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Parallel out-of-core algorithm for genome-scale enumeration of metabolic systemic pathways

Systemic pathways-oriented approaches to analysis of metabolic networks are effective for small networks but are computationally infeasible for genome scale networks. Current computational approaches to this analysis are based on the mathematical principles of convex analysis. The enumeration of a complete set of "systemically independent" metabolic pathways is at the core of these approaches and it is computationally the most demanding component. An efficient parallel out-of-core algorithm for generating a complete set of systemically independent metabolic pathways, termed "extreme pathways", is presented. These pathways represent the edges of a high-dimensional convex cone and can be used to derive any admissible steady-state flux distribution (or phenotype) for a specified metabolic genotype. The algorithm can be used for computing "elementary flux modes" that are different but closely related to extreme pathways. The algorithm combines a bitmap data representation, search space reduction, and out-of-core implementation to improve CPU-time and memory requirements by several orders of magnitude. Augmented with a parallel implementation, it provides extremely scalable performance. No previous parallel and/or out-of-core algorithms for the enumeration of systemically defined metabolic pathways are known.