Investigating modularity in the analysis of process algebra models of biochemical systems

TL;DR

This paper explores how the inherent compositional structure of process algebra models can be utilized to improve the analysis of biochemical systems, specifically through a case study on yeast pheromone pathways.

Contribution

It investigates the potential of leveraging process algebra compositionality for decomposed solution and analysis in biochemical modeling, highlighting challenges and preliminary findings.

Findings

Decomposition based on process algebra is challenging due to strong component interdependencies.

Using compositional structure can potentially guide more efficient model analysis.

Preliminary case study demonstrates feasibility and benefits of this approach.

Abstract

Compositionality is a key feature of process algebras which is often cited as one of their advantages as a modelling technique. It is certainly true that in biochemical systems, as in many other systems, model construction is made easier in a formalism which allows the problem to be tackled compositionally. In this paper we consider the extent to which the compositional structure which is inherent in process algebra models of biochemical systems can be exploited during model solution. In essence this means using the compositional structure to guide decomposed solution and analysis. Unfortunately the dynamic behaviour of biochemical systems exhibits strong interdependencies between the components of the model making decomposed solution a difficult task. Nevertheless we believe that if such decomposition based on process algebras could be established it would demonstrate substantial benefits for systems biology modelling. In this paper we present our preliminary investigations based on a case study of the pheromone pathway in yeast, modelling in the stochastic process algebra Bio-PEPA.