The number of active metabolic pathways is bounded by the number of cellular constraints at maximal metabolic rates

• Published in: PLoS computational biology 2019-03, Vol.15 (3), p.e1006858-e1006858
• Main Authors: de Groot, Daan H, van Boxtel, Coco, Planqué, Robert, Bruggeman, Frank J, Teusink, Bas
• Format: Article
• Language: English
• Subjects: Algorithms; Bioinformatics; Biology; Biology and Life Sciences; Catalysis; Computer and Information Sciences; Costs; Cytosol; E coli; Enzymes; Enzymes - metabolism; Fluctuations; Flux; Gene expression; Genomes; Growth rate; Kinetics; Metabolic flux; Metabolic Flux Analysis; Metabolic Networks and Pathways; Metabolic pathways; Metabolic rate; Metabolism; Metabolites; Microbial growth; Microorganisms; Optimization; Overflow; Periplasmic space; Physical Sciences; Physiological aspects; Proteins; Proteins - metabolism; Solvents; Supervision; Topology
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1006858

Growth rate is a near-universal selective pressure across microbial species. High growth rates require hundreds of metabolic enzymes, each with different nonlinear kinetics, to be precisely tuned within the bounds set by physicochemical constraints. Yet, the metabolic behaviour of many species is characterized by simple relations between growth rate, enzyme expression levels and metabolic rates. We asked if this simplicity could be the outcome of optimisation by evolution. Indeed, when the growth rate is maximized-in a static environment under mass-conservation and enzyme expression constraints-we prove mathematically that the resulting optimal metabolic flux distribution is described by a limited number of subnetworks, known as Elementary Flux Modes (EFMs). We show that, because EFMs are the minimal subnetworks leading to growth, a small active number automatically leads to the simple relations that are measured. We find that the maximal number of flux-carrying EFMs is determined only by the number of imposed constraints on enzyme expression, not by the size, kinetics or topology of the network. This minimal-EFM extremum principle is illustrated in a graphical framework, which explains qualitative changes in microbial behaviours, such as overflow metabolism and co-consumption, and provides a method for identification of the enzyme expression constraints that limit growth under the prevalent conditions. The extremum principle applies to all microorganisms that are selected for maximal growth rates under protein concentration constraints, for example the solvent capacities of cytosol, membrane or periplasmic space.