Identification of conserved moieties in metabolic networks by graph theoretical analysis of atom transition networks

TL;DR

This paper introduces a graph theoretical method for identifying conserved atomic groups in large-scale metabolic networks, providing exact moiety structures and enabling scalable analysis beyond previous computational limits.

Contribution

A novel polynomial-time algorithm that accurately identifies conserved moieties with atomic detail in genome-scale metabolic networks, surpassing previous methods limited by complexity.

Findings

Method completes in under five minutes for networks with over 4,000 reactions.

Enables analysis of large-scale metabolic networks for moiety conservation.

Facilitates new applications by elucidating atomic structures of conserved groups.

Abstract

Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network. Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling. All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network. Algorithms exist to compute such vectors based only on reaction stoichiometry but their computational complexity has limited their application to relatively small metabolic networks. Moreover, the vectors returned by existing algorithms do not, in general, represent conservation of a specific moiety with a defined atomic structure. Here, we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry. We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks. Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix. It can be implemented as a pipeline of polynomial time algorithms. Our implementation completes in under five minutes on a metabolic network with more than 4,000 mass balanced reactions. The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks. We also give examples of new applications made possible by elucidating the atomic structure of conserved moieties.