Simulation: Simulation using Dynamic Flux balance analysis over time as...
In i3bionet/CoRegFlux: CoRegFlux

Description Usage Arguments Details Value Examples

Simulation using Dynamic Flux balance analysis over time as in varma

Simulation(model, time = c(0, 1), metabolites, initial_biomass,
  biomass_flux_index = CoRegFlux::get_biomass_flux_position(model),
  coregnet = NULL, regulator_table = NULL, gene_table = NULL,
  gene_state_function = NULL, time_step_fba_bounds = NULL,
  softplus_parameter = 0, aliases = NULL)

`model`	An object of class modelOrg, the genome-scale metabolic model (GEM).
`time`	Timepoints at which the flux balance analysis solution will be evaluated.
`metabolites`	A data.frame containing the extraneous metabolites and the initial concentrations
`initial_biomass`	The value of the biomass at the beginning of the simulation
`biomass_flux_index`	index of the flux corresponding to the biomass reaction.
`coregnet`	Object of class CoRegNet, containing the regulatory and coregulatory interactions.
`regulator_table`	A data.frame containing 3 columns: "regulator", "influence","expression" containing respectively the name of a TF present in the CoRegNet object as a string, its influence in the condition of interest as a numerical and an expression factor of 0 for a KO, or an integer >1 for an overexpression
`gene_table`	A data.frame containing 2 columns: "gene" and "expression" containing respectively the name of a gene present in the modelOrg as a string and an expression factor of 0 for a KO, or an integer >1 for an overexpression
`gene_state_function`	Function to obtain the gene state for a given subset of gene
`time_step_fba_bounds`	Bounds for the fba problem at each time point, overrides any other form of constraining for a given flux.
`softplus_parameter`	the softplus parameter identify through calibration
`aliases`	Optional. A data.frame containing the gene names currently used in the network under the colname "geneName" and the alias under the colnames "alias"

The simulation function allows the user to run several kind of simulations based on the provided arguments. When providing only the GEM, time, initial biomass and the metabolites, a classical dFBA is carried out. To integrate the gene expression to the GEM, the gene_state_function must be provided while if the user wants to simulate a TF knock-out or overexpression, then a coregnet object and the regulator table should also be provided. See the vignette and quick-user guide for more examples.

Return a list containing the simulation information such as the objective_history, fluxes_history, met_concentration_history, biomass_history

data("SC_GRN_1")
data("SC_EXP_DATA")
data("SC_experiment_influence")
data("iMM904")
data("aliases_SC")
data("PredictedGeneState")

metabolites<-data.frame("name"=c("D-Glucose","Glycerol"),
                        "concentrations"=c(16,0))

result_without_any_constraint<-Simulation(iMM904,time=seq(1,10,by=1),
                   metabolites,
                   initial_biomass=0.45,
                   aliases = aliases_SC)

GeneState<-data.frame("Name"=names(PredictedGeneState),
                    "State"=unname(PredictedGeneState))

result<-Simulation(iMM904,time=seq(1,10,by=1),
                   metabolites,
                   initial_biomass=0.45,
                   gene_state_function=function(a,b){GeneState},
                   aliases = aliases_SC)

result$biomass_history