Team:KU Leuven/Project/Glucosemodel/MeS/Modelling-FBA

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   <h3 class="bg-green">Modelling on cellular level</h3>
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    <h3>Honeydew model</h3> </a>
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    <p>Design of the honeydew model</p>
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    <h3>E-β-Farnesene</h3>
 
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    <p>BanAphids produce EBF!</p>
 
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    <h3>Methyl Salicylate</h3> </a>
 
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    <p>BanAphids produce MeS!</p>
 
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     <h3>Flux Balance Analysis</h3> </a>
     <h3>Flux Balance Analysis</h3> </a>
     <p>You are here!</p>
     <p>You are here!</p>
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     <h3>Kinetic Parameters</h3>
     <h3>Kinetic Parameters</h3>
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     <p>BanAphids MeS production?</p>
     <p>BanAphids MeS production?</p>
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    <h3>qPCR</h3> </a>
 
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    <p>Wetlab data for the MeS model</p>
 
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   <h3 class="bg-oscillator">Methyl Salicylate Modelling - Flux Balance Analysis</h3>
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   <h3 class="bg-oscillator">Flux Balance Analysis on Methyl Salicylate</h3>
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We made two different models for our methyl salicylate part. This model describes our Flux Balance Analysis (FBA), the other model can be found <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling">here</a>.<br/>
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We needed to check whether the introduction of our MeS brick and the production of the components influences the overall BanAphid metabolism and/or growth rate. This modelling results will be checked against wetlab data, namely the growth curves we obtained while characterising our MeS biobricks.
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We ran the FBA using the <a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html" target="_blank">COBRA Toolbox</a> for MATLAB..</b>
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We also composed a Kinetic Parameter Model to estimate the average production rate of MeS. Approach and results can be found <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling">here</a>.<br/>
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   <h3 class="bg-oscillator">Matlab: COBRA Toolbox<h3>
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   <p align = "justify"> <b>COBRA</b> stands for <b>Constraint-Based Reconstruction and Analysis</b> (COBRA) approach. This approach provides a biochemically and genetically consistent framework for the generation of hypotheses and the testing of functions of microbial cells. Probably the most used analysis in COBRA is the <b>Flux Balance Analysis (FBA)</b> which will be discussed in more detail below. <br/> <br/>
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   <p align = "justify"> A FBA calculates possibilities for the flow of metabolites through a metabolic network while maximising a set objective, in our case the growth rate of an organism or the production of a biotechnologically important metabolite.
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<b>COBRA has been successfully applied to study the possible phenotypes that arise from a genome </b>(Covert, Schilling <i>et al.</i> 2001). COBRA consists of two fundamental steps. First, a GENRE (=GEnome-scale Network REconstruction) is formed, and second, the appropriate constraints are applied to form the corresponding GEMS (GEnome-scale Model <i>in Silico</i>). Cellular functions can be limited by different types of constraints in a biological system: e.g. physico-chemical constraints, topo-biological, environmental and regulatory constraints.
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We ran the FBA for methyl salicylate using the <a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html" target="_blank">COBRA Toolbox</a> for MATLAB.</b>
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<b>COBRA</b> stands for <b>Constraint-Based Reconstruction and Analysis</b> (COBRA) approach. It provides a biochemically and genetically consistent framework for the generation of hypotheses and the testing of functions of microbial cells. The <b>Flux Balance Analysis (FBA)</b> is probably the most used analysis within COBRA. <br/> <br/>
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<b>COBRA has been successfully applied to study the possible phenotypes that arise from a genome </b>(Covert, Schilling <i>et al.</i> 2001; Orth <i>et al.</i> 2010). COBRA consists of two fundamental steps.<br/>
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First, a GENRE (=GEnome-scale Network REconstruction) is formed, composed of the mathematical representation of all known metabolic reactions.<br/>
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Second, the appropriate constraints are applied to form the corresponding GEMS (GEnome-scale Model <i>in Silico</i>).  
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<p align = "justify">Two fundamental types of constraints exist: <b>balances and bounds</b> (Price, Reed <i>et al.</i> 2004). Balance constraints are associated with conserved quantities such as energy, mass etc. Bounds limit numerical ranges of individual variables and parameters such as concentrations, fluxes or kinetic constants. <b>At steady state, there is no accumulation or depletion of metabolites in a metabolic network, so the production rate of each metabolite in the network must equal its rate of consumption.</b> This balance of fluxes can be represented mathematically as S . v = 0, where v is a vector of fluxes through the metabolic network and S is the stoichiometric matrix containing the stoichiometry of all reactions in the network.
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<ol><li><b>Physico-chemical constraints</b> refer to reaction rates, enzyme turnover rates, diffusion rates etc. Moreover mass, energy and momentum must be conserved and biochemical reactions must result in a negative free-energy change to proceed in the forward direction.</li>
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<li><b>Topo-biological constraints</b> refer to the crowding of molecules in the cell. An example is the organisation of DNA in <i>Escherichia coli</i> by spatio-temporal patterns (Huang, Zhang <i>et al.</i> 2003). Therefore there are two competing needs that constrain the physical arrangement of DNA in the cell.</li>
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<li><b>Environmental constraints</b> are nutrient availability, pH, temperature, osmolarity and the availability of electron acceptors. They are time and condition dependent. Environmental constraints are of fundamental importance for the quantitative analysis of microorganisms. Defined media and environmental conditions are needed to integrate data into quantitative models that are both accurately descriptive and predictive.</li>
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<li><b>Regulatory constraints</b> differ from the others because they are self-imposed and are subject to evolutionary change. For this reason, these constraints may be referred to as regulatory restraints, in contrast to 'hard' physico-chemical constraints and time-dependent environmental constraints. On the basis of environmental conditions, regulatory constraints allow the cell to eliminate suboptimal phenotypic states and to confine itself to behaviours of increased fitness. Regulatory constraints are implemented by the cell in various ways, including the amount of gene products made (transcriptional and translational regulation) and their activity (enzyme regulation). </li></ol>
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  <p align = "justify">Two fundamental types of constraints exist: <b>balances and bounds</b> (Price, Reed <i>et al.</i> 2004). Balances are constraints that are associated with conserved quantities such as energy, mass etc. while bounds are constraints that limit numerical ranges of individual variables and parameters such as concentrations, fluxes or kinetic constants. <b>At steady state, there is no accumulation or depletion of metabolites in a metabolic network, so the production rate of each metabolite in the network must equal its rate of consumption.</b> This balance of fluxes can be represented mathematically as S . v = 0, where v is a vector of fluxes through the metabolic network and S is the stoichiometric matrix containing the stoichiometry of all reactions in the network.
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Both bound and balance constraints limit the functional states of reconstructed networks that are allowed. In mathematical terms, the range of allowable network states is described by a solution space which, in biology, represents the phenotypic potential of an organism. All allowable network states are contained in this solution space. (Covert and Palsson 2003; Price, Papin <i>et al.</i> 2003)
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Both bound and balance constraints limit the allowed functional states of reconstructed networks. Constraints can be very diverse in a biological system : physico-chemical constraints (reaction rates, enzyme turnover rates, diffusion rates etc.) , topo-biological (e.g. organisation of DNA in <i>Escherichia coli</i> by spatio-temporal patterns (Huang, Zhang <i>et al.</i> 2003)), environmental (nutrient availability, pH, temperature, osmolarity and the availability of electron acceptors and regulatory constraints).<br/>In mathematical terms, the constraints define a system of linear equations which will be solved by linear programming in FBA. This will result in a range of allowable network states, described by a solution space which, in biology, represents the phenotypic potential of an organism. All allowable network states are contained in this solution space. (Covert and Palsson 2003; Price, Papin <i>et al.</i> 2003)<br/>
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Thus, we can predict the growth rate potential of our BanAphids, defined by the constraints we impose, e.g. the growth medium, temperature, co-factor/precursor presence etc.</p>
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In recent years, many new <i>in silico</i> methods have been developed using the COBRA framework. Many methods can be used such as finding best or optimal states in the allowable range; investigating flux dependencies; studying all allowable states; altering possible phenotypes as a consequence of genetic variations; and defining and imposing further constraints (Covert, Schilling <i>et al.</i> 2001).
 
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<h5>Determination of optimal or best states</h5>
 
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This can be achieved through mathematical descriptions of desired network functions which takes the form of an objective function (Z). Z can express 3 different functions: the exploration of the phenotypical potential of the GENRE (Papin, Price <i>et al.</i> 2002, Schilling, Covert <i>et al.</i> 2002); the determination of likely physiological states by choosing the objective function as such (ATP production); the design of strains towards a specific engineering goal (improved production of a desired secreted product). The objective function Z can be either a linear or nonlinear function.<br/>
 
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The following should be noted: <i>in silico</i> modelling in biology differs from that in the physico-chemical sciences where a single and unique solution is sought. This means that there can be multiple network states or flux distributions with the same outcome (optimal value of the objective function). Therefore, the need for enumerating alternate optima arises. We performed flux variability analysis to understand the complete set of alternate optima.
 
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Flux variability analysis determines the full range of numerical values for each flux in the network, while still satisfying the given constraints and optimizing a particular objective (Mahadevan and Schilling 2003).
 
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This involves <b>the determination of the consequences of enzyme defects on functional states of GENREs</b>. The value of the flux is re-constrained through the affected reaction. This new value is used to recompute the optimal state for the enzyme defect. If the enzymatic function is reduced due to the defect, but the exact numbers are unknown, the flux can be sequentially changed through the reaction of interest and the objective function can be optimized at each step. Plotting the resulting optimal value versus the flux value through the reaction of interest creates a linear curve. Here the slope represents the reduced scientific cost, which can be used to determine the sensitivity of the Flux Balance Analysis (FBA) solution. <br/>
 
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The reduced cost is the derivative of the objective function with respect to an internal reaction with zero flux (this gives an indication on how much each particular reaction affects the objective). In COBRA this is given as vector n (FBAsolution.n). A similar measure is the shadow price (the name reflects the “cost aspect” in the equation). This is the derivative of the objective function with respect to the exchange flux of a metabolite i.e. how much will the addition of a metabolite increase or decrease the objective.  In COBRA this is given as vector m (FBA solution.m).
 
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   <h3 class="bg-oscillator">Results: what Did COBRA Do For us?</h3>
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We used an <I>E. coli</i> model from 2007 (iAF1260 by Feist, AM. <i>et al.</i>) in all the following COBRA toolbox analyses.<br/><br/>
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We used an <i>E. coli</i> model from 2007 (iAF1260 by Feist, AM. <i>et al.</i>) in all the following COBRA toolbox analyses.<br/><br/>
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<b>As a first step we tried to predict the growth under default conditions for this model</b>. This gave us the following results (after setting the biomass as objective function): for <i>E. coli</i> under default conditions a growth rate of 0.737 hr<sup>-1</sup> is predicted. The sensitivity of this solution is indicated by either the shadow price or the reduced cost. Shadow prices are the derivative of the objective function with respect to the exchange flux of a metabolite. They indicate how much the addition of that metabolite will increase or decrease the objective. Reduced costs are the derivatives of the objective function with respect to an internal reaction with 0 flux, indicating how much each particular reaction affects the objective.
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<b>As a first step we tried to predict the growth under default conditions for this model</b>. This gave us the following results (after setting the biomass as objective function): for <i>E. coli</i> under default conditions a growth rate of 0.74 hr<sup>-1</sup> is predicted. When performing the same calculations, but for LB medium conditions, a growth rate of 5.34 hr<sup>-1</sup> is predicted. This shows that <i>E. coli</i> benefit from the LB medium conditions.
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<b>In a next step we wanted to add the reactions which are necessary for our model, but lacking in the iAF1260 model.</b><br/>
<b>In a next step we wanted to add the reactions which are necessary for our model, but lacking in the iAF1260 model.</b><br/>
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We added 'pchA', 'chor[c] -> ichor[c]' for the isochorismate synthesis reaction, 'pchB', 'ichor[c] -> sali[c] + pyr[c]' for the salicylate synthesis reaction and 'BSMT1', 'sali[c] -> methylsalicylate' for the methyl salicylate synthesis reaction.<br/>
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We added 'pchA', 'chor[c] -> ichor[c]' for the isochorismate synthesis reaction, 'pchB', 'ichor[c] -> sali[c] + pyr[c]' for the salicylate synthesis reaction and 'BSMT1', 'sali[c] -> methylsalicylate' for the methyl salicylate synthesis reaction.<br/> We also added the exchange reaction for methylsalicylate ('Ex_methylsalicylate').
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When we performed the growth calculation analysis for this modified model and with the biomass set as objective function, we also observed a growth rate of 0.737 hr<i>-1</i> and thus can <b>conclude that no adverse effect on the <i>E. coli</i> growth rate was predicted by adding these reactions.</b>
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When we performed the growth calculation analysis for this modified model with the biomass set as objective function, we also observed a growth rate of 0.737 hr<i>-1</i> </b>
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   <p align = "justify"><b>Since our bacteria will be grown on LB medium, we changed the default medium settings towards those for LB medium.</b> This means that we set all the exchange reactions to zero except for the exchange reactions for metabolites present in the LB medium. The lower reaction bounds of the following reactions were all set to -1000 since this allows maximal uptake of the respective metabolites: <br/>
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   <p align = "justify"><b>Since our bacteria will be grown on LB medium, we changed the default medium settings towards those for LB medium.</b> This means that we changed the relevant exchange reactions for the metabolites present in LB medium as seen in <i>Tawornsamretkit et al.</i>. The lower reaction bounds of the relevant reactions were set as following:<br/>
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EX_glc(e), EX_phe_L(e), EX_cys_L(e), EX_ile_L(e), EX_ins(e), EX_hxan(e), EX_h2o(e), EX_o2(e), EX_co2(e), EX_nh4(e), EX_so4(e), EX_ca2(e), EX_h(e), EX_k(e), EX_mg2(e), EX_na1(e), EX_fe3(e), EX_nac(e), EX_thym(e), EX_ade(e), EX_thr_L(e), EX_val_L(e), EX_pro_L(e), EX_his_L(e), EX_leu_L(e), EX_ura(e), EX_tyr_L(e), EX_trp_L(e), EX_met_L(e), EX_ser_L(e), EX_arg_L(e), EX_asp_L(e), EX_lys_L(e), EX_lys_L(e), EX_ala_L(e), EX_zn2(e), EX_cd2(e), EX_glyc(e), EX_gln_L(e)
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model = changeRxnBounds (model,'EX_glc(e)',0,'l')<br/>
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model = changeRxnBounds (model,'EX_phe_L(e)',-0.1,'l')<br/>
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model = changeRxnBounds (model,'EX_cys_L(e)',0,'l')<br/>
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model = changeRxnBounds (model,'EX_ile_L(e)',-0.089,'l')<br/>
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model = changeRxnBounds (model,'EX_ins(e)',-0.1,'l')<br/>
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model = changeRxnBounds (model,'EX_hxan(e)',-0.1,'l')<br/>
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model = changeRxnBounds (model,'EX_h2o(e)',-1000,'l')<br/>
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model = changeRxnBounds (model,'EX_o2(e)',-1000,'l')<br/>
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model = changeRxnBounds (model,'EX_co2(e)',-1000,'l')<br/>
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model = changeRxnBounds (model,'EX_nh4(e)',-1000,'l')<br/>
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model = changeRxnBounds (model,'EX_so4(e)',-1000,'l')<br/>
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model = changeRxnBounds (model,'EX_ca2(e)',-1000,'l')<br/>
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model = changeRxnBounds (model,'EX_h(e)',-1000,'l')<br/>
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model = changeRxnBounds (model,'EX_k(e)',-1000,'l')<br/>
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model = changeRxnBounds (model,'EX_mg2(e)',-1000,'l')<br/>
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model = changeRxnBounds (model,'EX_na1(e)',-1000,'l')<br/>
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model = changeRxnBounds (model,'EX_fe3(e)',-1000,'l')<br/>
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model = changeRxnBounds (model,'EX_nac(e)',-1000,'l')<br/>
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model = changeRxnBounds (model,'EX_thym(e)',-1000,'l')<br/>
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model = changeRxnBounds (model,'EX_ade(e)',-1000,'l')<br/>
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model = changeRxnBounds (model,'EX_thr_L(e)',-0.288,'l')<br/>
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model = changeRxnBounds (model,'EX_val_L(e)',-0.071 ,'l')<br/>
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model = changeRxnBounds (model,'EX_pro_L(e)',-0.1,'l')<br/>
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model = changeRxnBounds (model,'EX_his_L(e)',-1.642,'l')<br/>
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model = changeRxnBounds (model,'EX_leu_L(e)',-0.1,'l')<br/>
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model = changeRxnBounds (model,'EX_ura(e)',-1000,'l')<br/>
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model = changeRxnBounds (model,'EX_tyr_L(e)',-0.035 ,'l')<br/>
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model = changeRxnBounds (model,'EX_trp_L(e)',0,'l')<br/>
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model = changeRxnBounds (model,'EX_met_L(e)',-0.1,'l')<br/>
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model = changeRxnBounds (model,'EX_ser_L(e)',-1.722,'l')<br/>
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model = changeRxnBounds (model,'EX_arg_L(e)',-1.17,'l')<br/>
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model = changeRxnBounds (model,'EX_asp_L(e)',-0.041,'l')<br/>
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model = changeRxnBounds (model,'EX_lys_L(e)',-0.1,'l')<br/>
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model = changeRxnBounds (model,'EX_lys_L(e)',-0.1,'l')<br/>
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model = changeRxnBounds (model,'EX_ala_L(e)',-0.369,'l')<br/>
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model = changeRxnBounds (model,'EX_zn2(e)',-1000,'l')<br/>
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model = changeRxnBounds (model,'EX_cd2(e)',-1000,'l')<br/>
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model = changeRxnBounds (model,'EX_glyc(e)',-0.014,'l')<br/>
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model = changeRxnBounds (model,'EX_gln_L(e)',-0.445,'l')<br/>
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model = changeRxnBounds (model,'EX_glu_L(e)',0,'l')<br/>
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model = changeRxnBounds (model,'EX_leu_L(e)',-0.235,'l')<br/>
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model = changeRxnBounds (model,'EX_met_D(e)',-0.084,'l')<br/>
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model = changeRxnBounds (model,'EX_met_L(e)',-0.084,'l')<br/>
 +
model = changeRxnBounds (model,'EX_tre(e)',-0.6,'l')<br/>
   </p>
   </p>
-
   <p align = "justify">When we performed the growth calculation with the biomass as objective function the flux to chorismate was 0.274 mol/hr in <b>non-LB medium</b> conditions and the growth of E. coli 0.737 hr<sup>-1</sup>. When we do the same but for the <b>LB medium conditions</b> we observe the flux to chorismate as 0.059 mol/hr and a growth rate of 66.88 hr.This suggests that LB-medium is beneficial for E.coli growth, but lowers the flux towards chorismate. This can be explained by the fact that the availability of certain nutrients in the medium reduces the need for a flux towards chorismate (a precursor of methyl salicylate). It is possible that the extra drain created by our system cannot easily be compensated by the cell. This stresses the importance of our aroG BioBrick <a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060000">(Part:BBa_K1060000)</a> approach, where we aim for a higher flux towards chorismate. This BioBrick contains mutations that can prevent the repression by Phenylalanine, that would occur otherwise and is in favour of chorismate production at the same time.
+
   <p align = "justify">When we performed the growth calculation with the biomass as objective function the flux to chorismate(a precursor of methyl salicylate) was 0.274 mol/hr in <b>non-LB medium</b> conditions and the growth of <i>E. coli</i> 0.737 hr<sup>-1</sup>. When we do the same but for the <b>LB medium conditions</b> we observe the flux to chorismate as 0.80 mol/hr and a growth rate of 5.34 hr<sup>-1</sup>. This suggests that LB-medium is beneficial for <i>E. coli</i> growth and improves the flux towards chorismate.  Our aroG BioBrick <a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060000">(Part:BBa_K1060000)</a> approach, aims however for a higher flux towards chorismate. This BioBrick contains mutations that can prevent the repression by Phenylalanine, that would occur otherwise and is in favour of chorismate production at the same time. </p>
-
<sup>-1</sup>.
+
  <p align = "justify">We were also interested to see how the maximal production of MeS is related to the maximal growth of <i>E. coli</i> under minimal conditions and in LB medium conditions. Therefore we set the objective function to MeS and set the lower bound for the biomass at different percentages of the maximal growth rate predicted with the biomass as objective function.</p>
-
  </p>
+
<img src="https://static.igem.org/mediawiki/2013/7/77/TinasFBA.jpg"/><br/>
-
  <p align = "justify">We were also interested to see how the maximal production of MeS is related to the maximal growth of E.Coli under minimal conditions. Therefore we set the objective function to MeS and set the lower bound for the biomass at different percentages of the maximal growth rate predicted with the biomass as objective function.</p>
+
<p align = "justify">As can be seen in the graph, there is a linear correlation between maximal flux towards MeS and maximal <i>E. coli</i> growth. This linear correlation is qualitatively similar both under minimal growth conditions as in LB medium conditions and shows that we are dealing with a trade off between bacterial growth and MeS production.<br/>
-
<img src="https://static.igem.org/mediawiki/2013/f/f5/Minimalconditions2.png" alt="minimalconditions"/>
+
If a higher flux to MeS is preferred over a lower <i>E. coli</i> growth mass a value at the left side of the graph should be considered, whereas a value to the right would give a higher <i>E. coli</i> production rate and a lower flux towards MeS. This trade-off is reminiscent of our wet-lab growth curve results for the MeS brick.</p><br/>
-
 
+
-
<p align = "justify">Performing the same calculations but for LB medium conditions gave us the following graph:</p> <img src="https://static.igem.org/mediawiki/2013/3/35/LBconditions2.png" alt="LBconditions"/>
+
-
 
+
-
<p align = "justify">As seen in the graphs, there is a linear correlation between maximal flux towards MeS and maximal E.Coli growth. This linear correlation is qualitatively similar both under minimal growth conditions as in LB medium conditions and shows that we are dealing with a trade off. This means that it is important to choose wisely where at the curve the best trade off between maximal cell growth and maximal flux towards MeS can be achieved, if a lower E.Coli yield is preferred in advantage of a higher flux to MeS a value at the left side of the graph should be considered, whereas a value to the right would give a higher E.Coli production rate and a lower flux towards MeS. This is consistent when taking into account the results from the wetlab since we observed lower growth curves for higher concentrations of added salicylate. If we assume that a loss of SAM
+
 +
<img src="https://static.igem.org/mediawiki/2013/d/d5/BBa_K1060003.jpg"/><br/>
 +
<p align = "justify">This figure shows how higher concentrations of added salicylate (0.1 mM) result in a longer lag phase. Higher levels of salicylate may lead to higher levels of S-adenosylmethionine (SAM) consumption, a co-substrate of the methyltransferase reaction producing methylsalicylate from salicylate. These higher consumption levels can in turn be associated with higher homocysteine levels, a side-product that stays behind when the methylgroup has been transferred to salicylate. Increased homocysteine levels are toxic for <i>E. coli</i> strains (Tuite <i>et al.</i> which may explain the ceiling we observe when adding salicylate, hoping for higher MeS fluxes.</p><br/>
  </div>
  </div>
</div>
</div>
Line 230: Line 181:
<div class="row-fluid">
<div class="row-fluid">
  <div class="span12 white">
  <div class="span12 white">
-
   
+
<p align = "justify">FBA analysis predicted LB medium conditions to be beneficial for both <i>E. coli</i> growth and the flux towards chorismate, an important precursor for methylsalicylate. Moreover, a linear correlation between maximal predicted MeS flux and maximal growth rate exists, showing a <b>trade off</b> between the two. This linear correlation is present not only for LB medium conditions but also for minimal medium conditions and is qualitatively similar, showing a steeper correlation for LB medium conditions. <b>These trade-offs are not only present <I>in silico</I> but we also found them <I>in vivo</I></b> in the choice between methyl salicylate production and growth rate. Finally, also our GC-MS analysis fits with this modelling, when salicylate was added to the medium, we not only observed a reduced growth rate but also a distinct MeS production peak.<br/>
 +
These results not only show correlation between wetlab and modelling data but also suggest that cellular level modelling can result in colony wide effects (as observed in the delayed growth).  
  </div>
  </div>
</div>
</div>
Line 246: Line 198:
Feist, AM <i>et al.</i> (2007) “A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information.” Mol. Syst. Biol. 3 121.<br/>  
Feist, AM <i>et al.</i> (2007) “A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information.” Mol. Syst. Biol. 3 121.<br/>  
Huang, J., Q. Zhang and T. Schlick (2003). "Effect of DNA superhelicity and bound proteins on mechanistic aspects of the Hin-mediated and Fis-enhanced inversion." Biophys J 85(2): 804-817.<br/>
Huang, J., Q. Zhang and T. Schlick (2003). "Effect of DNA superhelicity and bound proteins on mechanistic aspects of the Hin-mediated and Fis-enhanced inversion." Biophys J 85(2): 804-817.<br/>
-
Mahadevan, R. and C. H. Schilling (2003). "The effects of alternate optimal solutions in constraint-based genome-scale metabolic models." Metab Eng 5(4): 264-276.<br/>
+
Iyarest Tawornsamretkit, Rattana Thanasomboon, Jittrawan Thaiprasit, Dujduan Waraho, Supapon Cheevadhanarak, Asawin Meechai, Analysis of Metabolic Network of Synthetic Escherichia coli Producing Linalool Using Constraint-based Modeling, Procedia Computer Science, Volume 11, 2012, Pages 24-35, ISSN 1877-0509Mahadevan, R. and C. H. Schilling (2003). "The effects of alternate optimal solutions in constraint-based genome-scale metabolic models." Metab Eng 5(4): 264-276.<br/>
 +
Orth, J. D., I. Thiele, and B.O. Palsson (2010). "What is Flux Balance Analysis?" Nature Biotechnology (28): 245-248.<br/>  
Papin, J. A., N. D. Price, J. S. Edwards and B. B. Palsson (2002). "The genome-scale metabolic extreme pathway structure in Haemophilus influenzae shows significant network redundancy." J Theor Biol 215(1): 67-82.<br/>
Papin, J. A., N. D. Price, J. S. Edwards and B. B. Palsson (2002). "The genome-scale metabolic extreme pathway structure in Haemophilus influenzae shows significant network redundancy." J Theor Biol 215(1): 67-82.<br/>
Price, N. D., J. A. Papin, C. H. Schilling and B. O. Palsson (2003). "Genome-scale microbial in silico models: the constraints-based approach." Trends Biotechnol 21(4): 162-169.<br/>
Price, N. D., J. A. Papin, C. H. Schilling and B. O. Palsson (2003). "Genome-scale microbial in silico models: the constraints-based approach." Trends Biotechnol 21(4): 162-169.<br/>
Price, N. D., J. L. Reed and B. O. Palsson (2004). "Genome-scale models of microbial cells: evaluating the consequences of constraints." Nat Rev Microbiol 2(11): 886-897.<br/>
Price, N. D., J. L. Reed and B. O. Palsson (2004). "Genome-scale models of microbial cells: evaluating the consequences of constraints." Nat Rev Microbiol 2(11): 886-897.<br/>
Schilling, C. H., M. W. Covert, I. Famili, G. M. Church, J. S. Edwards and B. O. Palsson (2002). "Genome-scale metabolic model of Helicobacter pylori 26695." J Bacteriol 184(16): 4582-4593.<br/>
Schilling, C. H., M. W. Covert, I. Famili, G. M. Church, J. S. Edwards and B. O. Palsson (2002). "Genome-scale metabolic model of Helicobacter pylori 26695." J Bacteriol 184(16): 4582-4593.<br/>
 +
Tuite, N. L.,Fraser, K. R.,O'Byrne, C. P.(2005)."Homocysteine toxicity in Escherichia coli is caused by a perturbation of branched-chain amino acid biosynthesis" Journal of bacteriology 187(13):4362-4371.<br/>
   </p>
   </p>
  </div>
  </div>
</div>
</div>

Latest revision as of 03:24, 29 October 2013

iGem

Secret garden

Congratulations! You've found our secret garden! Follow the instructions below and win a great prize at the World jamboree!


  • A video shows that two of our team members are having great fun at our favourite company. Do you know the name of the second member that appears in the video?
  • For one of our models we had to do very extensive computations. To prevent our own computers from overheating and to keep the temperature in our iGEM room at a normal level, we used a supercomputer. Which centre maintains this supercomputer? (Dutch abbreviation)
  • We organised a symposium with a debate, some seminars and 2 iGEM project presentations. An iGEM team came all the way from the Netherlands to present their project. What is the name of their city?

Now put all of these in this URL:https://2013.igem.org/Team:KU_Leuven/(firstname)(abbreviation)(city), (loose the brackets and put everything in lowercase) and follow the very last instruction to get your special jamboree prize!

tree ladybugcartoon

Kinetic Parameters

BanAphids MeS production?

We needed to check whether the introduction of our MeS brick and the production of the components influences the overall BanAphid metabolism and/or growth rate. This modelling results will be checked against wetlab data, namely the growth curves we obtained while characterising our MeS biobricks. We also composed a Kinetic Parameter Model to estimate the average production rate of MeS. Approach and results can be found here.

A FBA calculates possibilities for the flow of metabolites through a metabolic network while maximising a set objective, in our case the growth rate of an organism or the production of a biotechnologically important metabolite. We ran the FBA for methyl salicylate using the COBRA Toolbox for MATLAB. COBRA stands for Constraint-Based Reconstruction and Analysis (COBRA) approach. It provides a biochemically and genetically consistent framework for the generation of hypotheses and the testing of functions of microbial cells. The Flux Balance Analysis (FBA) is probably the most used analysis within COBRA.

COBRA has been successfully applied to study the possible phenotypes that arise from a genome (Covert, Schilling et al. 2001; Orth et al. 2010). COBRA consists of two fundamental steps.
First, a GENRE (=GEnome-scale Network REconstruction) is formed, composed of the mathematical representation of all known metabolic reactions.
Second, the appropriate constraints are applied to form the corresponding GEMS (GEnome-scale Model in Silico).

Two fundamental types of constraints exist: balances and bounds (Price, Reed et al. 2004). Balance constraints are associated with conserved quantities such as energy, mass etc. Bounds limit numerical ranges of individual variables and parameters such as concentrations, fluxes or kinetic constants. At steady state, there is no accumulation or depletion of metabolites in a metabolic network, so the production rate of each metabolite in the network must equal its rate of consumption. This balance of fluxes can be represented mathematically as S . v = 0, where v is a vector of fluxes through the metabolic network and S is the stoichiometric matrix containing the stoichiometry of all reactions in the network.

Both bound and balance constraints limit the allowed functional states of reconstructed networks. Constraints can be very diverse in a biological system : physico-chemical constraints (reaction rates, enzyme turnover rates, diffusion rates etc.) , topo-biological (e.g. organisation of DNA in Escherichia coli by spatio-temporal patterns (Huang, Zhang et al. 2003)), environmental (nutrient availability, pH, temperature, osmolarity and the availability of electron acceptors and regulatory constraints).
In mathematical terms, the constraints define a system of linear equations which will be solved by linear programming in FBA. This will result in a range of allowable network states, described by a solution space which, in biology, represents the phenotypic potential of an organism. All allowable network states are contained in this solution space. (Covert and Palsson 2003; Price, Papin et al. 2003)
Thus, we can predict the growth rate potential of our BanAphids, defined by the constraints we impose, e.g. the growth medium, temperature, co-factor/precursor presence etc.

We used an E. coli model from 2007 (iAF1260 by Feist, AM. et al.) in all the following COBRA toolbox analyses.

As a first step we tried to predict the growth under default conditions for this model. This gave us the following results (after setting the biomass as objective function): for E. coli under default conditions a growth rate of 0.74 hr-1 is predicted. When performing the same calculations, but for LB medium conditions, a growth rate of 5.34 hr-1 is predicted. This shows that E. coli benefit from the LB medium conditions.

In a next step we wanted to add the reactions which are necessary for our model, but lacking in the iAF1260 model.
We added 'pchA', 'chor[c] -> ichor[c]' for the isochorismate synthesis reaction, 'pchB', 'ichor[c] -> sali[c] + pyr[c]' for the salicylate synthesis reaction and 'BSMT1', 'sali[c] -> methylsalicylate' for the methyl salicylate synthesis reaction.
We also added the exchange reaction for methylsalicylate ('Ex_methylsalicylate'). When we performed the growth calculation analysis for this modified model with the biomass set as objective function, we also observed a growth rate of 0.737 hr-1

Since our bacteria will be grown on LB medium, we changed the default medium settings towards those for LB medium. This means that we changed the relevant exchange reactions for the metabolites present in LB medium as seen in Tawornsamretkit et al.. The lower reaction bounds of the relevant reactions were set as following:
model = changeRxnBounds (model,'EX_glc(e)',0,'l')
model = changeRxnBounds (model,'EX_phe_L(e)',-0.1,'l')
model = changeRxnBounds (model,'EX_cys_L(e)',0,'l')
model = changeRxnBounds (model,'EX_ile_L(e)',-0.089,'l')
model = changeRxnBounds (model,'EX_ins(e)',-0.1,'l')
model = changeRxnBounds (model,'EX_hxan(e)',-0.1,'l')
model = changeRxnBounds (model,'EX_h2o(e)',-1000,'l')
model = changeRxnBounds (model,'EX_o2(e)',-1000,'l')
model = changeRxnBounds (model,'EX_co2(e)',-1000,'l')
model = changeRxnBounds (model,'EX_nh4(e)',-1000,'l')
model = changeRxnBounds (model,'EX_so4(e)',-1000,'l')
model = changeRxnBounds (model,'EX_ca2(e)',-1000,'l')
model = changeRxnBounds (model,'EX_h(e)',-1000,'l')
model = changeRxnBounds (model,'EX_k(e)',-1000,'l')
model = changeRxnBounds (model,'EX_mg2(e)',-1000,'l')
model = changeRxnBounds (model,'EX_na1(e)',-1000,'l')
model = changeRxnBounds (model,'EX_fe3(e)',-1000,'l')
model = changeRxnBounds (model,'EX_nac(e)',-1000,'l')
model = changeRxnBounds (model,'EX_thym(e)',-1000,'l')
model = changeRxnBounds (model,'EX_ade(e)',-1000,'l')
model = changeRxnBounds (model,'EX_thr_L(e)',-0.288,'l')
model = changeRxnBounds (model,'EX_val_L(e)',-0.071 ,'l')
model = changeRxnBounds (model,'EX_pro_L(e)',-0.1,'l')
model = changeRxnBounds (model,'EX_his_L(e)',-1.642,'l')
model = changeRxnBounds (model,'EX_leu_L(e)',-0.1,'l')
model = changeRxnBounds (model,'EX_ura(e)',-1000,'l')
model = changeRxnBounds (model,'EX_tyr_L(e)',-0.035 ,'l')
model = changeRxnBounds (model,'EX_trp_L(e)',0,'l')
model = changeRxnBounds (model,'EX_met_L(e)',-0.1,'l')
model = changeRxnBounds (model,'EX_ser_L(e)',-1.722,'l')
model = changeRxnBounds (model,'EX_arg_L(e)',-1.17,'l')
model = changeRxnBounds (model,'EX_asp_L(e)',-0.041,'l')
model = changeRxnBounds (model,'EX_lys_L(e)',-0.1,'l')
model = changeRxnBounds (model,'EX_lys_L(e)',-0.1,'l')
model = changeRxnBounds (model,'EX_ala_L(e)',-0.369,'l')
model = changeRxnBounds (model,'EX_zn2(e)',-1000,'l')
model = changeRxnBounds (model,'EX_cd2(e)',-1000,'l')
model = changeRxnBounds (model,'EX_glyc(e)',-0.014,'l')
model = changeRxnBounds (model,'EX_gln_L(e)',-0.445,'l')
model = changeRxnBounds (model,'EX_glu_L(e)',0,'l')
model = changeRxnBounds (model,'EX_leu_L(e)',-0.235,'l')
model = changeRxnBounds (model,'EX_met_D(e)',-0.084,'l')
model = changeRxnBounds (model,'EX_met_L(e)',-0.084,'l')
model = changeRxnBounds (model,'EX_tre(e)',-0.6,'l')

When we performed the growth calculation with the biomass as objective function the flux to chorismate(a precursor of methyl salicylate) was 0.274 mol/hr in non-LB medium conditions and the growth of E. coli 0.737 hr-1. When we do the same but for the LB medium conditions we observe the flux to chorismate as 0.80 mol/hr and a growth rate of 5.34 hr-1. This suggests that LB-medium is beneficial for E. coli growth and improves the flux towards chorismate. Our aroG BioBrick (Part:BBa_K1060000) approach, aims however for a higher flux towards chorismate. This BioBrick contains mutations that can prevent the repression by Phenylalanine, that would occur otherwise and is in favour of chorismate production at the same time.

We were also interested to see how the maximal production of MeS is related to the maximal growth of E. coli under minimal conditions and in LB medium conditions. Therefore we set the objective function to MeS and set the lower bound for the biomass at different percentages of the maximal growth rate predicted with the biomass as objective function.


As can be seen in the graph, there is a linear correlation between maximal flux towards MeS and maximal E. coli growth. This linear correlation is qualitatively similar both under minimal growth conditions as in LB medium conditions and shows that we are dealing with a trade off between bacterial growth and MeS production.
If a higher flux to MeS is preferred over a lower E. coli growth mass a value at the left side of the graph should be considered, whereas a value to the right would give a higher E. coli production rate and a lower flux towards MeS. This trade-off is reminiscent of our wet-lab growth curve results for the MeS brick.



This figure shows how higher concentrations of added salicylate (0.1 mM) result in a longer lag phase. Higher levels of salicylate may lead to higher levels of S-adenosylmethionine (SAM) consumption, a co-substrate of the methyltransferase reaction producing methylsalicylate from salicylate. These higher consumption levels can in turn be associated with higher homocysteine levels, a side-product that stays behind when the methylgroup has been transferred to salicylate. Increased homocysteine levels are toxic for E. coli strains (Tuite et al. which may explain the ceiling we observe when adding salicylate, hoping for higher MeS fluxes.


FBA analysis predicted LB medium conditions to be beneficial for both E. coli growth and the flux towards chorismate, an important precursor for methylsalicylate. Moreover, a linear correlation between maximal predicted MeS flux and maximal growth rate exists, showing a trade off between the two. This linear correlation is present not only for LB medium conditions but also for minimal medium conditions and is qualitatively similar, showing a steeper correlation for LB medium conditions. These trade-offs are not only present in silico but we also found them in vivo in the choice between methyl salicylate production and growth rate. Finally, also our GC-MS analysis fits with this modelling, when salicylate was added to the medium, we not only observed a reduced growth rate but also a distinct MeS production peak.
These results not only show correlation between wetlab and modelling data but also suggest that cellular level modelling can result in colony wide effects (as observed in the delayed growth).

Covert, M. W. and B. O. Palsson (2003). "Constraints-based models: regulation of gene expression reduces the steady-state solution space." J Theor Biol 221(3): 309-325.
Covert, M. W., C. H. Schilling, I. Famili, J. S. Edwards, Goryanin, II, E. Selkov and B. O. Palsson (2001). "Metabolic modeling of microbial strains in silico." Trends Biochem Sci 26(3): 179-186.
Feist, AM et al. (2007) “A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information.” Mol. Syst. Biol. 3 121.
Huang, J., Q. Zhang and T. Schlick (2003). "Effect of DNA superhelicity and bound proteins on mechanistic aspects of the Hin-mediated and Fis-enhanced inversion." Biophys J 85(2): 804-817.
Iyarest Tawornsamretkit, Rattana Thanasomboon, Jittrawan Thaiprasit, Dujduan Waraho, Supapon Cheevadhanarak, Asawin Meechai, Analysis of Metabolic Network of Synthetic Escherichia coli Producing Linalool Using Constraint-based Modeling, Procedia Computer Science, Volume 11, 2012, Pages 24-35, ISSN 1877-0509Mahadevan, R. and C. H. Schilling (2003). "The effects of alternate optimal solutions in constraint-based genome-scale metabolic models." Metab Eng 5(4): 264-276.
Orth, J. D., I. Thiele, and B.O. Palsson (2010). "What is Flux Balance Analysis?" Nature Biotechnology (28): 245-248.
Papin, J. A., N. D. Price, J. S. Edwards and B. B. Palsson (2002). "The genome-scale metabolic extreme pathway structure in Haemophilus influenzae shows significant network redundancy." J Theor Biol 215(1): 67-82.
Price, N. D., J. A. Papin, C. H. Schilling and B. O. Palsson (2003). "Genome-scale microbial in silico models: the constraints-based approach." Trends Biotechnol 21(4): 162-169.
Price, N. D., J. L. Reed and B. O. Palsson (2004). "Genome-scale models of microbial cells: evaluating the consequences of constraints." Nat Rev Microbiol 2(11): 886-897.
Schilling, C. H., M. W. Covert, I. Famili, G. M. Church, J. S. Edwards and B. O. Palsson (2002). "Genome-scale metabolic model of Helicobacter pylori 26695." J Bacteriol 184(16): 4582-4593.
Tuite, N. L.,Fraser, K. R.,O'Byrne, C. P.(2005)."Homocysteine toxicity in Escherichia coli is caused by a perturbation of branched-chain amino acid biosynthesis" Journal of bacteriology 187(13):4362-4371.