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See detailParallel Approximate Steady-state Analysis of Large Probabilistic Boolean Networks
Mizera, Andrzej UL; Pang, Jun UL; Yuan, Qixia UL

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 ▲]

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See detailASSA-PBN 2.0: A software tool for probabilistic Boolean networks.
Mizera, Andrzej UL; Pang, Jun UL; Yuan, Qixia UL

in Proceedings of 14th International Conference on Computational Methods in Systems Biology (2016)

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See detailImproving BDD-based attractor detection for synchronous Boolean networks.
Yuan, Qixia UL; Qu, Hongyang; Pang, Jun UL et al

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

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See detailChemometric analysis of attenuated total reflectance infrared spectra of Proteus mirabilis strains with defined structures of LPS.
Zarnowiec, Paulina; Mizera, Andrzej UL; Chrapek, Magdalena et al

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

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See detailFast simulation of probabilistic Boolean networks.
Mizera, Andrzej UL; Pang, Jun UL; Yuan, Qixia UL

in Proceedings of 14th International Conference on Computational Methods in Systems Biology (2016)

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See detailASSA-PBN: An approximate steady-state analyser for probabilistic Boolean networks
Mizera, Andrzej UL; Pang, Jun UL; Yuan, Qixia UL

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

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See detailActivity tracking: A new attack on location privacy
Chen, Xihui; Mizera, Andrzej UL; Pang, Jun UL

in Proceedings of the 3rd IEEE Conference on Communications and Network Security (CNS'15) (2015)

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See detailImproving BDD-based attractor detection for synchronous Boolean networks
Qu, Hongyang; Yuan, Qixia UL; Pang, Jun UL et al

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 ▲]

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See detailoptPBN: An Optimisation Toolbox for Probabilistic Boolean Networks
Trairatphisan, Panuwat UL; Mizera, Andrzej UL; Pang, Jun UL et al

in PLoS ONE (2014), 9(7), 980011-15

Background There exist several computational tools which allow for the optimisation and inference of biological networks using a Boolean formalism. Nevertheless, the results from such tools yield only ... [more ▼]

Background There exist several computational tools which allow for the optimisation and inference of biological networks using a Boolean formalism. Nevertheless, the results from such tools yield only limited quantitative insights into the complexity of biological systems because of the inherited qualitative nature of Boolean networks. Results We introduce optPBN, a Matlab-based toolbox for the optimisation of probabilistic Boolean networks (PBN) which operates under the framework of the BN/PBN toolbox. optPBN offers an easy generation of probabilistic Boolean networks from rule-based Boolean model specification and it allows for flexible measurement data integration from multiple experiments. Subsequently, optPBN generates integrated optimisation problems which can be solved by various optimisers. In term of functionalities, optPBN allows for the construction of a probabilistic Boolean network from a given set of potential constitutive Boolean networks by optimising the selection probabilities for these networks so that the resulting PBN fits experimental data. Furthermore, the optPBN pipeline can also be operated on large-scale computational platforms to solve complex optimisation problems. Apart from exemplary case studies which we correctly inferred the original network, we also successfully applied optPBN to study a large-scale Boolean model of apoptosis where it allows identifying the inverse correlation between UVB irradiation, NFκB and Caspase 3 activations, and apoptosis in primary hepatocytes quantitatively. Also, the results from optPBN help elucidating the relevancy of crosstalk interactions in the apoptotic network. Summary The optPBN toolbox provides a simple yet comprehensive pipeline for integrated optimisation problem generation in the PBN formalism that can readily be solved by various optimisers on local or grid-based computational platforms. optPBN can be further applied to various biological studies such as the inference of gene regulatory networks or the identification of the interaction's relevancy in signal transduction networks. [less ▲]

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See detailModel-checking based approaches to parameter estimation of gene regulatory networks
Mizera, Andrzej UL; Pang, Jun UL; Yuan, Qixia UL

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

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See detailRecent development and biomedical applications of probabilistic Boolean networks
Trairatphisan, Panuwat UL; Mizera, Andrzej UL; Pang, Jun UL et al

in Cell Communication and Signaling (2013), 11(46),

Probabilistic Boolean network (PBN) modelling is a semi-quantitative approach widely used for the study of the topology and dynamic aspects of biological systems. The combined use of rule-based ... [more ▼]

Probabilistic Boolean network (PBN) modelling is a semi-quantitative approach widely used for the study of the topology and dynamic aspects of biological systems. The combined use of rule-based representation and probability makes PBN appealing for large-scale modelling of biological networks where degrees of uncertainty need to be considered. A considerable expansion of our knowledge in the field of theoretical research on PBN can be observed over the past few years, with a focus on network inference, network intervention and control. With respect to areas of applications, PBN is mainly used for the study of gene regulatory networks though with an increasing emergence in signal transduction, metabolic, and also physiological networks. At the same time, a number of computational tools, facilitating the modelling and analysis of PBNs, are continuously developed. A concise yet comprehensive review of the state-of-the-art on PBN modelling is offered in this article, including a comparative discussion on PBN versus similar models with respect to concepts and biomedical applications. Due to their many advantages, we consider PBN to stand as a suitable modelling framework for the description and analysis of complex biological systems, ranging from molecular to physiological levels. [less ▲]

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See detailA balancing act: Parameter estimation for biological models with steady-state measurements
Mizera, Andrzej UL; Pang, Jun UL; Sauter, Thomas UL et al

in Proceedings of 11th Conference on Computational Methods in Systems Biology (CMSB'13) (2013)

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See detailMathematical modelling of the Platelet-Derived Growth Factor (PDGF) signalling pathway
Mizera, Andrzej UL; Pang, Jun UL; Sauter, Thomas UL et al

in Proceedings of 4th Workshop on Computational Models for Cell Processes (CompMod'13) (2013)

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See detailQuantitative analysis of the self-assembly strategies of intermediate filaments from tetrameric vimentin
Czeizler, Eugen; Mizera, Andrzej UL; Czeizler, Elena et al

in IEEE/ACM Transactions on Computational Biology and Bioinformatics (2012), 9(3), 885-898

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See detailComputational methods for quantitative submodel comparison
Mizera, Andrzej UL; Czeizler, Elena; Petre, Ion

in Katz, Evgeny (Ed.) Biomolecular Information Processing. From Logic Systems to Smart Sensors and Actuators (2012)

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See detailA Boolean Approach for Disentangling the Roles of Submodules to the Global Properties of a Biomodel
Czeizler, Elena; Mizera, Andrzej UL; Petre, Ion

in Fundamenta Informaticae (2012), 116(1-4), 51-63

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See detailSelf-assembly models of variable resolution
Mizera, Andrzej UL; Czeizler, Eugen; Petre, Ion

in Lecture Notes in Computer Science (2012), 7625

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See detailMethods for Biochemical Model Decomposition and Quantitative Submodel Comparison
Mizera, Andrzej UL; Czeizler, Elena; Petre, Ion

in Israel Journal of Chemistry (2011), 51(1), 151164

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