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  Control Strategy Identification via Trap Spaces in Boolean Networks

Cifuentes Fontanals, L., Tonello, E., & Siebert, H. (2020). Control Strategy Identification via Trap Spaces in Boolean Networks. In A. Abate, T. Petrov, & V. Wolf (Eds.), CMSB 2020, part of Lecture Notes in Computer Science 12314 (pp. 159-175). Cham, Switzerland: Springer Nature Switzerland AG. doi:10.1007/978-3-030-60327-4_9.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000E-5B36-0 Version Permalink: https://hdl.handle.net/21.11116/0000-000E-5B37-F
Genre: Conference Paper

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CMSB 2020_Cifuentes Fontanals et al.pdf (Publisher version), 417KB
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 Creators:
Cifuentes Fontanals, Laura1, Author                 
Tonello, Elisa , Author
Siebert, Heike, Author
Affiliations:
1IMPRS for Biology and Computation (Anne-Dominique Gindrat), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479666              

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Free keywords: Boolean network · Control strategy · Trap space · Phenotype
 Abstract: The control of biological systems presents interesting applications such as cell reprogramming or drug target identification. A common type of control strategy consists in a set of interventions that, by fixing the values of some variables, force the system to evolve to a desired state. This work presents a new approach for finding control strategies in biological systems modeled by Boolean networks. In this context, we explore the properties of trap spaces, subspaces of the state space which the dynamics cannot leave. Trap spaces for biological networks can often be efficiently computed, and provide useful approximations of attraction basins. Our approach provides control strategies for a target phenotype that are based on interventions that allow the control to be eventually released. Moreover, our method can incorporate information about the attractors to find new control strategies that would escape usual percolation-based methods. We show the applicability of our approach to two cell fate decision models.

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Language(s): eng - English
 Dates: 2020-09-29
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-030-60327-4_9
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Title: 18th International Conference, CMSB 2019: Computational Methods in Systems Biology
Place of Event: Konstanz
Start-/End Date: 2020-09-23 - 2020-09-25

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Title: CMSB 2020, part of Lecture Notes in Computer Science 12314
Source Genre: Proceedings
 Creator(s):
Abate, Alessandro, Editor
Petrov, Tatjana, Editor
Wolf, Verena, Editor
Affiliations:
-
Publ. Info: Cham, Switzerland : Springer Nature Switzerland AG
Pages: - Volume / Issue: LNBI 12314 Sequence Number: - Start / End Page: 159 - 175 Identifier: ISBN: 978-3-030-60326-7 (print) 978-3-030-60327-4 (online)