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In silico identification of switching nodes in metabolic networksuse asterix (*) to get italics
Francis MairetPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"
2024
<p>Cells modulate their metabolism according to environmental conditions. A major challenge to better understand metabolic regulation is to identify, from the hundreds or thousands of molecules, the key metabolites where the re-orientation of fluxes occurs. Here, a method called ISIS (for In Silico Identification of Switches) is proposed to locate these nodes in a metabolic network, based on the analysis of a set of flux vectors (obtained e.g. by parsimonious flux balance analysis with different inputs). A metabolite is considered as a switch if the fluxes at this point are redirected in a different way when conditions change. The soundness of ISIS is shown with four case studies, using both core and genome-scale metabolic networks of <em>Escherichia coli</em>, <em>Saccharomyces cerevisiae</em> and the diatom<em> Phaeodactylum tricornutum</em>. Through these examples, we show that ISIS can identify hot-spots where fluxes are reoriented. Additionally, switch metabolites are deeply involved in post-translational modification of proteins, showing their importance in cellular regulation. In <em>P. tricornutum</em>, we show that Erythrose 4-phosphate is an important switch metabolite for mixotrophy suggesting the importance of this metabolite in the non-oxidative pentose phosphate pathway to orchestrate the flux variations between glycolysis, the Calvin cycle and the oxidative pentose phosphate pathway when the trophic mode changes. Finally, a comparison between ISIS and reporter metabolites identified with transcriptomic data confirms the key role of metabolites such as L-glutamate or L-aspartate in the yeast response to nitrogen input variation. Overall, ISIS opens up new possibilities for studying cellular metabolism and regulation, as well as potentially for developing metabolic engineering.</p>
https://github.com/fmairet/ISISYou should fill this box only if you chose 'All or part of the results presented in this preprint are based on data'. URL must start with http:// or https://
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Genome-scale metabolic model; Branching point; Reporter metabolites; Flux Balance Analysis
NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).
Graph theory, Physiology, Systems biology
Alexander Bockmayr Alexander.Bockmayr@fu-berlin.de, TEUSINK Bas b.teusink@vu.nl, Damien Eveillard damien.eveillard@univ-nantes.fr, Clémence Frioux clemence.frioux@inria.fr
e.g. John Doe john@doe.com
No need for them to be recommenders of PCI Math Comp Biol. Please do not suggest reviewers for whom there might be a conflict of interest. Reviewers are not allowed to review preprints written by close colleagues (with whom they have published in the last four years, with whom they have received joint funding in the last four years, or with whom they are currently writing a manuscript, or submitting a grant proposal), or by family members, friends, or anyone for whom bias might affect the nature of the review - see the code of conduct
e.g. John Doe john@doe.com
2023-05-26 17:24:26
Claudine Chaouiya
Anonymous