Lrs

• Lrsinterface

equivalentSets(model)

Finds the stoichiometrically balanced loops and the equivalent sets

USAGE:

[loops, eqSets, P, P2, cyclicBool] = equivalentSets (model)

INPUT:

model – COBRA Toolbox model with fields:

• .S - matrix

• .lb - lower bounds

• .ub - upper bounds

• .biomassAbbr - biomass abbreviation

OUTPUTS:

• loops – stoichiometrically balanced loops

• eqSets – equivalent sets

• P – as P2 but additinally dependent on cyclicBool

• P2 – output of lrsOutputReadRay

• cyclicBool – boolean variable

extremePathways(model, positivity, inequality)

Calculates the extreme pathways of a stoichiometric model using the vertex/facet enumeration package

USAGE:

[R, V] = extremePathways (model, positivity, inequality)

INPUTS:

model – COBRA Toolbox model with fields:

• .description - string used to name files

• .lb - lower bounds and

• .ub - upper bounds to derive directionality

• .b - dxdt

OPTIONAL INPUT:

• positivity – {0, (1)} if positivity == 1, then positive orthant base

• inequality – {(0), 1} if inequality == 1, then use two inequalities rather than a single equality

OUTPUTS:

• R – nDim by nRay matrix of extreme rays

• V – nDim by nVertex matrix of vertices

extremePools(model, param)

Calculates the extreme pools of a stoichiometric model using the vertex / facet enumeration package such that

INPUT:

model.S - `m x (n + k)

OPTIONAL INPUTS:

• model.SConsistentRxnBool – n x 1 boolean indicating stoichiometrically consistent metabolites

• model.SIntRxnBool - Boolean of reactions heuristically though to be mass balanced.

• model.SIntMetBool - Boolean of metabolites heuristically though to be involved in mass balanced reactions.

• positivity – {0, (1)} if positivity == 1, then positive orthant base

• inequality – {(0), 1} if inequality == 1, then use two inequalities rather than a single equaltiy

OUTPUT:

• P – p x m matrix of non-negative entries such that P*N = 0.

• vertexBool n x 1 Boolean vector indicating which columns of P are vertices

• N – m x n stoichiometric matrix used such that P*N.

Author(s) Ronan Fleming