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Inference of a Boolean Network From Causal Logic Implications

Biological systems contain a large number of molecules that have diverse interactions. A fruitful path to understanding these systems is to represent them with interaction networks, and then describe flow processes in the network with a dynamic model. Boolean modeling, the simplest discrete dynamic...

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Autores principales: Maheshwari, Parul, Assmann, Sarah M., Albert, Reka
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9246059/
https://www.ncbi.nlm.nih.gov/pubmed/35783282
http://dx.doi.org/10.3389/fgene.2022.836856
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author Maheshwari, Parul
Assmann, Sarah M.
Albert, Reka
author_facet Maheshwari, Parul
Assmann, Sarah M.
Albert, Reka
author_sort Maheshwari, Parul
collection PubMed
description Biological systems contain a large number of molecules that have diverse interactions. A fruitful path to understanding these systems is to represent them with interaction networks, and then describe flow processes in the network with a dynamic model. Boolean modeling, the simplest discrete dynamic modeling framework for biological networks, has proven its value in recapitulating experimental results and making predictions. A first step and major roadblock to the widespread use of Boolean networks in biology is the laborious network inference and construction process. Here we present a streamlined network inference method that combines the discovery of a parsimonious network structure and the identification of Boolean functions that determine the dynamics of the system. This inference method is based on a causal logic analysis method that associates a logic type (sufficient or necessary) to node-pair relationships (whether promoting or inhibitory). We use the causal logic framework to assimilate indirect information obtained from perturbation experiments and infer relationships that have not yet been documented experimentally. We apply this inference method to a well-studied process of hormone signaling in plants, the signaling underlying abscisic acid (ABA)—induced stomatal closure. Applying the causal logic inference method significantly reduces the manual work typically required for network and Boolean model construction. The inferred model agrees with the manually curated model. We also test this method by re-inferring a network representing epithelial to mesenchymal transition based on a subset of the information that was initially used to construct the model. We find that the inference method performs well for various likely scenarios of inference input information. We conclude that our method is an effective approach toward inference of biological networks and can become an efficient step in the iterative process between experiments and computations.
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spelling pubmed-92460592022-07-01 Inference of a Boolean Network From Causal Logic Implications Maheshwari, Parul Assmann, Sarah M. Albert, Reka Front Genet Genetics Biological systems contain a large number of molecules that have diverse interactions. A fruitful path to understanding these systems is to represent them with interaction networks, and then describe flow processes in the network with a dynamic model. Boolean modeling, the simplest discrete dynamic modeling framework for biological networks, has proven its value in recapitulating experimental results and making predictions. A first step and major roadblock to the widespread use of Boolean networks in biology is the laborious network inference and construction process. Here we present a streamlined network inference method that combines the discovery of a parsimonious network structure and the identification of Boolean functions that determine the dynamics of the system. This inference method is based on a causal logic analysis method that associates a logic type (sufficient or necessary) to node-pair relationships (whether promoting or inhibitory). We use the causal logic framework to assimilate indirect information obtained from perturbation experiments and infer relationships that have not yet been documented experimentally. We apply this inference method to a well-studied process of hormone signaling in plants, the signaling underlying abscisic acid (ABA)—induced stomatal closure. Applying the causal logic inference method significantly reduces the manual work typically required for network and Boolean model construction. The inferred model agrees with the manually curated model. We also test this method by re-inferring a network representing epithelial to mesenchymal transition based on a subset of the information that was initially used to construct the model. We find that the inference method performs well for various likely scenarios of inference input information. We conclude that our method is an effective approach toward inference of biological networks and can become an efficient step in the iterative process between experiments and computations. Frontiers Media S.A. 2022-06-16 /pmc/articles/PMC9246059/ /pubmed/35783282 http://dx.doi.org/10.3389/fgene.2022.836856 Text en Copyright © 2022 Maheshwari, Assmann and Albert. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Maheshwari, Parul
Assmann, Sarah M.
Albert, Reka
Inference of a Boolean Network From Causal Logic Implications
title Inference of a Boolean Network From Causal Logic Implications
title_full Inference of a Boolean Network From Causal Logic Implications
title_fullStr Inference of a Boolean Network From Causal Logic Implications
title_full_unstemmed Inference of a Boolean Network From Causal Logic Implications
title_short Inference of a Boolean Network From Causal Logic Implications
title_sort inference of a boolean network from causal logic implications
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9246059/
https://www.ncbi.nlm.nih.gov/pubmed/35783282
http://dx.doi.org/10.3389/fgene.2022.836856
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