markovSimulation {BoolNet}  R Documentation 
Identify important states in probabilistic Boolean networks
Description
Identifies important states in probabilistic Boolean networks (PBN) using a Markov chain simulation
Usage
markovSimulation(network,
numIterations = 1000,
startStates = list(),
cutoff = 0.001,
returnTable = TRUE)
Arguments
network 
An object of class 
numIterations 
The number of iterations for the matrix multiplication, which corresponds to the number of state transitions to simulate 
startStates 
An optional list of start states. Each entry of the list must be a vector with a 0/1 value for each gene. If specified, the simulation is restricted to the states reachable from the supplied start states. Otherwise, all states are considered. 
cutoff 
The cutoff value used to determine if a probability is 0. All output probabilities less than or equal to this value are set to 0. 
returnTable 
If set to true, a transition table annotated with the probabilities for the transitions is included in the results. This is required by 
Details
The algorithm identifies important states by performing the following steps: First, a Markov matrix is calculated from the set of transition functions, where each entry of the matrix specifies the probability of a state transition from the state belonging to the corresponding row to the state belonging to the corresponding column. A vector is initialized with uniform probability for all states (or – if specified – uniform probability for all start states) and repeatedly multiplied with the Markov matrix. The method returns all states with nonzero probability in this vector. See the references for more details.
Value
An object of class MarkovSimulation
with the following components:
reachedStates 
A data frame with one state in each row. The first columns specify the gene values of the state, and the last column holds the probability that the corresponding state is reached after 
genes 
A vector of gene names of the input network 
table 
If

References
I. Shmulevich, E. R. Dougherty, S. Kim, W. Zhang (2002), Probabilistic Boolean networks: a rulebased uncertainty model for gene regulatory networks. Bioinformatics 18(2):261–274.
See Also
reconstructNetwork
, plotPBNTransitions
, getTransitionProbabilities
Examples
## Not run:
# load example network
data(examplePBN)
# perform a Markov chain simulation
sim < markovSimulation(examplePBN)
# print the relevant states and transition probabilities
print(sim)
# plot the transitions and their probabilities
plotPBNTransitions(sim)
## End(Not run)