A new decomposition-based method for detecting attractors in synchronous Boolean networks

in Science of Computer Programming (2019), 180

Taming asynchrony for attractor detection in large Boolean networks

in IEEE/ACM Transactions on Computational Biology and Bioinformatics (2019), 16(1), 31-42

A Decomposition-based Approach towards the Control of Boolean Networks

in Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (2018)

Reviving the two-state Markov chain approach

in IEEE/ACM Transactions on Computational Biology and Bioinformatics (2018), 15(5), 1525-1537

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 ... [more ▼]

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 this paper, we revive the two-state Markov chain approach to solve this problem. This paper contributes in three aspects. First, we identify a problem of generating biased results with the approach and we propose a few heuristics to avoid such a pitfall. Secondly, we conduct an extensive experimental comparison of the extended two-state Markov chain approach and another approach based on the Skart method. We analyse the results with machine learning techniques and we show that statistically the two-state Markov chain approach has a better performance. Finally, we demonstrate the potential of the extended two-state Markov chain approach on a case study of a large PBN model of apoptosis in hepatocytes. [less ▲]

Parallel Approximate Steady-state Analysis of Large Probabilistic Boolean Networks

in Proceedings of the 31st ACM Symposium on Applied Computing (2016, April)

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 ... [more ▼]

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 machinery. However, obtaining the steady-state distributions for such systems poses a significant challenge due to the state space explosion problem which arises in the case of large PBNs. The only viable way is to use statistical methods. In the literature, the two-state Markov chain approach and the Skart method have been proposed for the analysis of large PBNs. However, the sample size required by 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 Gelman & 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. [less ▲]

Improving BDD-based attractor detection for synchronous Boolean networks.

in SCIENCE CHINA Information Sciences (2016), 59(8), 0801011-08010116

Chemometric analysis of attenuated total reflectance infrared spectra of Proteus mirabilis strains with defined structures of LPS.

in Innate Immunity (2016), 22(5), 325-335

Improving BDD-based attractor detection for synchronous Boolean networks

in Proceedings of the 7th Asia-Pacific Symposium on Internetware (2015)

Boolean networks are an important formalism for modelling biological systems and have attracted much attention in recent years. An important direction in Boolean networks is to exhaustively find ... [more ▼]

Boolean networks are an important formalism for modelling biological systems and have attracted much attention in recent years. An important direction in Boolean networks is to exhaustively find attractors, which represent steady states when a biological network evolves for a long term. In this paper, we propose a new approach to improve the efficiency of BDD-based attractor detection. Our approach includes a monolithic algorithm for small networks, an enumerative strategy to deal with large networks, and two heuristics on ordering BDD variables. We demonstrate the performance of our approach on a number of examples, and compare it with one existing technique in the literature. [less ▲]

ASSA-PBN: An approximate steady-state analyser for probabilistic Boolean networks

in Proceedings of the 13th International Symposium on Automated Technology for Verification and Analysis (ATVA'15) (2015)

Model-checking based approaches to parameter estimation of gene regulatory networks

in Proceedings of 19th IEEE Conference on Engineering of Complex Computer Systems (2014)