2015/01/08 by Andrzej Mizera, Mizera, Andrzej, Jun Pang +3
Biochemistry, Genetics and Molecular Biology · Computer Science · #Computational Drug Discovery Methods #Computational Engineering #FOS: Computer and information sciences #Finance #Gene Regulatory Network Analysis #Logic in Computer Science (cs.LO) #Protein Structure and Dynamics #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.1501.01779
openalex publication_date 2015/01/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Probabilistic Boolean networks (PBNs) is a well-established computational framework for modelling biological systems. The steady-state dynamics of PBNs is of crucial importance in the study of such systems. However, for large PBNs, which often arise in systems biology, obtaining the steady-state distribution poses a significant challenge. In fact, statistical methods for steady-state approximation are the only viable means when dealing with large networks. In this paper, we revive the two-state Markov chain approach presented in the literature. We first identify a problem of generating biased results, due to the size of the initial sample with which the approach needs to start and we propose a few heuristics to avoid such a pitfall. Second, we conduct an extensive experimental comparison of the two-state Markov chain approach and another approach based on the Skart method and we show that statistically the two-state Markov chain has a better performance. Finally, we apply this approach to a large PBN model of apoptosis in hepatocytes.