Efficient parameter search for qualitative models of regulatory networks using symbolic model checking

TL;DR

This paper introduces a symbolic model checking approach to efficiently explore parameter spaces in qualitative biological network models, enabling validation and optimization of network structures against observed data.

Contribution

It presents a novel method that avoids exhaustive parameter enumeration for qualitative models, improving analysis of biological regulatory networks.

Findings

Successfully applied to the IRMA synthetic network

Efficiently tested network-data compatibility

Identified parameter modifications for robustness

Abstract

Investigating the relation between the structure and behavior of complex biological networks often involves posing the following two questions: Is a hypothesized structure of a regulatory network consistent with the observed behavior? And can a proposed structure generate a desired behavior? Answering these questions presupposes that we are able to test the compatibility of network structure and behavior. We cast these questions into a parameter search problem for qualitative models of regulatory networks, in particular piecewise-affine differential equation models. We develop a method based on symbolic model checking that avoids enumerating all possible parametrizations, and show that this method performs well on real biological problems, using the IRMA synthetic network and benchmark experimental data sets. We test the consistency between the IRMA network structure and the time-series data, and search for parameter modifications that would improve the robustness of the external control of the system behavior.