Algebraic and diagrammatic methods for the rule-based modeling of multi-particle complexes

TL;DR

This paper introduces an algebraic and diagrammatic framework for rule-based modeling of multi-particle complexes, unifying statistical physics and computational simulation methods for stochastic chemical systems.

Contribution

It develops a novel operator algebra and diagrammatic methods that support complex assembly/disassembly, bridging existing formalisms for biochemical system modeling.

Findings

Supports creation, annihilation, assembly, and disassembly of complexes

Enables analysis of systems in and out of thermal equilibrium

Provides a stochastic simulation algorithm based on the formalism

Abstract

The formation, dissolution, and dynamics of multi-particle complexes is of fundamental interest in the study of stochastic chemical systems. In 1976, Masao Doi introduced a Fock space formalism for modeling classical particles. Doi's formalism, however, does not support the assembly of multiple particles into complexes. Starting in the 2000's, multiple groups developed rule-based methods for computationally simulating biochemical systems involving large macromolecular complexes. However, these methods are based on graph-rewriting rules and/or process algebras that are mathematically disconnected from the statistical physics methods generally used to analyze equilibrium and nonequilibrium systems. Here we bridge these two approaches by introducing an operator algebra for the rule-based modeling of multi-particle complexes. Our formalism is based on a Fock space that supports not only the creation and annihilation of classical particles, but also the assembly of multiple particles into complexes, as well as the disassembly of complexes into their components. Rules are specified by algebraic operators that act on particles through a manifestation of Wick's theorem. We further describe diagrammatic methods that facilitate rule specification and analytic calculations. We demonstrate our formalism on systems in and out of thermal equilibrium, and for nonequilibrium systems we present a stochastic simulation algorithm based on our formalism. The results provide a unified approach to the mathematical and computational study of stochastic chemical systems in which multi-particle complexes play an important role.