Identification of flux trade-offs in metabolic networks

• Published in: Scientific reports Vol. 11; no. 1; p. 23776
• Main Authors: Hashemi, Seirana, Razaghi-Moghadam, Zahra, Nikoloski, Zoran
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 10.12.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/553; Algorithms; Arabidopsis - physiology; Biosynthesis; Carbon; Carbon - metabolism; Carbon sources; Computational Biology - methods; E coli; Energy Metabolism; Escherichia coli - physiology; Fluctuations; Gene expression; Genomes; Humanities and Social Sciences; Metabolic flux; Metabolic networks; Metabolic Networks and Pathways; Metabolism; Models, Biological; multidisciplinary; Nitrogen; Nutrients - metabolism; Saccharomyces cerevisiae - physiology; Science; Science (multidisciplinary); Sulfur; Systems Biology - methods
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-03224-9

Trade-offs are inherent to biochemical networks governing diverse cellular functions, from gene expression to metabolism. Yet, trade-offs between fluxes of biochemical reactions in a metabolic network have not been formally studied. Here, we introduce the concept of absolute flux trade-offs and devise a constraint-based approach, termed FluTO, to identify and enumerate flux trade-offs in a given genome-scale metabolic network. By employing the metabolic networks of Escherichia coli and Saccharomyces cerevisiae , we demonstrate that the flux trade-offs are specific to carbon sources provided but that reactions involved in the cofactor and prosthetic group biosynthesis are present in trade-offs across all carbon sources supporting growth. We also show that absolute flux trade-offs depend on the biomass reaction used to model the growth of Arabidopsis thaliana under different carbon and nitrogen conditions. The identified flux trade-offs reflect the tight coupling between nitrogen, carbon, and sulphur metabolisms in leaves of C 3 plants. Altogether, FluTO provides the means to explore the space of alternative metabolic routes reflecting the constraints imposed by inherent flux trade-offs in large-scale metabolic networks.