User:Daniela A. Garcia S./Notebook/Modeling 2011 UNAM Genomics-Mexico/2011/04/03

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Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox, Scott A Becker, Adam M Feist, Monica L Mo, Gregory Hannum, Bernhard Ø Palsson & Markus J Herrgard

  • Constraint-based reconstruction and analysis toolbox
  • Allows for quantitative prediction of cellular behavior using a constraint-based approach
  • Reconstruction is herein defined as the list of biochemical reactions occurring in a particular cellular system. A reconstruction can be converted to a model by including the assumptions necessary for computational simulation
  • in silico analysis of these networks accessible to researchers with a wide range of interests
  • Most of the methods described herein compute reaction fluxes, which are a quantitative representation of the reaction rates of each biochemical reaction in the network; a range of feasible values exist when fluxes are subjected to known constraints.
  • Some of the methods we describe are based on the assumption that cells strive to maximize their growth rate. This assumption is satisfied by simulating maximal production of the molecules required to make new cells (biomass precursor molecules).
  • S, is the centerpiece of a mathematical representation of genome-scale metabolic networks5. This matrix represents each reaction as a column and each metabolite as a row, where each numerical element is the corresponding stoichiometric coefficient.
  • It is important to note that if the same compound exists in multiple cellular compartments, it must be given a separate row for each compartment.
  • All protocols described herein require defining an upper and lower bound for the allowable flux through each reaction; the lowest and highest reaction rate possible for each reaction. The set of upper and lower bounds is represented as two separate vectors, each containing as many components as there are columns in the stoichiometric matrix, and in the same order.
    • Setting upper and lower bounds is especially important for reactions that exchange metabolites across the system boundary. Exchange reactions serve to uptake compounds


1. Reading SBML format models into Matlab

model = readCbModel(fileName);

It can also be downloaded directly, as the ecoli_core_model

2. Changing lower and/or upper bounds of flux

model = changeRxnBounds(model, rxnNameList, boundValue, boundType);

For exchange reactions, negative lower bounds allow entry into the system, whereas positive upper bounds allow exit from the system.

3. Changing the objective function

model = changeObjective(model,rxnNameList,objectiveCoeff)

ObjectiveCoeff is not used in the FBA Tutorial

4. Adding or removing reactions

model = addReaction(model,rxnName,metaboliteList,stoichCoeffList);

metaboliteList and stoichCoeffList contain the names of the metabolites participating in the reaction (name designated in rxnName) and the corresponding stoichiometric coefficients in a vector. By default, reversible reaction.

model = removeRxns(model,rxnRemoveList);

5.Printing out reaction formulas


  • This are the basical points to work with a SBML file