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Probabilistic edge weights fine-tune Boolean network dynamics
Biological systems are noisy by nature. This aspect is reflected in our experimental measurements and should be reflected in the models we build to better understand these systems. Noise can be especially consequential when trying to interpret specific regulatory interactions, i.e. regulatory networ...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9584532/ https://www.ncbi.nlm.nih.gov/pubmed/36215324 http://dx.doi.org/10.1371/journal.pcbi.1010536 |
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author | Deritei, Dávid Kunšič, Nina Csermely, Péter |
author_facet | Deritei, Dávid Kunšič, Nina Csermely, Péter |
author_sort | Deritei, Dávid |
collection | PubMed |
description | Biological systems are noisy by nature. This aspect is reflected in our experimental measurements and should be reflected in the models we build to better understand these systems. Noise can be especially consequential when trying to interpret specific regulatory interactions, i.e. regulatory network edges. In this paper, we propose a method to explicitly encode edge-noise in Boolean dynamical systems by probabilistic edge-weight (PEW) operators. PEW operators have two important features: first, they introduce a form of edge-weight into Boolean models through the noise, second, the noise is dependent on the dynamical state of the system, which enables more biologically meaningful modeling choices. Moreover, we offer a simple-to-use implementation in the already well-established BooleanNet framework. In two application cases, we show how the introduction of just a few PEW operators in Boolean models can fine-tune the emergent dynamics and increase the accuracy of qualitative predictions. This includes fine-tuning interactions which cause non-biological behaviors when switching between asynchronous and synchronous update schemes in dynamical simulations. Moreover, PEW operators also open the way to encode more exotic cellular dynamics, such as cellular learning, and to implementing edge-weights for regulatory networks inferred from omics data. |
format | Online Article Text |
id | pubmed-9584532 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-95845322022-10-21 Probabilistic edge weights fine-tune Boolean network dynamics Deritei, Dávid Kunšič, Nina Csermely, Péter PLoS Comput Biol Research Article Biological systems are noisy by nature. This aspect is reflected in our experimental measurements and should be reflected in the models we build to better understand these systems. Noise can be especially consequential when trying to interpret specific regulatory interactions, i.e. regulatory network edges. In this paper, we propose a method to explicitly encode edge-noise in Boolean dynamical systems by probabilistic edge-weight (PEW) operators. PEW operators have two important features: first, they introduce a form of edge-weight into Boolean models through the noise, second, the noise is dependent on the dynamical state of the system, which enables more biologically meaningful modeling choices. Moreover, we offer a simple-to-use implementation in the already well-established BooleanNet framework. In two application cases, we show how the introduction of just a few PEW operators in Boolean models can fine-tune the emergent dynamics and increase the accuracy of qualitative predictions. This includes fine-tuning interactions which cause non-biological behaviors when switching between asynchronous and synchronous update schemes in dynamical simulations. Moreover, PEW operators also open the way to encode more exotic cellular dynamics, such as cellular learning, and to implementing edge-weights for regulatory networks inferred from omics data. Public Library of Science 2022-10-10 /pmc/articles/PMC9584532/ /pubmed/36215324 http://dx.doi.org/10.1371/journal.pcbi.1010536 Text en © 2022 Deritei et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Deritei, Dávid Kunšič, Nina Csermely, Péter Probabilistic edge weights fine-tune Boolean network dynamics |
title | Probabilistic edge weights fine-tune Boolean network dynamics |
title_full | Probabilistic edge weights fine-tune Boolean network dynamics |
title_fullStr | Probabilistic edge weights fine-tune Boolean network dynamics |
title_full_unstemmed | Probabilistic edge weights fine-tune Boolean network dynamics |
title_short | Probabilistic edge weights fine-tune Boolean network dynamics |
title_sort | probabilistic edge weights fine-tune boolean network dynamics |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9584532/ https://www.ncbi.nlm.nih.gov/pubmed/36215324 http://dx.doi.org/10.1371/journal.pcbi.1010536 |
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