Authors: Mark J. Willis and Sam Barker
The inference of mathematical descriptions of (bio)chemical reaction networks from experimental data is a challenging problem whose solution would have significant commercial and academic impact. In this paper, integer linear programming (ILP) is used to develop a generic framework to recover sets of reactions comprising a chemical reaction network. Network search using ILP promotes sparse connectivity, incorporates elemental mass conservation and integrates both heuristic and experimentally identified structural properties using linear equality and inequality constraints. Two case studies are used to demonstrate the discovery of plausible chemical reaction network structures using ILP, the validity of which are verified by kinetic model identification studies.
Keywords: reaction network, optimisation, modelling, kinetic fitting.