A Compressed Sensing Based Basis-pursuit Formulation of the Room Algorithm

Abstract

Regulatory on off minimization (ROOM) is a popular metabolic modeling strategy for obtaining the fluxes of various metabolic reactions in a mutant. It is based on minimization of the number of flux changes with respect to the wild-type. The ROOM approach involves solving an integer programming problem. In ROOM, the number of integer decision variables is equal to the number of reactions in the metabolic network under consideration. Typically, metabolic networks of interest are genome scale implying that the number of reactions in the network and hence the number of integer decision variables is large. The ROOM approach thus has inherent difficulties associated with large scale integer programming problems. In the current work, motivated by the emerging area of compressed sensing, we propose a reformulation, known as basis-pursuit, of the ROOM algorithm. The proposed formulation is an L1 norm minimization problem and is thus convex in nature. The proposed approach is used to obtain the flux profiles for various mutants of the Synechocystis species strain PCC 6803. The results are compared with the existing ROOM approach. It is observed that the proposed algorithm performs better in most cases. Use of compressed sensing based formulation creates exciting possibilities of efficiently reformulating various other metabolic network analysis problems.