ANALYSIS OF OBJECTIVES AND CONSTRAINTS TOWARDS PREDICTIVE MODELING OF COMPLEX METABOLISM
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Abstract
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.