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Emergent Systems Energy Laws for Predicting Myosin Ensemble Processivity
In complex systems with stochastic components, systems laws often emerge that describe higher level behavior regardless of lower level component configurations. In this paper, emergent laws for describing mechanochemical systems are investigated for processive myosin-actin motility systems. On the b...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4401713/ https://www.ncbi.nlm.nih.gov/pubmed/25885169 http://dx.doi.org/10.1371/journal.pcbi.1004177 |
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author | Egan, Paul Moore, Jeffrey Schunn, Christian Cagan, Jonathan LeDuc, Philip |
author_facet | Egan, Paul Moore, Jeffrey Schunn, Christian Cagan, Jonathan LeDuc, Philip |
author_sort | Egan, Paul |
collection | PubMed |
description | In complex systems with stochastic components, systems laws often emerge that describe higher level behavior regardless of lower level component configurations. In this paper, emergent laws for describing mechanochemical systems are investigated for processive myosin-actin motility systems. On the basis of prior experimental evidence that longer processive lifetimes are enabled by larger myosin ensembles, it is hypothesized that emergent scaling laws could coincide with myosin-actin contact probability or system energy consumption. Because processivity is difficult to predict analytically and measure experimentally, agent-based computational techniques are developed to simulate processive myosin ensembles and produce novel processive lifetime measurements. It is demonstrated that only systems energy relationships hold regardless of isoform configurations or ensemble size, and a unified expression for predicting processive lifetime is revealed. The finding of such laws provides insight for how patterns emerge in stochastic mechanochemical systems, while also informing understanding and engineering of complex biological systems. |
format | Online Article Text |
id | pubmed-4401713 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-44017132015-04-21 Emergent Systems Energy Laws for Predicting Myosin Ensemble Processivity Egan, Paul Moore, Jeffrey Schunn, Christian Cagan, Jonathan LeDuc, Philip PLoS Comput Biol Research Article In complex systems with stochastic components, systems laws often emerge that describe higher level behavior regardless of lower level component configurations. In this paper, emergent laws for describing mechanochemical systems are investigated for processive myosin-actin motility systems. On the basis of prior experimental evidence that longer processive lifetimes are enabled by larger myosin ensembles, it is hypothesized that emergent scaling laws could coincide with myosin-actin contact probability or system energy consumption. Because processivity is difficult to predict analytically and measure experimentally, agent-based computational techniques are developed to simulate processive myosin ensembles and produce novel processive lifetime measurements. It is demonstrated that only systems energy relationships hold regardless of isoform configurations or ensemble size, and a unified expression for predicting processive lifetime is revealed. The finding of such laws provides insight for how patterns emerge in stochastic mechanochemical systems, while also informing understanding and engineering of complex biological systems. Public Library of Science 2015-04-17 /pmc/articles/PMC4401713/ /pubmed/25885169 http://dx.doi.org/10.1371/journal.pcbi.1004177 Text en © 2015 Egan 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, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Egan, Paul Moore, Jeffrey Schunn, Christian Cagan, Jonathan LeDuc, Philip Emergent Systems Energy Laws for Predicting Myosin Ensemble Processivity |
title | Emergent Systems Energy Laws for Predicting Myosin Ensemble Processivity |
title_full | Emergent Systems Energy Laws for Predicting Myosin Ensemble Processivity |
title_fullStr | Emergent Systems Energy Laws for Predicting Myosin Ensemble Processivity |
title_full_unstemmed | Emergent Systems Energy Laws for Predicting Myosin Ensemble Processivity |
title_short | Emergent Systems Energy Laws for Predicting Myosin Ensemble Processivity |
title_sort | emergent systems energy laws for predicting myosin ensemble processivity |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4401713/ https://www.ncbi.nlm.nih.gov/pubmed/25885169 http://dx.doi.org/10.1371/journal.pcbi.1004177 |
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