TY - JOUR
T1 - Emergent Systems Energy Laws for Predicting Myosin Ensemble Processivity
AU - Egan, Paul
AU - Moore, Jeffrey
AU - Schunn, Christian
AU - Cagan, Jonathan
AU - LeDuc, Philip
N1 - Publisher Copyright:
© 2015 Egan et al.
PY - 2015/4/1
Y1 - 2015/4/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84929497887&partnerID=8YFLogxK
U2 - 10.1371/journal.pcbi.1004177
DO - 10.1371/journal.pcbi.1004177
M3 - Article
C2 - 25885169
AN - SCOPUS:84929497887
SN - 1553-734X
VL - 11
JO - PLoS Computational Biology
JF - PLoS Computational Biology
IS - 4
M1 - e1004177
ER -