טכניון מכון טכנולוגי לישראל
הטכניון מכון טכנולוגי לישראל - בית הספר ללימודי מוסמכים  
M.Sc Thesis
M.Sc StudentAdadi Roi
SubjectPrediction of Microbial Growth Rate versus Biomass Yield by
a Metabolic Network with Kinetic Parameters
DepartmentDepartment of Computer Science
Supervisor Professor Tomer Shlomi
Full Thesis textFull thesis text - English Version


Abstract

Identifying the factors that determine microbial growth rate under various environmental and genetic conditions is a major challenge of systems biology. While current genome-scale metabolic modeling approaches enable to successfully predict a variety of metabolic phenotypes, including maximal biomass yield, the prediction of actual growth rate is a long standing goal that is currently beyond reach. This gap stems from strictly relying on data regarding enzyme stoichiometry and directionality, without accounting for enzyme kinetic considerations. Here, we present a novel constraint-based modeling method, MetabOlic Modeling with ENzyme kineTics (MOMENT), which predicts cellular metabolic state and growth rate by utilizing prior data on enzyme turnover rates and molecular weights. Both enzyme turnover rates and molecular weights are shown to be significantly correlated with measured metabolic flux under various conditions, testifying for the importance of these parameters in metabolic flux analysis. Extending upon previous attempts to utilize kinetic data in genome-scale metabolic modeling, our approach takes into account enzyme concentration requirements for catalyzing metabolic flux, considering isozymes, protein complexes, and multi-functional enzymes. Applied to the prediction of growth rate of E. coli across a set of 24 different media, MOMENT's predictions are shown to significantly correlate with measurements, while existing state-of-the art metabolic modeling approaches fail to do so. MOMENT is further shown to significantly improve the prediction accuracy of various metabolic phenotypes in E. coli, including intracellular flux rates and differential gene expression levels across growth rates.