Browse ORBilu by Open Access
- What it is and what it isn't
- Green Road / Gold Road?
- Ready to Publish. Now What?
- How can I support the OA movement?
- Where can I learn more?
ORBilu is a project developed by
Showing results 1 to 20 of 37
   An efficient approach towards the source-target control of Boolean networksPaul, Soumya  ; Su, Cui ; Pang, Jun et alin IEEE/ACM Transactions on Computational Biology and Bioinformatics (in press) We study the problem of computing a minimal subset of nodes of a given asynchronous Boolean network that need to be perturbed in a single-step to drive its dynamics from an initial state to a target ... [more ▼] We study the problem of computing a minimal subset of nodes of a given asynchronous Boolean network that need to be perturbed in a single-step to drive its dynamics from an initial state to a target steady state (or attractor), which we call the source-target control of Boolean networks. Due to the phenomenon of state-space explosion, a simple global approach that performs computations on the entire network, may not scale well for large networks. We believe that efficient algorithms for such networks must exploit the structure of the networks together with their dynamics. Taking this view, we derive a decomposition-based solution to the minimal source-target control problem which can be significantly faster than the existing approaches on large networks. We then show that the solution can be further optimised if we take into account appropriate information about the source state. We apply our solutions to both real-life biological networks and randomly generated networks, demonstrating the efficiency and efficacy of our approach. [less ▲] Detailed reference viewed: 98 (10 UL) A new decomposition-based method for detecting attractors in synchronous Boolean networks; Mizera, Andrzej ; Pang, Jun et alin Science of Computer Programming (2019), 180 Detailed reference viewed: 70 (5 UL)GPU-accelerated steady-state computation of large probabilistic Boolean networksMizera, Andrzej ; Pang, Jun ; in Formal Aspects of Computing (2019), 31(1), 27-46 Detailed reference viewed: 72 (1 UL)Taming asynchrony for attractor detection in large Boolean networksMizera, Andrzej ; Pang, Jun ; et alin IEEE/ACM Transactions on Computational Biology and Bioinformatics (2019), 16(1), 31-42 Detailed reference viewed: 84 (3 UL)A Decomposition-based Approach towards the Control of Boolean NetworksPaul, Soumya ; ; Pang, Jun et alin Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (2018) Detailed reference viewed: 88 (4 UL)Reviving the two-state Markov chain approachMizera, Andrzej ; Pang, Jun ; Yuan, Qixia 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 ▲] Detailed reference viewed: 116 (7 UL)ASSA-PBN 3.0: Analysing Context-Sensitive Probabilistic Boolean NetworksMizera, Andrzej ; Pang, Jun ; et alin Proceedings of the 16th International Conference on Computational Methods in Systems Biology (2018) Detailed reference viewed: 79 (3 UL)ASSA-PBN: A Toolbox for Probabilistic Boolean NetworksMizera, Andrzej ; Pang, Jun ; et alin IEEE/ACM Transactions on Computational Biology and Bioinformatics (2018), 15(4), 1203-1216 Detailed reference viewed: 61 (4 UL)A new decomposition method for attractor detection in large synchronous Boolean networksMizera, Andrzej ; Pang, Jun ; et alin Proceedings of the 3rd International Symposium on Dependable Software Engineering: Theories, Tools, and Applications (2017) Detailed reference viewed: 121 (5 UL)Parallel Approximate Steady-state Analysis of Large Probabilistic Boolean NetworksMizera, Andrzej ; Pang, Jun ; Yuan, Qixia 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 ▲] Detailed reference viewed: 127 (9 UL)GPU-accelerated steady-state analysis of probabilistic Boolean networksMizera, Andrzej ; Pang, Jun ; Yuan, Qixia Poster (2016) Detailed reference viewed: 55 (7 UL)Improving BDD-based attractor detection for synchronous Boolean networks.Yuan, Qixia ; ; Pang, Jun et alin SCIENCE CHINA Information Sciences (2016), 59(8), 0801011-08010116 Detailed reference viewed: 139 (24 UL)Chemometric analysis of attenuated total reflectance infrared spectra of Proteus mirabilis strains with defined structures of LPS.; Mizera, Andrzej ; et alin Innate Immunity (2016), 22(5), 325-335 Detailed reference viewed: 146 (5 UL)ASSA-PBN 2.0: A software tool for probabilistic Boolean networks.Mizera, Andrzej ; Pang, Jun ; Yuan, Qixia in Proceedings of 14th International Conference on Computational Methods in Systems Biology (2016) Detailed reference viewed: 131 (10 UL)Fast simulation of probabilistic Boolean networks.Mizera, Andrzej ; Pang, Jun ; Yuan, Qixia in Proceedings of 14th International Conference on Computational Methods in Systems Biology (2016) Detailed reference viewed: 97 (5 UL)Improving BDD-based attractor detection for synchronous Boolean networks; Yuan, Qixia ; Pang, Jun et alin 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 ▲] Detailed reference viewed: 101 (6 UL)ASSA-PBN: An approximate steady-state analyser for probabilistic Boolean networksMizera, Andrzej ; Pang, Jun ; Yuan, Qixia in Proceedings of the 13th International Symposium on Automated Technology for Verification and Analysis (ATVA'15) (2015) Detailed reference viewed: 124 (10 UL)Activity tracking: A new attack on location privacy; Mizera, Andrzej ; Pang, Jun in Proceedings of the 3rd IEEE Conference on Communications and Network Security (CNS'15) (2015) Detailed reference viewed: 106 (11 UL)optPBN: An Optimisation Toolbox for Probabilistic Boolean NetworksTrairatphisan, Panuwat ; Mizera, Andrzej ; Pang, Jun et alin 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 ▲] Detailed reference viewed: 178 (17 UL)Model-checking based approaches to parameter estimation of gene regulatory networksMizera, Andrzej ; Pang, Jun ; Yuan, Qixia in Proceedings of 19th IEEE Conference on Engineering of Complex Computer Systems (2014) Detailed reference viewed: 107 (11 UL)
1
2
|
||
