Mass-balanced randomization of metabolic networks

Motivation: Network-centered studies in systems biology attempt to integrate the topological properties of biological networks with expe-rimental data in order to make predictions and posit hypotheses. For any topology-based prediction, it is necessary to rst assess the signi-cance of the analyzed property in a biologically meaningful context. Therefore, devising network null models, carefully tailored to the topo-logical and biochemical constraints imposed on the network, remains an important computational problem. Results: We rst review the shortcomings of the existing generic sampling schemeswitch randomizationand explain its unsuitabi-lity for application to metabolic networks. We then devise a novel polynomial-time algorithm for randomizing metabolic networks under the (bio)chemical constraint of mass balance. The tractability of our method follows from the concept of mass equivalence classes, de-ned on the representation of compounds in the vector space over che-mical elements. We nally demonstrate the uniformity of the proposed method on seven genome-scale metabolic networks, and empirically validate the theoretical ndings. The proposed method allows a bio-logically meaningful estimation of signicance for metabolic network properties. Contact