UC San Diego

Understanding the behavior of complex biochemical networks is the primary goal of systems biology. This task is often addressed through the generation of large data sets such as measurements of biological components like mRNA transcripts, proteins, and metabolites. Although these methods have become increasingly accurate and comprehensive at measuring the state of the system, uncovering the function of the system then becomes a problem of analysis to extract an understanding of the system from the data. A key challenge in analyzing biological data sets is that determining the function of the system depends on a knowledge of the relationship between the components of the system. These relationships can be captured by grouping variables by known associations, such as pathways, or by explicitly modeling their relationships mathematically. Metabolic networks are particularly primed for both of these approaches, because metabolic pathways are well-defined by network topology and the equation governing their function, the mass balance equation, is well understood. In this thesis, the capabilities of metabolic networks to interpret biological data are advanced through the development and application of models of increasing levels of detail. First, pathways systematically derived from a global human metabolic network reconstruction are used to identify metabolic perturbations tied to drug side effects from in vitro drug -treated gene expression data. Second, steady-state flux modeling of a core human metabolic network is used to identify factors underlying two hallmarks of cancer metabolism: the Warburg effect and glutamine addiction. Finally, the concept of a metabolic network reconstruction is extended by the definition of detailed enzyme kinetic mechanisms within E. coli central metabolism, integrating multiple data sets mechanistically to calculate dynamic functional states of enzymes. This work furthers the use of metabolic networks in analyzing complex biological data sets, showcasing the utility of these networks in addressing practical questions in systems biology using methods of increasing mechanistic resolution