ANALYSIS OF OBJECTIVES AND CONSTRAINTS TOWARDS PREDICTIVE MODELING OF COMPLEX METABOLISM
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The central theme of this dissertation is predictive modeling of metabolism in complex biological systems with genome-scale stoichiometric metabolic models to (i) gain nontrivial insight on cellular metabolism and (ii) provide justifications for hitherto unexplained metabolic phenomena. The crux of high-quality predictive modeling with genome-scale stoichiometric metabolic models is appropriate selection of (i) a biologically relevant objective function and (ii) a set of constraints based on experimental data. However, in many complex systems, like a plant tissue with its wide array of specialized cells, a biological objective is not always apparent. Additionally, generation of experimental data to develop biochemically relevant constraints can be nearly impossible in systems that cannot be cultured under a controlled environment for the duration of an experiment. Such limitations necessitate careful reformulation of the biological question, development of novel methods and data analysis strategies. Here, we push the boundaries of predictive modeling by demonstrating its first application in deciphering hitherto unexplained metabolic phenomena and in developing novel hypotheses on metabolism. Towards achieving this goal, we developed several novel approaches and employed them in diverse biological systems. Firstly, we investigated the selection of carbohydrate degrading pathway employed by Saccharophagus degradans, an aerobic cellulosic marine bacterium. Flux balance analyses of its growth in nutrient rich hypoxic marine environment predicted that the selection of carbohydrate degrading pathway is possibly influenced by inorganic nutrient availability. Secondly, multi-tissue genome-scale metabolic modeling of Populus trichoparpa, a perennial woody tree, and analyses with a novel strategy based on multiple biologically relevant metrics provided a metabolic justification for the predominance of glutamine as the predominant nitrogen transport amino acid for internal nitrogen recycling. Thirdly, predictive modeling of maize grain filling predicted amino acid fermentation as a mechanism for expending excess reductant cofactors for continual starch synthesis in the hypoxic environment of endosperm. Finally, we developed bilevel optimization framework to integrate publicly available transcriptome datasets with metabolic networks. This framework predicted accumulation of specific classes of maize endosperm storage protein at distinct stages of grain filling. We anticipate that the employment of these aforementioned approaches in other biological systems will lead to the generation of a wide array of nontrivial hypothesis on cellular metabolism and to develop targeted experiments to validate them.