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BNClassifier: Classifying Boolean Models by Dynamic Properties

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Computational Methods in Systems Biology (CMSB 2024)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 14971))

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Abstract

Partially Specified Boolean Networks (PSBNs) represent a family of Boolean models resulting from possible interpretations of unknown update logics. Hybrid extension of CTL (HCTL) has the power to express complex dynamical phenomena, such as oscillations or stability. We present BNClassifier to classify Boolean Networks corresponding to a given PSBN according to criteria specified in HCTL. The implementation of the tool is fully symbolic (based on BDDs). The results are visualised using the machine-learning-based technology of decision trees.

The work has been supported by the Czech Science Foundation grant No. GA22-10845S. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 101034413.

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Notes

  1. 1.

    https://crates.io/crates/biodivine-hctl-model-checker.

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Authors and Affiliations

  1. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Nikola Beneš, Luboš Brim, Ondřej Huvar & David Šafránek

  2. Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria

    Samuel Pastva

Authors
  1. Nikola Beneš
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  2. Luboš Brim
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  3. Ondřej Huvar
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  4. Samuel Pastva
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  5. David Šafránek
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Corresponding author

Correspondence to David Šafránek .

Editor information

Editors and Affiliations

  1. University of Pisa, Pisa, Italy

    Roberta Gori

  2. Università di Pisa, Pisa, Italy

    Paolo Milazzo

  3. IMT School for Advanced Studies Lucca, Lucca, Italy

    Mirco Tribastone

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Beneš, N., Brim, L., Huvar, O., Pastva, S., Šafránek, D. (2024). BNClassifier: Classifying Boolean Models by Dynamic Properties. In: Gori, R., Milazzo, P., Tribastone, M. (eds) Computational Methods in Systems Biology. CMSB 2024. Lecture Notes in Computer Science(), vol 14971. Springer, Cham. https://doi.org/10.1007/978-3-031-71671-3_2

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  • DOI: https://doi.org/10.1007/978-3-031-71671-3_2

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  • Online ISBN: 978-3-031-71671-3

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