Physicochemical and metabolic constraints for thermodynamics-based stoichiometric modelling under mesophilic growth conditions

Tomi-Andrino, Claudio; Norman, Rupert; Millat, Thomas; Soucaille, Philippe; Winzer, Klaus; Barrett, David A.; King, John; Kim, Dong-Hyun

Abstract

© 2021 Tomi-Andrino et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Metabolic engineering in the post-genomic era is characterised by the development of new methods for metabolomics and fluxomics, supported by the integration of genetic engineering tools and mathematical modelling. Particularly, constraint-based stoichiometric models have been widely studied: (i) flux balance analysis (FBA) (in silico), and (ii) metabolic flux analysis (MFA) (in vivo). Recent studies have enabled the incorporation of thermodynamics and metabolomics data to improve the predictive capabilities of these approaches. However, an in-depth comparison and evaluation of these methods is lacking. This study presents a thorough analysis of two different in silico methods tested against experimental data (metabolomics and 13C-MFA) for the mesophile Escherichia coli. In particular, a modified version of the recently published matTFA toolbox was created, providing a broader range of physicochemical parameters. Validating against experimental data allowed the determination of the best physicochemical parameters to perform the TFA (Thermodynamics-based Flux Analysis). An analysis of flux pattern changes in the central carbon metabolism between 13C-MFA and TFA highlighted the limited capabilities of both approaches for elucidating the anaplerotic fluxes. In addition, a method based on centrality measures was suggested to identify important metabolites that (if quantified) would allow to further constrain the TFA. Finally, this study emphasised the need for standardisation in the fluxomics community: novel approaches are frequently released but a thorough comparison with currently accepted methods is not always performed.

Citation

Tomi-Andrino, C., Norman, R., Millat, T., Soucaille, P., Winzer, K., Barrett, D. A., King, J., & Kim, D.-H. (2021). Physicochemical and metabolic constraints for thermodynamics-based stoichiometric modelling under mesophilic growth conditions. PLoS Computational Biology, 17(1), Article e1007694. https://doi.org/10.1371/journal.pcbi.1007694

• Journal Article Type: Article
• Acceptance Date: Dec 28, 2020
• Online Publication Date: Jan 25, 2021
• Publication Date: Jan 25, 2021
• Deposit Date: Feb 5, 2021
• Publicly Available Date: Feb 5, 2021
• Journal: PLoS Computational Biology
• Print ISSN: 1553-734X
• Electronic ISSN: 1553-7358
• Publisher: Public Library of Science
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Issue: 1
• Article Number: e1007694
• DOI: https://doi.org/10.1371/journal.pcbi.1007694
• Keywords: Ecology; Modelling and Simulation; Computational Theory and Mathematics; Genetics; Ecology, Evolution, Behavior and Systematics; Molecular Biology; Cellular and Molecular Neuroscience
• Public URL: https://nottingham-repository.worktribe.com/output/5275831
• Publisher URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007694