Interaction Models for Biochemical Reactions

This paper presents a stochastic modelling framework for complex biochemical reaction networks from a component-based perspective. Our approach takes into account the discrete character of quantities of participating entities (i.e. the individual populations of the involved chemical species) and the inherent probabilistic nature of microscopic molecular collisions. We collect all instances of one species into one component, hence the size of the state space is reduced considerably. Additionally, the formalism allows for a modular description process and a graphical representation. It may serve as a link between very intuitive but informal models from biology and abstract mathematical models serving as input for analysis tools. We discuss example networks and illustrate the benefits of our approach compared with other compositional modelling formalisms (e.g. the stochastic π-calculus).