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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

Stochasticity affects velocity and attachment among myosins and filaments for varied ensemble sizes.Histograms for simulated ensembles of (a) N = 25 myosins and (b) N = 100 myosins when δ+ = 10nm, kon = 900s−1, and koff = 1600s−1 as the number of attached myosins are counted for 1000 random samplings (c) The normalized unloaded filament velocity vu for the isoform from “a” when considering ensemble size for analytical predictions (N = 60 myosins), simulation (N = 60 myosins), and empirical data (normalized to 100μg/mL concentration of chicken skeletal myosins added to a flowcell). (d) Analytical curve of contact probability PC and average number of attached myosins Natt for a median isoform of δ+ = 10nm, kon = 2000s−1, and koff = 2500s-1 as ensemble size varies. All simulated isoforms are identical to the median except for one perturbed parameter as indicated in the key, with all outputs collapsing on a single curve. Therefore, systems have nearly identical contact probabilities for a given number of attached myosins, independent of ensemble size or isoform configuration.
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pcbi.1004177.g004: Stochasticity affects velocity and attachment among myosins and filaments for varied ensemble sizes.Histograms for simulated ensembles of (a) N = 25 myosins and (b) N = 100 myosins when δ+ = 10nm, kon = 900s−1, and koff = 1600s−1 as the number of attached myosins are counted for 1000 random samplings (c) The normalized unloaded filament velocity vu for the isoform from “a” when considering ensemble size for analytical predictions (N = 60 myosins), simulation (N = 60 myosins), and empirical data (normalized to 100μg/mL concentration of chicken skeletal myosins added to a flowcell). (d) Analytical curve of contact probability PC and average number of attached myosins Natt for a median isoform of δ+ = 10nm, kon = 2000s−1, and koff = 2500s-1 as ensemble size varies. All simulated isoforms are identical to the median except for one perturbed parameter as indicated in the key, with all outputs collapsing on a single curve. Therefore, systems have nearly identical contact probabilities for a given number of attached myosins, independent of ensemble size or isoform configuration.

Mentions: The analytical and simulation models are both extendible to predicting stochastic ensemble behavior, such as determining the probability that at least one myosin in the system is attached to actin, which is necessary for ensuring the system operates with a consistent trajectory and does not dissociate [27]. The contact probability PC that describes the percentage of time that at least one myosin is attached to actin is used to find an adjusted unloaded filament velocity . To determine the simulated PC, Monte Carlo methods were used to count the number of attached myosins during run-time. Fig 4A and 4B are histograms for ensembles of N = 25 myosins and N = 100 myosins, demonstrating that occurrences obey a Poisson distribution, and there is a much lower chance of no myosins being attached as N increases.


Emergent systems energy laws for predicting myosin ensemble processivity.

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

Stochasticity affects velocity and attachment among myosins and filaments for varied ensemble sizes.Histograms for simulated ensembles of (a) N = 25 myosins and (b) N = 100 myosins when δ+ = 10nm, kon = 900s−1, and koff = 1600s−1 as the number of attached myosins are counted for 1000 random samplings (c) The normalized unloaded filament velocity vu for the isoform from “a” when considering ensemble size for analytical predictions (N = 60 myosins), simulation (N = 60 myosins), and empirical data (normalized to 100μg/mL concentration of chicken skeletal myosins added to a flowcell). (d) Analytical curve of contact probability PC and average number of attached myosins Natt for a median isoform of δ+ = 10nm, kon = 2000s−1, and koff = 2500s-1 as ensemble size varies. All simulated isoforms are identical to the median except for one perturbed parameter as indicated in the key, with all outputs collapsing on a single curve. Therefore, systems have nearly identical contact probabilities for a given number of attached myosins, independent of ensemble size or isoform configuration.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4401713&req=5

pcbi.1004177.g004: Stochasticity affects velocity and attachment among myosins and filaments for varied ensemble sizes.Histograms for simulated ensembles of (a) N = 25 myosins and (b) N = 100 myosins when δ+ = 10nm, kon = 900s−1, and koff = 1600s−1 as the number of attached myosins are counted for 1000 random samplings (c) The normalized unloaded filament velocity vu for the isoform from “a” when considering ensemble size for analytical predictions (N = 60 myosins), simulation (N = 60 myosins), and empirical data (normalized to 100μg/mL concentration of chicken skeletal myosins added to a flowcell). (d) Analytical curve of contact probability PC and average number of attached myosins Natt for a median isoform of δ+ = 10nm, kon = 2000s−1, and koff = 2500s-1 as ensemble size varies. All simulated isoforms are identical to the median except for one perturbed parameter as indicated in the key, with all outputs collapsing on a single curve. Therefore, systems have nearly identical contact probabilities for a given number of attached myosins, independent of ensemble size or isoform configuration.
Mentions: The analytical and simulation models are both extendible to predicting stochastic ensemble behavior, such as determining the probability that at least one myosin in the system is attached to actin, which is necessary for ensuring the system operates with a consistent trajectory and does not dissociate [27]. The contact probability PC that describes the percentage of time that at least one myosin is attached to actin is used to find an adjusted unloaded filament velocity . To determine the simulated PC, Monte Carlo methods were used to count the number of attached myosins during run-time. Fig 4A and 4B are histograms for ensembles of N = 25 myosins and N = 100 myosins, demonstrating that occurrences obey a Poisson distribution, and there is a much lower chance of no myosins being attached as N increases.

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