An Abstraction Theory for Qualitative Models of Biological Systems

TL;DR

This paper develops an abstraction theory for multi-valued network models in biology, enabling state space reduction while preserving key properties, thus aiding analysis and automation of model simplification.

Contribution

It introduces a formal abstraction framework for multi-valued networks and techniques for efficient identification of such abstractions, facilitating model analysis and comparison.

Findings

Successfully identified abstractions for biological models

Reduced state space complexity in case studies

Provided automated techniques for abstraction identification

Abstract

Multi-valued network models are an important qualitative modelling approach used widely by the biological community. In this paper we consider developing an abstraction theory for multi-valued network models that allows the state space of a model to be reduced while preserving key properties of the model. This is important as it aids the analysis and comparison of multi-valued networks and in particular, helps address the well-known problem of state space explosion associated with such analysis. We also consider developing techniques for efficiently identifying abstractions and so provide a basis for the automation of this task. We illustrate the theory and techniques developed by investigating the identification of abstractions for two published MVN models of the lysis-lysogeny switch in the bacteriophage lambda.