rFBA

dynamicRFBA(model, substrateRxns, initConcentrations, initBiomass, timeStep, nSteps, plotRxns, exclUptakeRxns)

Performs dynamic rFBA simulation using the static optimization approach

USAGE:

[concentrationMatrix, excRxnNames, timeVec,biomassVec, drGenes, constrainedRxns, states] = dynamicRFBA(model, substrateRxns, initConcentrations, initBiomass, timeStep, nSteps, plotRxns, exclUptakeRxns)

INPUTS:

model: a regulatory COBRA model substrateRxns: list of exchange reaction names for substrates

initially in the media that may change (i.e. not h2o or co2)

initConcentrations: initial concentrations of substrates (in the same

structure as substrateRxns)

initBiomass: initial biomass timeStep: time step size nSteps: maximum number of time steps plotRxns: reactions to be plotted exclUptakeRxns: list of uptake reactions whose substrate

concentrations do not change (opt, default {‘EX_co2(e)’, ‘EX_o2(e)’, ‘EX_h2o(e)’, ‘EX_h(e)’})

OUTPUTS:

concentrationMatrix: matrix of extracellular metabolite concentrations excRxnNames: names of exchange reactions for the EC metabolites timeVec: vector of time points biomassVec: vector of biomass values drGenes: vector of downregulated genes constrainedRxns: vector of downregulated reactions states: vector of regulatory network states

If no initial concentration is given for a substrate that has an open uptake in the model (i.e. model.lb < 0) the concentration is assumed to be high enough to not be limiting. If the uptake rate for a nutrient is calculated to exceed the maximum uptake rate for that nutrient specified in the model and the max uptake rate specified is > 0, the maximum uptake rate specified in the model is used instead of the calculated uptake rate.

The dynamic FBA method implemented in this function is essentially the same as the method described in [Varma, A., and B. O. Palsson. Appl. Environ. Microbiol. 60:3724 (1994)]. This function does not implement the dynamic FBA using dynamic optimization approach described in [Mahadevan, R. et al. Biophys J, 83:1331-1340 (2003)].

optimizeRegModel(model, initialRegState)

Finds the steady state solution of a model with Boolean regulatory constraints

USAGE:

[FBAsols, DRgenes, constrainedRxns, cycleStart, states] = optimizeRegModel(model, initialRegState)

INPUTS:

model: a regulatory COBRA model initialRegState: the initial state of the regulatory network as a

Boolean vector (opt, default = all false)

OUTPUTS:

FBAsols: all of the FBA solutions at the steady state (or stable

cycle) of the regulatory network

DRgenes: the genes that are OFF for every FBA solution constrainedRxns: the reactions that are OFF for every FBA solution cycleStart: the number of iterations before the regulatory network

reaches the steady state or cycle

states: the state of the regulatory network at every iteration

calculated

solveBooleanRegModel(model, initialState, inputs1States, inputs2States)

Determines the next state of the regulatory network based on the current state. Called by optimizeRegModel and dynamicRFBA

USAGE:

[finalState, finalInputs1States, finalInputs2States] = solveBooleanRegModel(model, initialState, inputs1States, inputs2States)

INPUTS:

model: a regulatory COBRA model initialState: initial state of regulatory network inputs1States: initial state of type 1 inputs (metabolites) inputs2States: initial state of type 2 inputs (reactions)

OUTPUTS:

finalState: final state of regulatory network finalInputs1States: final state of type 1 inputs finalInputs2States: final state of type 2 inputs