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SAF: SAT-Based Attractor Finder in Asynchronous Automata Networks

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Computational Methods in Systems Biology (CMSB 2023)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 14137))

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Abstract

In this paper, we present a SAT-based Attractor Finder (SAF) which computes attractors in biological regulatory networks modelled as asynchronous automata networks. SAF is based on translating the problem of finding attractors of a bounded size into a satisfiability problem to take advantage of state-of-the-art SAT encodings and solvers. SAF accepts an automata network and outputs attractors in ascending size order until the bound is reached. SAF’s main contribution is providing an alternative to existing attractor finders. There are cases where it is able to find some attractors while other techniques fail to do so. We observed such capability on both automata networks and Boolean networks. SAF is simple to use: it is available as a command line tool as well as a web application. Finally, SAF being written in Scala, it can run on any operating system with a Java virtual machine when combined with the SAT solver Sat4j.

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Notes

  1. 1.

    In our environment, Pystablemotifs version 3.3 throws an error to T-LGL on our computer. Thus, we provide the number of attractors found in their paper [18].

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Acknowledgements

This work was financially supported by the “PHC Sakura” program (43009SC, JPJSBP120193213), implemented by MESRI and JSPS. This work was also supported by JSPS KAKENHI (JP21K11828, JP22K11973, JP23K11047), and by ROIS NII Open Collaborative Research 2023 (23FP04).

Author information

Authors and Affiliations

  1. Kobe University, Information Infrastructure and Digital Transformation Initiatives Headquarters, 1-1, Rokko-dai, Nada, Kobe, Hyogo, 657-8501, Japan

    Takehide Soh

  2. Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, 44000, Nantes, France

    Morgan Magnin

  3. Univ. Artois, CNRS, Centre de Recherche en Informatique de Lens (CRIL), 62300, Lens, France

    Daniel Le Berre

  4. Nagoya University, Graduate School of Informatics, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan

    Mutsunori Banbara

  5. Kobe University, 1-1, Rokko-dai, Nada, Kobe, Hyogo, 657-8501, Japan

    Naoyuki Tamura

Authors
  1. Takehide Soh
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  2. Morgan Magnin
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  3. Daniel Le Berre
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  4. Mutsunori Banbara
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  5. Naoyuki Tamura
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Corresponding author

Correspondence to Takehide Soh .

Editor information

Editors and Affiliations

  1. University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Jun Pang

  2. Inria Lille, Villeneuve d’Ascq, France

    Joachim Niehren

Input File and Web Interface of SAF

Input File and Web Interface of SAF

The automata network from Fig. 1 can be described by the following automata network runningexample.an input file as in Fig. 3. The web application version of SAF is demonstrated in Fig. 3 as well.

Fig. 3.
figure 3

Input File and Web Interface of SAF.

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Soh, T., Magnin, M., Le Berre, D., Banbara, M., Tamura, N. (2023). SAF: SAT-Based Attractor Finder in Asynchronous Automata Networks. In: Pang, J., Niehren, J. (eds) Computational Methods in Systems Biology. CMSB 2023. Lecture Notes in Computer Science(), vol 14137. Springer, Cham. https://doi.org/10.1007/978-3-031-42697-1_12

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  • DOI: https://doi.org/10.1007/978-3-031-42697-1_12

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  • Print ISBN: 978-3-031-42696-4

  • Online ISBN: 978-3-031-42697-1

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