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Emergent Systems Energy Laws for Predicting Myosin Ensemble Processivity

Egan, P and Moore, J and Schunn, C and Cagan, J and LeDuc, P (2015) Emergent Systems Energy Laws for Predicting Myosin Ensemble Processivity. PLoS Computational Biology, 11 (4). ISSN 1553-734X

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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.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Egan, P
Moore, J
Schunn, Cschunn@pitt.eduSCHUNN0000-0003-3589-297X
Cagan, J
LeDuc, P
ContributionContributors NameEmailPitt UsernameORCID
Date: 1 April 2015
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: PLoS Computational Biology
Volume: 11
Number: 4
DOI or Unique Handle: 10.1371/journal.pcbi.1004177
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Psychology
Refereed: Yes
ISSN: 1553-734X
Date Deposited: 23 Aug 2016 13:42
Last Modified: 09 Nov 2021 13:55


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