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arXiv · 1501.01779

Reviving the Two-state Markov Chain Approach (Technical Report)

Andrzej Mizera·Jun Pang·Qixia Yuan
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Abstract

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.

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Andrzej Mizera, Jun Pang, Qixia Yuan. 2015-01-08. Reviving the Two-state Markov Chain Approach (Technical Report). https://arxiv.org/abs/1501.01779

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