Kinetic Monte Carlo Method for Rule-based Modeling of Biochemical Networks

TL;DR

This paper introduces a kinetic Monte Carlo method for simulating biochemical networks based on reaction rules, enabling efficient modeling of complex systems without enumerating all reactions.

Contribution

The paper presents a novel rule-based kinetic Monte Carlo simulation approach that reduces computational cost by not requiring pre-specification of all reactions.

Findings

Efficient simulation of ligand-receptor kinetics

Applicable to cellular signaling and aggregation phenomena

Cost independent of reaction network size

Abstract

We present a kinetic Monte Carlo method for simulating chemical transformations specified by reaction rules, which can be viewed as generators of chemical reactions, or equivalently, definitions of reaction classes. A rule identifies the molecular components involved in a transformation, how these components change, conditions that affect whether a transformation occurs, and a rate law. The computational cost of the method, unlike conventional simulation approaches, is independent of the number of possible reactions, which need not be specified in advance or explicitly generated in a simulation. To demonstrate the method, we apply it to study the kinetics of multivalent ligand-receptor interactions. We expect the method will be useful for studying cellular signaling systems and other physical systems involving aggregation phenomena.