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Improving BDD-based attractor detection for synchronous Boolean networks

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Abstract

Boolean networks are an important formalism for modelling biological systems and have attracted much attention in recent years. An important challenge in Boolean networks is to exhaustively find attractors, which represent steady states of a biological network. 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, a method to accelerate attractor detection based on an analysis of the network structure, and two heuristics on ordering BDD variables. We demonstrate the performance of our approach on a number of examples and on a realistic model of apoptosis in hepatocytes. We compare it with one existing technique in the literature.

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Authors and Affiliations

  1. Faculty of Science, Technology and Communication, University of Luxembourg, Luxembourg, 1359, Luxembourg

    Qixia Yuan, Jun Pang & Andrzej Mizera

  2. Department of Automatic Control & Systems Engineering, University of Sheffield, Sheffield, S1 3JD, UK

    Hongyang Qu

  3. Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg, 1359, Luxembourg

    Jun Pang

Authors
  1. Qixia Yuan
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  2. Hongyang Qu
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  3. Jun Pang
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  4. Andrzej Mizera
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Correspondence to Qixia Yuan.

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Yuan, Q., Qu, H., Pang, J. et al. Improving BDD-based attractor detection for synchronous Boolean networks. Sci. China Inf. Sci. 59, 080101 (2016). https://doi.org/10.1007/s11432-016-5594-9

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