Cellular determinants of metabolite concentration ranges

• Published in: PLoS computational biology 2019-01, Vol.15 (1), p.e1006687-e1006687
• Main Authors: Küken, Anika, Eloundou-Mbebi, Jeanne M O, Basler, Georg, Nikoloski, Zoran
• Format: Article
• Language: English
• Subjects: Bacteria; Biochemistry; Bioinformatics; Biology; Biology and Life Sciences; Cellular communication; Computer and Information Sciences; Computer simulation; Determinants; E coli; Enzymes; Escherichia coli; Escherichia coli - genetics; Genomes; Genomics; Growth rate; Kinetics; Medicine and Health Sciences; Metabolic networks; Metabolic Networks and Pathways - genetics; Metabolism; Metabolites; Metabolome - genetics; Models, Biological; Ordinary differential equations; Organisms; Physical Sciences; Physiological aspects; Physiology; Systems Biology - methods
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1006687

Cellular functions are shaped by reaction networks whose dynamics are determined by the concentrations of underlying components. However, cellular mechanisms ensuring that a component's concentration resides in a given range remain elusive. We present network properties which suffice to identify components whose concentration ranges can be efficiently computed in mass-action metabolic networks. We show that the derived ranges are in excellent agreement with simulations from a detailed kinetic metabolic model of Escherichia coli. We demonstrate that the approach can be used with genome-scale metabolic models to arrive at predictions concordant with measurements from Escherichia coli under different growth scenarios. By application to 14 genome-scale metabolic models from diverse species, our approach specifies the cellular determinants of concentration ranges that can be effectively employed to make predictions for a variety of biotechnological and medical applications.