pystablemotifs: Python library for attractor identification and control in Boolean networks

Summary: pystablemotifs is a Python 3 library for analyzing Boolean networks. Its non-heuristic and exhaustive attractor identification algorithm was previously presented in Rozum et al. (2021). Here, we illustrate its performance improvements over similar methods and discuss how it uses outputs of the attractor identification process to drive a system to one of its attractors from any initial state. We implement six attractor control algorithms, five of which are new in this work. By design, these algorithms can return different control strategies, allowing for synergistic use. We also give a brief overview of the other tools implemented in pystablemotifs.

Availability and implementation: The source code is on GitHub at https://github.com/jcrozum/pystablemotifs/.

Supplementary information: Supplementary data are available at Bioinformatics online.

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Work Title pystablemotifs: Python library for attractor identification and control in Boolean networks
Access
Open Access
Creators
  1. Jordan C. Rozum
  2. Dávid Deritei
  3. Kyu Hyong Park
  4. Jorge Gómez Tejeda Zañudo
  5. Réka Albert
License In Copyright (Rights Reserved)
Work Type Article
Publisher
  1. Bioinformatics
Publication Date December 7, 2021
Publisher Identifier (DOI)
  1. https://doi.org/10.1093/bioinformatics/btab825
Related URLs
Deposited August 01, 2022

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Version 1
published

  • Created
  • Added pystablemotifs_final.pdf
  • Added Creator Jordan C. Rozum
  • Added Creator Dávid Deritei
  • Added Creator Kyu Hyong Park
  • Added Creator Jorge Gómez Tejeda Zañudo
  • Added Creator Réka Albert
  • Published
  • Updated Work Title, Subtitle, Related URLs, and 1 more Show Changes
    Work Title
    • Pystablemotifs
    • pystablemotifs: Python library for attractor identification and control in Boolean networks
    Subtitle
    • Python library for attractor identification and control in Boolean networks
    Related URLs
    • https://github.com/jcrozum/pystablemotifs/
    Publication Date
    • 2022-03-01
    • 2021-12-07
  • Updated Description Show Changes
    Description
    • <p>Summary: pystablemotifs is a Python 3 library for analyzing Boolean networks. Its non-heuristic and exhaustive attractor identification algorithm was previously presented in Rozum et al. (2021). Here, we illustrate its performance improvements over similar methods and discuss how it uses outputs of the attractor identification process to drive a system to one of its attractors from any initial state. We implement six attractor control algorithms, five of which are new in this work. By design, these algorithms can return different control strategies, allowing for synergistic use. We also give a brief overview of the other tools implemented in pystablemotifs. </p>
    • <p>Summary: pystablemotifs is a Python 3 library for analyzing Boolean networks. Its non-heuristic and exhaustive attractor identification algorithm was previously presented in Rozum et al. (2021). Here, we illustrate its performance improvements over similar methods and discuss how it uses outputs of the attractor identification process to drive a system to one of its attractors from any initial state. We implement six attractor control algorithms, five of which are new in this work. By design, these algorithms can return different control strategies, allowing for synergistic use. We also give a brief overview of the other tools implemented in pystablemotifs. </p> Availability and implementation:
    • The source code is on GitHub at https://github.com/jcrozum/pystablemotifs/.
    • Supplementary information:
    • Supplementary data are available at Bioinformatics online.
  • Updated Description Show Changes
    Description
    • <p>Summary: pystablemotifs is a Python 3 library for analyzing Boolean networks. Its non-heuristic and exhaustive attractor identification algorithm was previously presented in Rozum et al. (2021). Here, we illustrate its performance improvements over similar methods and discuss how it uses outputs of the attractor identification process to drive a system to one of its attractors from any initial state. We implement six attractor control algorithms, five of which are new in this work. By design, these algorithms can return different control strategies, allowing for synergistic use. We also give a brief overview of the other tools implemented in pystablemotifs. </p> Availability and implementation:
    • <p>Summary: pystablemotifs is a Python 3 library for analyzing Boolean networks. Its non-heuristic and exhaustive attractor identification algorithm was previously presented in Rozum et al. (2021). Here, we illustrate its performance improvements over similar methods and discuss how it uses outputs of the attractor identification process to drive a system to one of its attractors from any initial state. We implement six attractor control algorithms, five of which are new in this work. By design, these algorithms can return different control strategies, allowing for synergistic use. We also give a brief overview of the other tools implemented in pystablemotifs. </p>
    • Availability and implementation:
    • The source code is on GitHub at https://github.com/jcrozum/pystablemotifs/.
    • Supplementary information:
    • Supplementary data are available at Bioinformatics online.