  Probabilistic Boolean networks (PBNs) is a widely used computational
framework for modelling biological systems. The steady-state dynamics of PBNs
is of special interest in the analysis of biological systems. However,
obtaining the steady-state distributions for such systems poses a significant
challenge due to the state space explosion problem which often arises in the
case of large PBNs. The only viable way is to use statistical methods. We have
considered the two-state Markov chain approach and the Skart method for the
analysis of large PBNs in our previous work. However, the sample size required
in both methods is often huge in the case of large PBNs and generating them is
expensive in terms of computation time. Parallelising the sample generation is
an ideal way to solve this issue. In this paper, we consider combining the
German & Rubin method with either the two-state Markov chain approach or the
Skart method for parallelisation. The first method can be used to run multiple
independent Markov chains in parallel and to control their convergence to the
steady-state while the other two methods can be used to determine the sample
size required for computing the steady-state probability of states of interest.
Experimental results show that our proposed combinations can reduce time cost
of computing stead-state probabilities of large PBNs significantly.
