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Emergent systems energy laws for predicting myosin ensemble processivity.

Egan P, Moore J, Schunn C, Cagan J, LeDuc P - PLoS Comput. Biol. (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Comparison of agent-based molecular simulation and analytical methods to experimental data.(a) A datum isoform (squares) has kon = 900s−1, koff = 1600s−1, and δ+ = 5nm that correspond to empirical measurements [27], whereas extrapolated isoforms have one perturbed parameter each as labeled on the chart (e.g. the “kon = 1500s−1” isoform has values kon = 1500s−1, koff = 1600s−1, and δ+ = 5nm). Each line corresponds to analytical outputs while symbols refer to simulation data, with the exception of solid circles that represent experimental data. (b) vu as myosin isoforms vary for analysis and simulations, with each isoform normalized to one perturbed parameter as other parameters remain constant. The koff perturbation (blue diamonds) has kon = 900s−1, koff = 3500s−1, and δ+ = 10nm, normalization; the kon perturbation (orange rectangles) has kon = 3500s−1, koff = 1000s−1, and δ+ = 10nm normalization; the δ+ perturbation (pink triangles) has kon = 900s−1, koff = 800s−1, and δ+ = 13nm normalization. Experimental data corresponds to the δ+ [14] and koff [33] values.
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pcbi.1004177.g003: Comparison of agent-based molecular simulation and analytical methods to experimental data.(a) A datum isoform (squares) has kon = 900s−1, koff = 1600s−1, and δ+ = 5nm that correspond to empirical measurements [27], whereas extrapolated isoforms have one perturbed parameter each as labeled on the chart (e.g. the “kon = 1500s−1” isoform has values kon = 1500s−1, koff = 1600s−1, and δ+ = 5nm). Each line corresponds to analytical outputs while symbols refer to simulation data, with the exception of solid circles that represent experimental data. (b) vu as myosin isoforms vary for analysis and simulations, with each isoform normalized to one perturbed parameter as other parameters remain constant. The koff perturbation (blue diamonds) has kon = 900s−1, koff = 3500s−1, and δ+ = 10nm, normalization; the kon perturbation (orange rectangles) has kon = 3500s−1, koff = 1000s−1, and δ+ = 10nm normalization; the δ+ perturbation (pink triangles) has kon = 900s−1, koff = 800s−1, and δ+ = 13nm normalization. Experimental data corresponds to the δ+ [14] and koff [33] values.

Mentions: The analytical and simulation models were validated by comparing empirical data [27] for chicken skeletal muscle myosin (kon = 900s−1, koff = 1600s−1, and δ+ = 5nm) under load to isoforms with one configuration variable altered, while the remaining two are identical to chicken skeletal myosin as indicated in Fig 3A. The force-velocity relationship was found analytically through solving 〈f〉 = κ ∙ r ∙ 〈d〉 as described further in the methods section, while the simulated force-velocity relationship was determined through simulating ensemble systems at varied velocities and aggregating to find the time-average force until error was negligible.


Emergent systems energy laws for predicting myosin ensemble processivity.

Egan P, Moore J, Schunn C, Cagan J, LeDuc P - PLoS Comput. Biol. (2015)

Comparison of agent-based molecular simulation and analytical methods to experimental data.(a) A datum isoform (squares) has kon = 900s−1, koff = 1600s−1, and δ+ = 5nm that correspond to empirical measurements [27], whereas extrapolated isoforms have one perturbed parameter each as labeled on the chart (e.g. the “kon = 1500s−1” isoform has values kon = 1500s−1, koff = 1600s−1, and δ+ = 5nm). Each line corresponds to analytical outputs while symbols refer to simulation data, with the exception of solid circles that represent experimental data. (b) vu as myosin isoforms vary for analysis and simulations, with each isoform normalized to one perturbed parameter as other parameters remain constant. The koff perturbation (blue diamonds) has kon = 900s−1, koff = 3500s−1, and δ+ = 10nm, normalization; the kon perturbation (orange rectangles) has kon = 3500s−1, koff = 1000s−1, and δ+ = 10nm normalization; the δ+ perturbation (pink triangles) has kon = 900s−1, koff = 800s−1, and δ+ = 13nm normalization. Experimental data corresponds to the δ+ [14] and koff [33] values.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4401713&req=5

pcbi.1004177.g003: Comparison of agent-based molecular simulation and analytical methods to experimental data.(a) A datum isoform (squares) has kon = 900s−1, koff = 1600s−1, and δ+ = 5nm that correspond to empirical measurements [27], whereas extrapolated isoforms have one perturbed parameter each as labeled on the chart (e.g. the “kon = 1500s−1” isoform has values kon = 1500s−1, koff = 1600s−1, and δ+ = 5nm). Each line corresponds to analytical outputs while symbols refer to simulation data, with the exception of solid circles that represent experimental data. (b) vu as myosin isoforms vary for analysis and simulations, with each isoform normalized to one perturbed parameter as other parameters remain constant. The koff perturbation (blue diamonds) has kon = 900s−1, koff = 3500s−1, and δ+ = 10nm, normalization; the kon perturbation (orange rectangles) has kon = 3500s−1, koff = 1000s−1, and δ+ = 10nm normalization; the δ+ perturbation (pink triangles) has kon = 900s−1, koff = 800s−1, and δ+ = 13nm normalization. Experimental data corresponds to the δ+ [14] and koff [33] values.
Mentions: The analytical and simulation models were validated by comparing empirical data [27] for chicken skeletal muscle myosin (kon = 900s−1, koff = 1600s−1, and δ+ = 5nm) under load to isoforms with one configuration variable altered, while the remaining two are identical to chicken skeletal myosin as indicated in Fig 3A. The force-velocity relationship was found analytically through solving 〈f〉 = κ ∙ r ∙ 〈d〉 as described further in the methods section, while the simulated force-velocity relationship was determined through simulating ensemble systems at varied velocities and aggregating to find the time-average force until error was negligible.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus