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Optimality principles reveal a complex interplay of intermediate toxicity and kinetic efficiency in the regulation of prokaryotic metabolism

Zitieren Sie bitte immer diese URN: urn:nbn:de:bvb:20-opus-180870
  • A precise and rapid adjustment of fluxes through metabolic pathways is crucial for organisms to prevail in changing environmental conditions. Based on this reasoning, many guiding principles that govern the evolution of metabolic networks and their regulation have been uncovered. To this end, methods from dynamic optimization are ideally suited since they allow to uncover optimality principles behind the regulation of metabolic networks. We used dynamic optimization to investigate the influence of toxic intermediates in connection with theA precise and rapid adjustment of fluxes through metabolic pathways is crucial for organisms to prevail in changing environmental conditions. Based on this reasoning, many guiding principles that govern the evolution of metabolic networks and their regulation have been uncovered. To this end, methods from dynamic optimization are ideally suited since they allow to uncover optimality principles behind the regulation of metabolic networks. We used dynamic optimization to investigate the influence of toxic intermediates in connection with the efficiency of enzymes on the regulation of a linear metabolic pathway. Our results predict that transcriptional regulation favors the control of highly efficient enzymes with less toxic upstream intermediates to reduce accumulation of toxic downstream intermediates. We show that the derived optimality principles hold by the analysis of the interplay between intermediate toxicity and pathway regulation in the metabolic pathways of over 5000 sequenced prokaryotes. Moreover, using the lipopolysaccharide biosynthesis in Escherichia coli as an example, we show how knowledge about the relation of regulation, kinetic efficiency and intermediate toxicity can be used to identify drug targets, which control endogenous toxic metabolites and prevent microbial growth. Beyond prokaryotes, we discuss the potential of our findings for the development of antifungal drugs.zeige mehrzeige weniger

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Autor(en): Jan Ewald, Martin Bartl, Thomas Dandekar, Christoph Kaleta
URN:urn:nbn:de:bvb:20-opus-180870
Dokumentart:Artikel / Aufsatz in einer Zeitschrift
Institute der Universität:Fakultät für Biologie / Theodor-Boveri-Institut für Biowissenschaften
Sprache der Veröffentlichung:Englisch
Titel des übergeordneten Werkes / der Zeitschrift (Englisch):PLOS Computational Biology
Erscheinungsjahr:2017
Band / Jahrgang:13
Heft / Ausgabe:2
Aufsatznummer:e1005371
Seitenangabe:19
Originalveröffentlichung / Quelle:PLOS Computational Biology (2017) 13:2, e1005371. https://doi.org/10.1371/journal.pcbi.1005371
DOI:https://doi.org/10.1371/journal.pcbi.1005371
Allgemeine fachliche Zuordnung (DDC-Klassifikation):5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Freie Schlagwort(e):Enzyme kinetics; Enzyme metabolism; Enzyme regulation; Enzymes; Metabolic pathways; Predictive toxicology; Toxicity; Transcriptional control
Datum der Freischaltung:17.05.2021
Lizenz (Deutsch):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International