MetaboLouise (version 1.0.0)

DataSimulateR: DataSimulateR

Description

Simulated dynamic/longitudinal data based on an underlying network. The network is initialized with values for every node (e.g. concentrations in the case of metabolites). These values evolve over time caused by the (variable) rates.

Usage

DataSimulateR(NetworkMatrix, dT, Tstart, Tstop, T0_nodes = 100,
  influx_vector = NULL, influx_Tframe = NULL, rate_vector = NULL,
  rate_mapping = NULL, RateFunctionObject = NULL, plot_out = TRUE,
  plot_title = NULL)

Arguments

NetworkMatrix

The underlying network in matrix form (e.g. form the NetworkCreateR function).

dT

Simulation time step (must be small enough to avoid approximation errors).

Tstart

Starting time point.

Tstop

Ending time point.

T0_nodes

Vector (or single value) with the initial node value(s) (metabolite concentrations).

influx_vector

Vector with the influx (per time unit) received by the corresponding metabolites.

influx_Tframe

Vector of two values indicating the start and ending time of the influx (if only single ending time value supplied, assumption of influx start = Tstart is made). This can also be a data frame/matrix with 2 columns and 1 row per metabolite, or just a single ending value.

rate_vector

Vector with the initial rates. Number of rates can be between 1 and the number of edges in the network.

rate_mapping

A matrix (same size as NetworkMatrix) with for every link/edge in the network (1 in NetworkMatrix) an index of rate_vector to be matched.

RateFunctionObject

An object from RateFunctionBuildR describing the evolution of rates. If not provided, the rates are constant.

plot_out

Whether to plot the simulated data.

plot_title

Optional plot title.

Value

A list with: the time vector and a matrix with the simulated data. (1 row per node)

Examples

# NOT RUN {
Nmetabos <- 20L
Nrates <- 10L

Network <- NetworkCreateR(N = Nmetabos, BA_power = 0.5, BA_mValue = 4)

Rate_function <- RateFunctionBuildR(type = "sigmoid")

rate_vector <- round(5*runif(Nrates))

rate_mapping <- Network
active_rates <- which(Network == 1, arr.ind = TRUE)
for(rr in 1:nrow(active_rates)){
    rate_mapping[active_rates[rr,1], active_rates[rr,2]] <- sample(seq_along(rate_vector), size = 1)
    }
    
 No_influx <- DataSimulateR(NetworkMatrix = Network, dT = 0.01, Tstart = 0, Tstop = 3, 
                            T0_nodes = 100, rate_vector = rate_vector, rate_mapping = rate_mapping, 
                            RateFunctionObject = Rate_function, plot_out = TRUE)

# }