Nonstationary Metabolic Flux Analysis (NMFA) for the Elucidation of Cellular Physiology by

Many current and future applications of biological engineering hinge on our ability to measure, understand, and manipulate metabolism. Many diseases for which we seek cures are metabolic in nature. Small-molecule biomanufacturing almost always involves metabolic engineering. Biofuels, a current topic of great interest, is essentially a metabolic problem. Even bioprocesses that involve complex products, such as enzyme or antibody manufacturing, still rely on a healthy and optimal metabolism and can benefit from a greater understanding therein. A cell’s metabolic flux distribution has been proposed to be one of the most solid and meaningful indicators and descriptors of metabolism. Metabolic fluxes represent integrative information and are a function of gene expression, translation, posttranslational modifications, and protein-metabolite interactions. Metabolic flux analysis (MFA) is a powerful method for determining these flux distribution through a cellular reaction network. However, MFA has experimental limitations (most notably, a requirement for isotopic steady state) that restrict the scope of biological contexts in which it can be applied. Nonstationary metabolic flux analysis (NMFA) has recently emerged as a combined computational and experimental method that improves upon MFA with the capacity to estimate fluxes even during periods of isotopic transience in metabolism, allowing flux analysis to be applied in a broader range of experimental settings. In this thesis, we have developed and applied robust and efficient NMFA tools and techniques and applied them to understand various cellular physiologies. We built a software package (MetranCL) that combines the elementary metabolite unit (EMU) framework, a new network decomposition strategy termed block decoupling, and a customized differential equation solver. MetranCL performs flux estimations as much as 5000 times faster than the previous state-of-the-art NMFA methods, opening entirely new types of biological systems to the possibility of flux analysis. We applied MetranCL to a simulated large network representing E. coli metabolism and were able to successfully estimate reaction fluxes and metabolite concentrations