative phosphorylation (ETC) on addition of high concentration of azide.



According to the model the following reactions showed major flux changes due to Proteorhodopsin :

  1. adentylate kinase GTP
  2. adenosine hydrolase
  3. dihydroorotic acid menaquinone 8
  4. 3'isopropylmalatedehydrogenase'
  5. psicoselysine transport via proton symportperiplasm
  6. purine nucleoside phosphorylaseDeoxyadenosine
  7. L threonine via sodium symportperiplasm
Reaction Knockout Analysis of '3 isopropylmalate dehydrogenase' was lethal and the other reactions didn’t have major effects on growth rates .



Reference

  1. "A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information" Adam M Feist[1], Christopher S Henry[2], Jennifer L Reed[1], Markus Krummenacker[3], Andrew R Joyce[1], Peter D Karp[3],Linda J Broadbelt[2], Vassily Hatzimanikatis[4] and Bernhard Ø Palsson[1],*
  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*§
  3. "Light-powering Escherichia coli with proteorhodopsin" Jessica M. Walter*†, Derek Greenfield*‡, Carlos Bustamante*†‡§¶_, and Jan Liphardt*†‡**