Moma

MOMA(modelWT, modelDel, osenseStr, verbFlag, minNorm)[source]

Performs a quadratic version of the MOMA (minimization of metabolic adjustment) approach

USAGE

[solutionDel, solutionWT, totalFluxDiff, solStatus] = MOMA (modelWT, modelDel, osenseStr, verbFlag, minNorm)

INPUTS

  • modelWT – Wild type model

  • modelDel – Deletion strain model

OPTIONAL INPUTS

  • osenseStr – Maximize (‘max’) / minimize (‘min’) (Default = ‘max’)

  • verbFlag – Verbose output (Default = false)

  • minNorm – Determines the approach to solving the first optimisation problem See minNorm option of optimizeCbModel {(1),(0), ‘one’, ‘zero’, > 0 , n x 1 vector}, where [m,n]=size(S); i.e. 1e-6 is the default, which minimises the Euclidean Norm of the

    subject to atainment of the optimal FBA objective as an additional constraint.

linearMOMA(modelWT, modelDel, osenseStr, minFluxFlag, verbFlag)[source]

Performs a linear version of the MOMA (minimization of metabolic adjustment) approach

USAGE

[solutionDel, solutionWT, totalFluxDiff, solStatus] = linearMOMA (modelWT, modelDel, osenseStr, minFluxFlag, verbFlab)

INPUTS

  • modelWT – Wild type model

  • modelDel – Deletion strain model

OPTIONAL INPUTS

  • osenseStr – Maximize (‘max’) / minimize (‘min’) (Default = ‘max’)

  • minFluxFlag – Minimize the absolute value of fluxes in the optimal MOMA solution (Default = false)

  • verbFlag – Verbose output (Default = false)

OUTPUTS

  • solutionDel – Deletion solution structure

  • solutionWT – Wild-type solution structure

  • totalFluxDiff – Value of the linear MOMA objective, i.e. \(\sum |v_{wt}-v_{del}|\)

  • solStatus – Solution status - solves the problem: (f_wt is the optimal wild type objective value found by FBA)

\[\begin{split}min ~&~ \sum |v_{wt} - v_{del}| \\ ~&~ S_{wt}v_{wt} = 0 \\ ~&~ lb_{wt} \leq v_{wt} \leq ub_{wt} \\ ~&~ c_{wt}^T v_{wt} = f_{wt} \\ ~&~ S_{del}v_{del} = 0 \\ ~&~ lb_{del} \leq v_{del} \leq ub_{del}\end{split}\]

Note

1) This formulation allows for selecting the most appropriate optimal wild type FBA solution as the starting point as opposed to picking an arbitrary starting point (original MOMA implementation).

2) The reaction sets in the two models do not have to be equal as long as there is at least one reaction in common