A new approach to obtain EFMs using graph methods based on the shortest path between end nodes

Abstract

Genome-scale metabolic networks let us to understand the behavior of the metabolism in the cells of live organisms. The availability of great amounts of such data gives scientific community the opportunity to infer in silico new metabolic knowledge. Elementary Flux Modes (EFM) are minimal contained pathways or subsets of a metabolic network that are very useful to achieve the comprehension of a very specific metabolic function (as well as dis-functions), and to get the knowledge to develop new drugs. Metabolic networks can have large connectivity and, therefore, EFMs resolution faces a combinational explosion challenge to be solved. In this paper we propose a new approach to obtain EFMs based on graph methods and the shortest path between end nodes. Our method finds all the pathways in the metabolic network and it is able to prioritize the pathway search accounting the biological mean pursued. Our technique has two phases, the exploration one and the characterization one, and we show how it works in a well-known case study.