Emergent Systems Energy Laws for Predicting Myosin Ensemble Processivity

Paul Egan, Jeffrey Moore, Christian Schunn, Jonathan Cagan, Philip LeDuc

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


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.

Original languageEnglish
Article numbere1004177
JournalPLoS Computational Biology
Issue number4
StatePublished - Apr 1 2015


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