Rfba

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

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)[source]

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)[source]

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