R: Perform a transition to the next state

stateTransition {BoolNet} | R Documentation

## Perform a transition to the next state

### Description

Calculates the next state in a supplied network for a given current state

### Usage

    stateTransition(network,
                    state,
                    type = c("synchronous","asynchronous","probabilistic"),
                    geneProbabilities,
                    chosenGene,
                    chosenFunctions,
                    timeStep = 0)

### Arguments

`network` | A network structure of class `BooleanNetwork`, `SymbolicBooleanNetwork` or `ProbabilisticBooleanNetwork`. These networks can be read from files by `loadNetwork`, generated by `generateRandomNKNetwork`, or reconstructed by `reconstructNetwork`.
`state` | The current state of the network, encoded as a vector with one 0-1 element for each gene. If `network` is of class `SymbolicBooleanNetwork` and makes use of more than one predecessor state, this can also be a matrix with the genes in the columns and multiple predecessor states in the rows.
`type` | The type of transition to be performed. If set to "synchronous", all genes are updated using the corresponding transition functions. If set to "asynchronous", only one gene is updated. This gene is either chosen randomly or supplied in parameter `chosenGene`. If set to "probabilistic", one transition function is chosen for each gene, and the genes are updated synchronously. The functions are either chosen randomly depending on their probabilities, or they are supplied in parameter `chosenFunctions`. Default is "synchronous" for objects of class `BooleanNetwork` and `SymbolicBooleanNetwork`, and "probabilistic" for objects of class `ProbabilisticBooleanNetwork`.
`geneProbabilities` | An optional vector of probabilities for the genes if `type="asynchronous"`. By default, each gene has the same probability to be chosen for the next state transition. These probabilities can be modified by supplying a vector of probabilities for the genes which sums up to one.
`chosenGene` | If `type="asynchronous"` and this parameter is supplied, no random update is performed. Instead, the gene with the supplied name or index is chosen for the next transition.
`chosenFunctions` | If `type="probabilistic"`, this parameter can contain a set of function indices for each gene. In this case, transition functions are not chosen randomly, but the provided functions are used in the state transition.
`timeStep` | An optional parameter that specifies the current time step associated with `state`. This is only relevant for networks of class `SymbolicBooleanNetwork` that make use of time-dependent predicates (`timelt, timeis, timegt`). Otherwise, this parameter is ignored.

### Value

The subsequent state of the network, encoded as a vector with one 0-1 element for each gene.

### See Also

`loadNetwork`, `generateRandomNKNetwork`, `generateState`

### Examples

    # load example network
    data(cellcycle)

    # calculate a synchronous state transition
    print(stateTransition(cellcycle, c(1,1,1,1,1,1,1,1,1,1)))

    # calculate an asynchronous state transition of gene CycA
    print(stateTransition(cellcycle, c(1,1,1,1,1,1,1,1,1,1),
                          type="asynchronous", chosenGene="CycA"))

    # load probabilistic network
    data(examplePBN)

    # perform a probabilistic state transition
    print(stateTransition(examplePBN, c(0,1,1),
                          type="probabilistic"))

[Package BoolNet version 2.1.4 Index]