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Network Class Superposition Analyses
Networks are often used to understand a whole system by modeling the interactions among its pieces. Examples include biomolecules in a cell interacting to provide some primary function, or species in an environment forming a stable community. However, these interactions are often unknown; instead, t...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3614996/ https://www.ncbi.nlm.nih.gov/pubmed/23565141 http://dx.doi.org/10.1371/journal.pone.0059046 |
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author | Pearson, Carl A. B. Zeng, Chen Simha, Rahul |
author_facet | Pearson, Carl A. B. Zeng, Chen Simha, Rahul |
author_sort | Pearson, Carl A. B. |
collection | PubMed |
description | Networks are often used to understand a whole system by modeling the interactions among its pieces. Examples include biomolecules in a cell interacting to provide some primary function, or species in an environment forming a stable community. However, these interactions are often unknown; instead, the pieces' dynamic states are known, and network structure must be inferred. Because observed function may be explained by many different networks (e.g., [Image: see text] for the yeast cell cycle process [1]), considering dynamics beyond this primary function means picking a single network or suitable sample: measuring over all networks exhibiting the primary function is computationally infeasible. We circumvent that obstacle by calculating the network class ensemble. We represent the ensemble by a stochastic matrix [Image: see text], which is a transition-by-transition superposition of the system dynamics for each member of the class. We present concrete results for [Image: see text] derived from Boolean time series dynamics on networks obeying the Strong Inhibition rule, by applying [Image: see text] to several traditional questions about network dynamics. We show that the distribution of the number of point attractors can be accurately estimated with [Image: see text]. We show how to generate Derrida plots based on [Image: see text]. We show that [Image: see text]-based Shannon entropy outperforms other methods at selecting experiments to further narrow the network structure. We also outline an experimental test of predictions based on [Image: see text]. We motivate all of these results in terms of a popular molecular biology Boolean network model for the yeast cell cycle, but the methods and analyses we introduce are general. We conclude with open questions for [Image: see text], for example, application to other models, computational considerations when scaling up to larger systems, and other potential analyses. |
format | Online Article Text |
id | pubmed-3614996 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-36149962013-04-05 Network Class Superposition Analyses Pearson, Carl A. B. Zeng, Chen Simha, Rahul PLoS One Research Article Networks are often used to understand a whole system by modeling the interactions among its pieces. Examples include biomolecules in a cell interacting to provide some primary function, or species in an environment forming a stable community. However, these interactions are often unknown; instead, the pieces' dynamic states are known, and network structure must be inferred. Because observed function may be explained by many different networks (e.g., [Image: see text] for the yeast cell cycle process [1]), considering dynamics beyond this primary function means picking a single network or suitable sample: measuring over all networks exhibiting the primary function is computationally infeasible. We circumvent that obstacle by calculating the network class ensemble. We represent the ensemble by a stochastic matrix [Image: see text], which is a transition-by-transition superposition of the system dynamics for each member of the class. We present concrete results for [Image: see text] derived from Boolean time series dynamics on networks obeying the Strong Inhibition rule, by applying [Image: see text] to several traditional questions about network dynamics. We show that the distribution of the number of point attractors can be accurately estimated with [Image: see text]. We show how to generate Derrida plots based on [Image: see text]. We show that [Image: see text]-based Shannon entropy outperforms other methods at selecting experiments to further narrow the network structure. We also outline an experimental test of predictions based on [Image: see text]. We motivate all of these results in terms of a popular molecular biology Boolean network model for the yeast cell cycle, but the methods and analyses we introduce are general. We conclude with open questions for [Image: see text], for example, application to other models, computational considerations when scaling up to larger systems, and other potential analyses. Public Library of Science 2013-04-02 /pmc/articles/PMC3614996/ /pubmed/23565141 http://dx.doi.org/10.1371/journal.pone.0059046 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. |
spellingShingle | Research Article Pearson, Carl A. B. Zeng, Chen Simha, Rahul Network Class Superposition Analyses |
title | Network Class Superposition Analyses |
title_full | Network Class Superposition Analyses |
title_fullStr | Network Class Superposition Analyses |
title_full_unstemmed | Network Class Superposition Analyses |
title_short | Network Class Superposition Analyses |
title_sort | network class superposition analyses |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3614996/ https://www.ncbi.nlm.nih.gov/pubmed/23565141 http://dx.doi.org/10.1371/journal.pone.0059046 |
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