Identifying all moiety conservation laws in genome-scale metabolic networks.

Andrea De Martino,Andrea Pagnani,Daniele De Martino,Roberto Mulet

doi:10.1371/journal.pone.0100750

Abstract

The stoichiometry of a metabolic network gives rise to a set of conservation laws for the aggregate level of specific pools of metabolites, which, on one hand, pose dynamical constraints that cross-link the variations of metabolite concentrations and, on the other, provide key insight into a cell's metabolic production capabilities. When the conserved quantity identifies with a chemical moiety, extracting all such conservation laws from the stoichiometry amounts to finding all non-negative integer solutions of a linear system, a programming problem known to be NP-hard. We present an efficient strategy to compute the complete set of integer conservation laws of a genome-scale stoichiometric matrix, also providing a certificate for correctness and maximality of the solution. Our method is deployed for the analysis of moiety conservation relationships in two large-scale reconstructions of the metabolism of the bacterium E. coli, in six tissue-specific human metabolic networks, and, finally, in the human reactome as a whole, revealing that bacterial metabolism could be evolutionarily designed to cover broader production spectra than human metabolism. Convergence to the full set of moiety conservation laws in each case is achieved in extremely reduced computing times. In addition, we uncover a scaling relation that links the size of the independent pool basis to the number of metabolites, for which we present an analytical explanation.

Highlights

When studying metabolic networks at the scale of the whole genome, it is often the case that the information required to develop dynamical models is not available, because either kinetic parameters or reaction mechanisms are partially or fully unknown
We have considered different reconstructions of the metabolic network of the bacterium E. coli and six tissue-specific human metabolic networks derived from the Recon-2 database [32]
moiety conservation laws’ (MCLs) based on a tRNA reflect the fact that, in the model where they have been found, the expression of each tRNA is necessarily constant

Summary

Introduction

When studying metabolic networks at the scale of the whole genome, it is often the case that the information required to develop dynamical models is not available, because either kinetic parameters or reaction mechanisms are partially or fully unknown. The full solution of the problems just listed on genome-scale networks with thousands of reactions and metabolites presents serious computational challenges, as the algorithms currently available do not scale gently with the system size. This is the case for the identification of moiety conservation laws that we shall consider here [10,21,22]

Methods

Results

Conclusion