Recursive exploration of metabolic yield space

Genome-scale metabolic network reconstructions contain extremely detailed and valuable information regarding cellular metabolism. For many applications such as finding genetic engineering targets and reduced kinetic model construction, metabolic network analysis techniques exist. Yield spaces based on the extreme rays of solution cones related to the metabolic network are frequently constructed for these types of analyses. However, for genome-scale networks, full enumeration of these extreme rays is not computationally feasible. In this work, a novel direct generation method for yield spaces is presented. This allows the application of many metabolic network analysis techniques to even the most recent genome-scale metabolic networks. Inspired by principles from multi-objective optimization algorithms, the proposed method performs highly efficient recursive exploration but specifically adapted to the mathematical properties of yield spaces. Two case studies showcase both the efficiency of the method and its applicability for analysis of genome-scale metabolic networks.