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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

Master curve that predicts processive lifetime of ensembles composed of many different myosin isoforms.Processive lifetimes for isoforms when adjusted system energy consumption E* varies. Isoforms are identical to Fig 6A, except for additional low (δ+ = 5nm, kon = 1000s−1, and koff = 1500s−1) and high (δ+ = 15nm, kon = 3000s−1, and koff = 3500s−1) isoforms, which demonstrate the master curve holds as multiple myosin parameters are altered. The master curve analytically captures the overall response predicted through the unified expression , with A ≈ 14.5 and B ≈ 4.5(10−4). Here, processive lifetime is predictable regardless of individual myosin configuration and the master curve asymptotes are indicative of energy thresholds for perpetual processivity.
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pcbi.1004177.g007: Master curve that predicts processive lifetime of ensembles composed of many different myosin isoforms.Processive lifetimes for isoforms when adjusted system energy consumption E* varies. Isoforms are identical to Fig 6A, except for additional low (δ+ = 5nm, kon = 1000s−1, and koff = 1500s−1) and high (δ+ = 15nm, kon = 3000s−1, and koff = 3500s−1) isoforms, which demonstrate the master curve holds as multiple myosin parameters are altered. The master curve analytically captures the overall response predicted through the unified expression , with A ≈ 14.5 and B ≈ 4.5(10−4). Here, processive lifetime is predictable regardless of individual myosin configuration and the master curve asymptotes are indicative of energy thresholds for perpetual processivity.

Mentions: When determining E* from Fig 6B simulation results are representative of ensembles with processive lifetimes of approximately 500ms. All isoforms have nearly identical E* while having vastly different contact probabilities, thus suggesting that E* is a viable predictor of processive lifetime (Supplementary Section 3 in S1 Text). Therefore, the unified expression that fits the simulation data is possible to express as . When simulation results from Fig 6A are reconsidered with E*, there is strong agreement among all isoform types adhering to one master curve (Fig 7). Coefficients were fit to the median isoform in Fig 7, resulting in A ≈ 14.5 and B ≈ 4.5(10−4) and even hold as multiple parameters of isoforms are varied simultaneously (as represented by “low” and “high” isoform configurations in Fig 7).


Emergent systems energy laws for predicting myosin ensemble processivity.

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

Master curve that predicts processive lifetime of ensembles composed of many different myosin isoforms.Processive lifetimes for isoforms when adjusted system energy consumption E* varies. Isoforms are identical to Fig 6A, except for additional low (δ+ = 5nm, kon = 1000s−1, and koff = 1500s−1) and high (δ+ = 15nm, kon = 3000s−1, and koff = 3500s−1) isoforms, which demonstrate the master curve holds as multiple myosin parameters are altered. The master curve analytically captures the overall response predicted through the unified expression , with A ≈ 14.5 and B ≈ 4.5(10−4). Here, processive lifetime is predictable regardless of individual myosin configuration and the master curve asymptotes are indicative of energy thresholds for perpetual processivity.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004177.g007: Master curve that predicts processive lifetime of ensembles composed of many different myosin isoforms.Processive lifetimes for isoforms when adjusted system energy consumption E* varies. Isoforms are identical to Fig 6A, except for additional low (δ+ = 5nm, kon = 1000s−1, and koff = 1500s−1) and high (δ+ = 15nm, kon = 3000s−1, and koff = 3500s−1) isoforms, which demonstrate the master curve holds as multiple myosin parameters are altered. The master curve analytically captures the overall response predicted through the unified expression , with A ≈ 14.5 and B ≈ 4.5(10−4). Here, processive lifetime is predictable regardless of individual myosin configuration and the master curve asymptotes are indicative of energy thresholds for perpetual processivity.
Mentions: When determining E* from Fig 6B simulation results are representative of ensembles with processive lifetimes of approximately 500ms. All isoforms have nearly identical E* while having vastly different contact probabilities, thus suggesting that E* is a viable predictor of processive lifetime (Supplementary Section 3 in S1 Text). Therefore, the unified expression that fits the simulation data is possible to express as . When simulation results from Fig 6A are reconsidered with E*, there is strong agreement among all isoform types adhering to one master curve (Fig 7). Coefficients were fit to the median isoform in Fig 7, resulting in A ≈ 14.5 and B ≈ 4.5(10−4) and even hold as multiple parameters of isoforms are varied simultaneously (as represented by “low” and “high” isoform configurations in Fig 7).

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