Assessing the impact of physicochemical parameters in the predictive capabilities of thermodynamics-based stoichiometric approaches under mesophilic and thermophilic conditions

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 four different in silico methods tested against experimental data (metabolomics and 13C-MFA) for the mesophile Escherichia coli and the thermophile Thermus thermophilus. In particular, a modified version of the recently published matTFA toolbox has been created, providing a broader range of physicochemical parameters. In addition, a max-min driving force approach (as implemented in eQuilibrator) was also performed in order to compare the predictive capabilities of both methods. Validating against experimental data allowed the determination of the best physicochemical parameters to perform the TFA for E. coli, whereas the lack of metabolomics data for T. thermophilus prevented from a full analysis. Results showed that analytical conditions predicting reliable flux distributions (similar to the in vivo fluxes) do not necessarily provide a good depiction of the experimental metabolomics landscape, and that the original matTFA toolbox can be improved. 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. Finally, this study highlights 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. Author summaryBiotechnology has benefitted from the development of high throughput methods characterising living systems at different levels (e.g. concerning genes or proteins), allowing the industrial production of chemical commodities (such as ethylene). Recently, focus has been put on determining reaction rates (or metabolic fluxes) in the metabolic network of certain microorganisms, in order to identify bottlenecks hindering their exploitation. Two main approaches can be highlighted, termed metabolic flux analysis (MFA) and flux balance analysis (FBA), based on measuring and estimating fluxes, respectively. While the influence of thermodynamics in living systems was accepted several decades ago, its application to study biochemical networks has been only recently enabled. In this sense, a multitude of different approaches constraining well-established modelling methods with thermodynamics has been suggested. However, physicochemical parameters are not properly adjusted to the experimental conditions, which might affect their predictive capabilities. In this study, we improved the reliability of currently available tools by exploring the impact of varying said parameters in the simulation of metabolic fluxes and metabolite concentration values. Additionally, our in-depth analysis allowed us to highlight limitations and potential solutions that should be considered in future studies.