Constraint-based modeling of metabolism - interpreting predictions of growth and ATP synthesis in human and yeast

Abstract: Growth is the primary objective of the cell. Diseases arise when cells diverge from a healthy growth-pattern. An increased understanding of cellular growth may thus be translated into improved human health. The cell requires materials and free energy (in the form of ATP) in order to grow, metabolism supplies the cell with this. The rate of metabolism is ultimately constrained by the biophysical properties of the metabolic enzymes. Interactions between the constraints and the growth-objective gives rise to metabolic trade-offs, e.g. between ATP synthesis from respiration and fermentation. We can gain quantitative insight into these processes by simulating metabolism using mathematical models. In this thesis I simulated the metabolism of four biological systems: the infant, cancer, yeast and muscle. The simulations demonstrated how a shift in metabolic strategy may increase the rates of ATP synthesis and growth. These increased metabolic rates come at the expense of decreased resource efficiency, i.e. ATP produced per carbon consumed. The effect was primarily caused by the low catalytic efficiency of the respiratory enzyme complexes I and V. By shifting from respiratory to fermentative ATP synthesis, the cell was able to bypass these constraints. An intermediate strategy involved bypassing only complex I. The phenomenon was experimentally corroborated in the working muscle, and it is the native state of the yeast Saccharomyces cerevisiae (which lacks complex I). The differences in efficiency between the different metabolic pathways also explained why cells grow faster on some carbon sources, e.g. the specific growth rate for yeast is higher on glucose than on ethanol. These models were extended to predict the world-record running-speeds at different distances, by taking the sizes of the body’s nutrient-deposits into account. A metabolic strategy employed by cancer cells involved excretion of the amino acid glutamate. The simulations showed a mechanistic relation to catabolism of branched-chain amino acids and the localization of amino acid metabolism to different cellular compartments. By experimentally inhibiting glutamate excretion using an off-the-shelf drug (sulfasalazine), the growth rate of a cancer cell line was reduced. The metabolic modeling involved integration of various types of data and thus demonstrated the potential to unify knowledge from different studies and domains. This exposed contradictory claims in literature and highlighted knowledge-gaps that need to be filled to further improve human health.