Control in Boolean Networks With Model Checking

Cifuentes-Fontanals, Laura and Tonello, Elisa and Siebert, Heike (2022) Control in Boolean Networks With Model Checking. Frontiers in Applied Mathematics and Statistics, 8. ISSN 2297-4687

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Abstract

Control in Boolean Networks With Model Checking Laura Cifuentes-Fontanals Elisa Tonello Heike Siebert

Understanding control mechanisms in biological systems plays a crucial role in important applications, for instance in cell reprogramming. Boolean modeling allows the identification of possible efficient strategies, helping to reduce the usually high and time-consuming experimental efforts. Available approaches to control strategy identification usually focus either on attractor or phenotype control, and are unable to deal with more complex control problems, for instance phenotype avoidance. They also fail to capture, in many situations, all possible minimal strategies, finding instead only sub-optimal solutions. In order to fill these gaps, we present a novel approach to control strategy identification in Boolean networks based on model checking. The method is guaranteed to identify all minimal control strategies, and provides maximal flexibility in the definition of the control target. We investigate the applicability of the approach by considering a range of control problems for different biological systems, comparing the results, where possible, to those obtained by alternative control methods.
4 26 2022 838546 10.3389/fams.2022.838546 1 10.3389/crossmark-policy frontiersin.org true Freie Universität Berlin http://dx.doi.org/10.13039/501100007537 https://creativecommons.org/licenses/by/4.0/ 10.3389/fams.2022.838546 https://www.frontiersin.org/articles/10.3389/fams.2022.838546/full https://www.frontiersin.org/articles/10.3389/fams.2022.838546/full PLoS Comput Biol Flobak 11 e1004426 2015 Discovery of drug synergies in gastric cancer cells predicted by logical modeling 10.1371/journal.pcbi.1004426 PLoS Comput Biol Calzone 6 e1000702 2010 Mathematical modelling of cell-fate decision in response to death receptor engagement 10.1371/journal.pcbi.1000702 PLoS Comput Biol Grieco 9 e1003286 2013 Integrative modelling of the influence of MAPK network on cancer cell fate decision 10.1371/journal.pcbi.1003286 Proc Natl Acad Sci USA Zhang 105 16308 2008 Network model of survival signaling in large granular lymphocyte leukemia 10.1073/pnas.0806447105 Nat Rev Mol Cell Biol Dongre 20 69 2019 New insights into the mechanisms of epithelial-mesenchymal transition and implications for cancer 10.1038/s41580-018-0080-4 Computational Methods in Systems Biology Mandon 3 2019 10.1007/978-3-030-31304-3_1 Sequential reprogramming of Boolean networks made practical PLoS Comput Biol Zañudo 11 e1004193 2015 Cell fate reprogramming by control of intracellular network dynamics 10.1371/journal.pcbi.1004193 IEEE/ACM Trans Comput Biol Bioinform Biane 16 1574 2019 Causal reasoning on boolean control networks based on abduction: theory and application to cancer drug discovery 10.1109/TCBB.2018.2889102 Computational Methods in Systems Biology Cifuentes Fontanals 159 2020 10.1007/978-3-030-60327-4_9 Control strategy identification via trap spaces in Boolean networks J Comput Biol Samaga 17 39 2010 Computing combinatorial intervention strategies and failure modes in signaling networks 10.1089/cmb.2009.0121 Front Physiol Yang 9 454 2018 Target control in logical models using the domain of influence of nodes 10.3389/fphys.2018.00454 Theory Pract Logic Programm Kaminski 13 675 2013 Minimal intervention strategies in logical signaling networks with ASP 10.1017/S1471068413000422 BMC Syst Biol Murrugarra 10 94 2016 Identification of control targets in Boolean molecular network models via computational algebra 10.1186/s12918-016-0332-x Bioinformatics Su 37 879 2020 CABEAN: a software for the control of asynchronous Boolean networks 10.1093/bioinformatics/btaa752 Front Plant Sci Carrillo 3 155 2012 An overview of existing modeling tools making use of model checking in the analysis of biochemical networks 10.3389/fpls.2012.00155 Front Bioeng Biotechnol Klarner 3 130 2015 Approximating attractors of Boolean networks by iterative CTL model checking 10.3389/fbioe.2015.00130 Principles of Model Checking Baier 2008 Bioinformatics Klarner 33 770 2016 PyBoolNet: a Python package for the generation, analysis and visualization of Boolean networks 10.1093/bioinformatics/btw682 Methods Mol Biol Chaouiya 804 463 2012 Logical modelling of gene regulatory networks with GINsim 10.1007/978-1-61779-361-5_23 Cancer Res Selvaggio 80 2407 2020 Hybrid epithelial-mesenchymal phenotypes are controlled by microenvironmental factors 10.1158/0008-5472.CAN-19-3147 Bioinformatics Videla 33 947 2016 Caspo: a toolbox for automated reasoning on the response of logical signaling networks families 10.1093/bioinformatics/btw738 Nat Rev Clin Oncol Carneiro 17 395 2020 Targeting apoptosis in cancer therapy 10.1038/s41571-020-0341-y

Item Type: Article
Subjects: Impact Archive > Mathematical Science
Depositing User: Managing Editor
Date Deposited: 05 Jan 2023 06:24
Last Modified: 23 May 2024 05:29
URI: http://research.sdpublishers.net/id/eprint/954

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