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Computational Methods for Estimating Molecular System from Membrane Potential Recordings in Nerve Growth Cone
Biological cells express intracellular biomolecular information to the extracellular environment as various physical responses. We show a novel computational approach to estimate intracellular biomolecular pathways from growth cone electrophysiological responses. Previously, it was shown that cGMP s...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5852145/ https://www.ncbi.nlm.nih.gov/pubmed/29540815 http://dx.doi.org/10.1038/s41598-018-22506-3 |
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author | Yamada, Tatsuya Nishiyama, Makoto Oba, Shigeyuki Jimbo, Henri Claver Ikeda, Kazushi Ishii, Shin Hong, Kyonsoo Sakumura, Yuichi |
author_facet | Yamada, Tatsuya Nishiyama, Makoto Oba, Shigeyuki Jimbo, Henri Claver Ikeda, Kazushi Ishii, Shin Hong, Kyonsoo Sakumura, Yuichi |
author_sort | Yamada, Tatsuya |
collection | PubMed |
description | Biological cells express intracellular biomolecular information to the extracellular environment as various physical responses. We show a novel computational approach to estimate intracellular biomolecular pathways from growth cone electrophysiological responses. Previously, it was shown that cGMP signaling regulates membrane potential (MP) shifts that control the growth cone turning direction during neuronal development. We present here an integrated deterministic mathematical model and Bayesian reversed-engineering framework that enables estimation of the molecular signaling pathway from electrical recordings and considers both the system uncertainty and cell-to-cell variability. Our computational method selects the most plausible molecular pathway from multiple candidates while satisfying model simplicity and considering all possible parameter ranges. The model quantitatively reproduces MP shifts depending on cGMP levels and MP variability potential in different experimental conditions. Lastly, our model predicts that chloride channel inhibition by cGMP-dependent protein kinase (PKG) is essential in the core system for regulation of the MP shifts. |
format | Online Article Text |
id | pubmed-5852145 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-58521452018-03-22 Computational Methods for Estimating Molecular System from Membrane Potential Recordings in Nerve Growth Cone Yamada, Tatsuya Nishiyama, Makoto Oba, Shigeyuki Jimbo, Henri Claver Ikeda, Kazushi Ishii, Shin Hong, Kyonsoo Sakumura, Yuichi Sci Rep Article Biological cells express intracellular biomolecular information to the extracellular environment as various physical responses. We show a novel computational approach to estimate intracellular biomolecular pathways from growth cone electrophysiological responses. Previously, it was shown that cGMP signaling regulates membrane potential (MP) shifts that control the growth cone turning direction during neuronal development. We present here an integrated deterministic mathematical model and Bayesian reversed-engineering framework that enables estimation of the molecular signaling pathway from electrical recordings and considers both the system uncertainty and cell-to-cell variability. Our computational method selects the most plausible molecular pathway from multiple candidates while satisfying model simplicity and considering all possible parameter ranges. The model quantitatively reproduces MP shifts depending on cGMP levels and MP variability potential in different experimental conditions. Lastly, our model predicts that chloride channel inhibition by cGMP-dependent protein kinase (PKG) is essential in the core system for regulation of the MP shifts. Nature Publishing Group UK 2018-03-14 /pmc/articles/PMC5852145/ /pubmed/29540815 http://dx.doi.org/10.1038/s41598-018-22506-3 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Yamada, Tatsuya Nishiyama, Makoto Oba, Shigeyuki Jimbo, Henri Claver Ikeda, Kazushi Ishii, Shin Hong, Kyonsoo Sakumura, Yuichi Computational Methods for Estimating Molecular System from Membrane Potential Recordings in Nerve Growth Cone |
title | Computational Methods for Estimating Molecular System from Membrane Potential Recordings in Nerve Growth Cone |
title_full | Computational Methods for Estimating Molecular System from Membrane Potential Recordings in Nerve Growth Cone |
title_fullStr | Computational Methods for Estimating Molecular System from Membrane Potential Recordings in Nerve Growth Cone |
title_full_unstemmed | Computational Methods for Estimating Molecular System from Membrane Potential Recordings in Nerve Growth Cone |
title_short | Computational Methods for Estimating Molecular System from Membrane Potential Recordings in Nerve Growth Cone |
title_sort | computational methods for estimating molecular system from membrane potential recordings in nerve growth cone |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5852145/ https://www.ncbi.nlm.nih.gov/pubmed/29540815 http://dx.doi.org/10.1038/s41598-018-22506-3 |
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