Thermodynamic Constraints Improve Metabolic Networks

Elias W. Krumholz, Igor G Libourel

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

In pursuit of establishing a realistic metabolic phenotypic space, the reversibility of reactions is thermodynamically constrained in modern metabolic networks. The reversibility constraints follow from heuristic thermodynamic poise approximations that take anticipated cellular metabolite concentration ranges into account. Because constraints reduce the feasible space, draft metabolic network reconstructions may need more extensive reconciliation, and a larger number of genes may become essential. Notwithstanding ubiquitous application, the effect of reversibility constraints on the predictive capabilities of metabolic networks has not been investigated in detail. Instead, work has focused on the implementation and validation of the thermodynamic poise calculation itself. With the advance of fast linear programming-based network reconciliation, the effects of reversibility constraints on network reconciliation and gene essentiality predictions have become feasible and are the subject of this study. Networks with thermodynamically informed reversibility constraints outperformed gene essentiality predictions compared to networks that were constrained with randomly shuffled constraints. Unconstrained networks predicted gene essentiality as accurately as thermodynamically constrained networks, but predicted substantially fewer essential genes. Networks that were reconciled with sequence similarity data and strongly enforced reversibility constraints outperformed all other networks. We conclude that metabolic network analysis confirmed the validity of the thermodynamic constraints, and that thermodynamic poise information is actionable during network reconciliation.

Original languageEnglish (US)
Pages (from-to)679-689
Number of pages11
JournalBiophysical journal
Volume113
Issue number3
DOIs
StatePublished - Aug 8 2017

Bibliographical note

Funding Information:
This work was funded by seed funding of the Biotechnology Institute at the University of Minnesota, the Office of Naval Research under award N141310552, and the National Science Foundation (NSF) award NSF/MCB-1042335.

Publisher Copyright:
© 2017 Biophysical Society

Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.

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