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   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: 128 (16 UL) Proceedings of the 6th Global Conference on Artificial Intelligence (GCAI 2020)Danoy, Grégoire ; Pang, Jun ; in 6th Global Conference on Artificial Intelligence (2020, May) Detailed reference viewed: 94 (8 UL)Preface for the special issue of the 12th International Symposium on Theoretical Aspects of Software Engineering (TASE 2018)Pang, Jun ; in Science of Computer Programming (2020), 187 Detailed reference viewed: 31 (0 UL)Algorithms for the Sequential Reprogramming of Boolean Networks; ; Pang, Jun et alin IEEE/ACM Transactions on Computational Biology and Bioinformatics (2019), 16(5), 1610-1619 Detailed reference viewed: 60 (1 UL)Scalable control of asynchronous Boolean networks; Paul, Soumya ; Pang, Jun in Proceedings of 17th International Conference on Computational Methods in Systems Biology (CMSB'19) (2019) Detailed reference viewed: 53 (0 UL)Controlling large Boolean networks with single-step perturbations; Paul, Soumya ; et alin Bioinformatics (2019), 35(14), 558-567 Detailed reference viewed: 114 (2 UL)An active learning-based approach for location-aware acquaintance inference; ; et al in Knowledge and Information Systems (2019), 59(3), 539-569 Detailed reference viewed: 93 (2 UL)Proceedings of the 24th International Conference on Engineering of Complex Computer SystemsPang, Jun ; Book published by IEEE (2019) Detailed reference viewed: 19 (0 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: 86 (3 UL)Controlling large Boolean networks with temporary and permanent perturbations; Paul, Soumya ; Pang, Jun in Proceedings of the 23rd International Symposium on Formal Methods (FM'19) (2019) Detailed reference viewed: 56 (1 UL)A graph-based approach to explore relationship between hashtags and imagesZhong, Zhiqiang ; ; Pang, Jun in Proceedings of the 20th International Conference on Web Information System Engineering (2019) Detailed reference viewed: 52 (2 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: 74 (5 UL)Sequential reprogramming of Boolean networks made practical; ; et al in Proceedings of 17th International Conference on Computational Methods in Systems Biology (CMSB'19) (2019) Detailed reference viewed: 54 (0 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: 78 (1 UL)Proceedings of the 12th International Symposium on Theoretical Aspects of Software EngineeringPang, Jun ; ; et alBook published by IEEE Computer Society (2018) Detailed reference viewed: 33 (2 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: 82 (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: 89 (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: 120 (7 UL)Tagvisor: A privacy advisor for sharing hashtags; ; et al in Proceedings of The Web Conference 2018 (WWW'18) (2018) Detailed reference viewed: 35 (2 UL)Towards the Existential Control of Boolean Networks: A Preliminary ReportPaul, Soumya ; Pang, Jun ; in Proceedings of the 4th International Symposium on Dependable Software Engineering. Theories, Tools, and Applications (2018) Detailed reference viewed: 58 (1 UL) |
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