Rule-based Modeling and Simulation of Biochemical Systems with Molecular Finite Automata

TL;DR

This paper introduces molecular finite automata (MFA), a formalism for modeling proteins as rule-based machines, enabling detailed simulation of biochemical systems' dynamics at the molecular level.

Contribution

The paper presents a novel formalism, MFA, for representing and simulating protein behaviors and interactions as programmable automata, bridging rule-based modeling and system-level dynamics.

Findings

MFA can model context-sensitive protein dynamics.

Both deterministic and stochastic simulations are feasible.

Application to MAP kinase cascade demonstrates MFA's utility.

Abstract

We propose a theoretical formalism, molecular finite automata (MFA), to describe individual proteins as rule-based computing machines. The MFA formalism provides a framework for modeling individual protein behaviors and systems-level dynamics via construction of programmable and executable machines. Models specified within this formalism explicitly represent the context-sensitive dynamics of individual proteins driven by external inputs and represent protein-protein interactions as synchronized machine reconfigurations. Both deterministic and stochastic simulations can be applied to quantitatively compute the dynamics of MFA models. We apply the MFA formalism to model and simulate a simple example of a signal transduction system that involves a MAP kinase cascade and a scaffold protein.