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Reaction Event Counting Statistics of Biopolymer Reaction Systems with Dynamic Heterogeneity.

Yu Rim Lim¹, Seong Jun Park¹, Bo Jung Park¹

¹Department of Chemistry, Chung-Ang University, Seoul 156-756, Korea.

Journal of Chemical Theory and Computation

|November 25, 2015

Summary

This study reveals a kinetic phase transition in biopolymer reaction statistics. Reaction event counting statistics shift from non-renewal to renewal behavior as measurement time increases, offering insights into fluctuating reaction rates.

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Published on: September 26, 2016

Area of Science:

Biophysics
Chemical Kinetics
Statistical Mechanics

Background:

Biopolymer reactions exhibit complex kinetics influenced by internal states and environment.
Reaction rate fluctuations introduce deviations from standard kinetic models.

Purpose of the Study:

Investigate reaction event counting statistics (RECS) in elementary biopolymer reactions.
Identify and characterize kinetic phase transitions in RECS due to dynamic heterogeneity.
Develop a method to analyze fluctuating reaction rate coefficients.

Main Methods:

Exact analytical analysis of a general biopolymer reaction model.
Generalization of Gillespie's stochastic simulation method for fluctuating rates.
Establishment of relationships between reaction event number distribution moments and rate correlations.

Main Results:

A universal kinetic phase transition in RECS was discovered.
Variance-mean relationships in RECS depend on the ratio of measurement time to relaxation time.
Simulation results validate analytical predictions for mean and variance.

Conclusions:

RECS exhibit distinct behaviors (non-renewal vs. renewal) based on measurement timescale.
A novel quantitative analysis method can extract information on rate coefficient fluctuations without model bias.
Exact relationships link distribution moments to multitime rate correlations for various initial states.