Limits...
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

Trends among isoform variations for simulated processive lifetimes and ensemble energy usage.(a) Simulation measurements of processive lifetime  and contact probability PC when ensemble size N varies. Isoforms include a median (red with δ+ = 10nm, kon = 2000s−1, and koff = 2500s−1), with other isoforms having one perturbed parameter as indicated, therefore higher contact probabilities lead to longer , and lower detachment rates koff lead to higher processive lifetimes for a given contact probability. (b) The minimum average system energy consumption E required for . Isoforms are all perturbed from a configuration where parameters are half of their normalized value; isoforms of higher koff require more energy to reach the same  for a given PC.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4401713&req=5

pcbi.1004177.g006: Trends among isoform variations for simulated processive lifetimes and ensemble energy usage.(a) Simulation measurements of processive lifetime and contact probability PC when ensemble size N varies. Isoforms include a median (red with δ+ = 10nm, kon = 2000s−1, and koff = 2500s−1), with other isoforms having one perturbed parameter as indicated, therefore higher contact probabilities lead to longer , and lower detachment rates koff lead to higher processive lifetimes for a given contact probability. (b) The minimum average system energy consumption E required for . Isoforms are all perturbed from a configuration where parameters are half of their normalized value; isoforms of higher koff require more energy to reach the same for a given PC.

Mentions: An investigation of many different ensemble sizes and isoform configurations was conducted using the simulation to measure processive lifetime of varied isoform configurations (Supplementary Movie 5 in S1 Text). A significant body of data was collected, beginning with the simulation of a median isoform configuration (kon = 2000s−1, koff = 2500s−1, and δ+ = 10nm) with an ensemble size N of 10 while myosin and system behaviors were recorded from the simulation. The ensemble size of the system was increased until the average processive lifetime exceeded 1s (higher values of processive lifetime began approaching perpetually processive systems that required extensive computational effort). The process was repeated for isoforms that varied by one input variable in comparison to the median isoform (e.g. an isoform with extrapolated kon = 1000s−1 represented an isoform of kon = 1000s−1, koff = 2500s−1, and δ+ = 10nm). The extrapolation of one isoform variable from the median isoform enabled a controlled basis of comparison to determine how each myosin isoform configuration input affects ensemble energy and processivity behavior. Isoforms were extrapolated one at a time to produce seven curves that represented how each ensemble's contact probability PC corresponded to its processive lifetime in Fig 6A.


Emergent systems energy laws for predicting myosin ensemble processivity.

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

Trends among isoform variations for simulated processive lifetimes and ensemble energy usage.(a) Simulation measurements of processive lifetime  and contact probability PC when ensemble size N varies. Isoforms include a median (red with δ+ = 10nm, kon = 2000s−1, and koff = 2500s−1), with other isoforms having one perturbed parameter as indicated, therefore higher contact probabilities lead to longer , and lower detachment rates koff lead to higher processive lifetimes for a given contact probability. (b) The minimum average system energy consumption E required for . Isoforms are all perturbed from a configuration where parameters are half of their normalized value; isoforms of higher koff require more energy to reach the same  for a given PC.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004177.g006: Trends among isoform variations for simulated processive lifetimes and ensemble energy usage.(a) Simulation measurements of processive lifetime and contact probability PC when ensemble size N varies. Isoforms include a median (red with δ+ = 10nm, kon = 2000s−1, and koff = 2500s−1), with other isoforms having one perturbed parameter as indicated, therefore higher contact probabilities lead to longer , and lower detachment rates koff lead to higher processive lifetimes for a given contact probability. (b) The minimum average system energy consumption E required for . Isoforms are all perturbed from a configuration where parameters are half of their normalized value; isoforms of higher koff require more energy to reach the same for a given PC.
Mentions: An investigation of many different ensemble sizes and isoform configurations was conducted using the simulation to measure processive lifetime of varied isoform configurations (Supplementary Movie 5 in S1 Text). A significant body of data was collected, beginning with the simulation of a median isoform configuration (kon = 2000s−1, koff = 2500s−1, and δ+ = 10nm) with an ensemble size N of 10 while myosin and system behaviors were recorded from the simulation. The ensemble size of the system was increased until the average processive lifetime exceeded 1s (higher values of processive lifetime began approaching perpetually processive systems that required extensive computational effort). The process was repeated for isoforms that varied by one input variable in comparison to the median isoform (e.g. an isoform with extrapolated kon = 1000s−1 represented an isoform of kon = 1000s−1, koff = 2500s−1, and δ+ = 10nm). The extrapolation of one isoform variable from the median isoform enabled a controlled basis of comparison to determine how each myosin isoform configuration input affects ensemble energy and processivity behavior. Isoforms were extrapolated one at a time to produce seven curves that represented how each ensemble's contact probability PC corresponded to its processive lifetime in Fig 6A.

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