A mixed-integer linear programming approach to the reduction of genome-scale metabolic networks

Abstract

Constraint-based analysis has become a widely used method to study metabolic networks. While some of the associated algorithms can be applied to genome- scale network reconstructions with several thousands of reactions, others are limited to small or medium-sized models. In 2015, Erdrich et al. introduced a method called NetworkReducer, which reduces large metabolic networks to smaller subnetworks, while preserving a set of biological requirements that can be specified by the user. Already in 2001, Burgard et al. developed a mixed-integer linear programming (MILP) approach for computing minimal reaction sets under a given growth requirement.