Literature DB >> 33493151

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

Claudio Tomi-Andrino1,2,3, Rupert Norman2, Thomas Millat2, Philippe Soucaille2,4,5,6, Klaus Winzer2, David A Barrett1, John King3, Dong-Hyun Kim1.   

Abstract

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.

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Year:  2021        PMID: 33493151      PMCID: PMC7861524          DOI: 10.1371/journal.pcbi.1007694

Source DB:  PubMed          Journal:  PLoS Comput Biol        ISSN: 1553-734X            Impact factor:   4.475


  67 in total

1.  Thermodynamic constraints for biochemical networks.

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5.  Synchronization of Escherichia coli in a chemostat by periodic phosphate feeding.

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Journal:  PLoS Comput Biol       Date:  2019-05-13       Impact factor: 4.475

Review 10.  Nitrogen assimilation in Escherichia coli: putting molecular data into a systems perspective.

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Journal:  Microbiol Mol Biol Rev       Date:  2013-12       Impact factor: 11.056

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  1 in total

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  1 in total

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