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Inferring phenomenological models of first passage processes
Biochemical processes in cells are governed by complex networks of many chemical species interacting stochastically in diverse ways and on different time scales. Constructing microscopically accurate models of such networks is often infeasible. Instead, here we propose a systematic framework for bui...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7968746/ https://www.ncbi.nlm.nih.gov/pubmed/33667218 http://dx.doi.org/10.1371/journal.pcbi.1008740 |
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author | Rivera, Catalina Hofmann, David Nemenman, Ilya |
author_facet | Rivera, Catalina Hofmann, David Nemenman, Ilya |
author_sort | Rivera, Catalina |
collection | PubMed |
description | Biochemical processes in cells are governed by complex networks of many chemical species interacting stochastically in diverse ways and on different time scales. Constructing microscopically accurate models of such networks is often infeasible. Instead, here we propose a systematic framework for building phenomenological models of such networks from experimental data, focusing on accurately approximating the time it takes to complete the process, the First Passage (FP) time. Our phenomenological models are mixtures of Gamma distributions, which have a natural biophysical interpretation. The complexity of the models is adapted automatically to account for the amount of available data and its temporal resolution. The framework can be used for predicting behavior of FP systems under varying external conditions. To demonstrate the utility of the approach, we build models for the distribution of inter-spike intervals of a morphologically complex neuron, a Purkinje cell, from experimental and simulated data. We demonstrate that the developed models can not only fit the data, but also make nontrivial predictions. We demonstrate that our coarse-grained models provide constraints on more mechanistically accurate models of the involved phenomena. |
format | Online Article Text |
id | pubmed-7968746 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-79687462021-03-31 Inferring phenomenological models of first passage processes Rivera, Catalina Hofmann, David Nemenman, Ilya PLoS Comput Biol Research Article Biochemical processes in cells are governed by complex networks of many chemical species interacting stochastically in diverse ways and on different time scales. Constructing microscopically accurate models of such networks is often infeasible. Instead, here we propose a systematic framework for building phenomenological models of such networks from experimental data, focusing on accurately approximating the time it takes to complete the process, the First Passage (FP) time. Our phenomenological models are mixtures of Gamma distributions, which have a natural biophysical interpretation. The complexity of the models is adapted automatically to account for the amount of available data and its temporal resolution. The framework can be used for predicting behavior of FP systems under varying external conditions. To demonstrate the utility of the approach, we build models for the distribution of inter-spike intervals of a morphologically complex neuron, a Purkinje cell, from experimental and simulated data. We demonstrate that the developed models can not only fit the data, but also make nontrivial predictions. We demonstrate that our coarse-grained models provide constraints on more mechanistically accurate models of the involved phenomena. Public Library of Science 2021-03-05 /pmc/articles/PMC7968746/ /pubmed/33667218 http://dx.doi.org/10.1371/journal.pcbi.1008740 Text en © 2021 Rivera et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://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 Rivera, Catalina Hofmann, David Nemenman, Ilya Inferring phenomenological models of first passage processes |
title | Inferring phenomenological models of first passage processes |
title_full | Inferring phenomenological models of first passage processes |
title_fullStr | Inferring phenomenological models of first passage processes |
title_full_unstemmed | Inferring phenomenological models of first passage processes |
title_short | Inferring phenomenological models of first passage processes |
title_sort | inferring phenomenological models of first passage processes |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7968746/ https://www.ncbi.nlm.nih.gov/pubmed/33667218 http://dx.doi.org/10.1371/journal.pcbi.1008740 |
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