Pyrimidine nucleotide de novo biosynthesis as a model of metabolic control



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Texas A&M University


This manuscript presents a thorough investigation and description of metabolic control dynamics in vivo and in silico using as a model de novo pyrimidine biosynthesis. Metabolic networks have been studied intensely for decades, helping develop a detailed understanding of the way cells carry out their biosynthetic and catabolic functions. Biochemical reactions have been defined, pathway structures have been proposed, networks of genetic control have been examined, and mechanisms of enzymatic activity and regulation have been elucidated. In parallel with these types of traditional biochemical analysis, there has been increasing interest in engineering cellular metabolism for commercial and medical applications. Several different mathematical approaches have been developed to model biochemical pathways by combining stoichiometric and/or kinetic information with probabilistic analysis, or deciphering the comparative logic of metabolic networks using genomic-derived data. However, most of the research performed to date has relied on theoretical analyses and non-dynamic physiological states. The studies described in this dissertation provide a unique effort toward combining mathematical analysis with dynamic transition experimental data. Most importantly these studies emphasize the significance of providing a quantitative framework for understanding metabolic control. The pathway of de novo biosynthesis of pyrimidines in Escherichia coli provides an ideal model for the study of metabolic control, as there is extensive documentation available on each gene and enzyme involved as well as on their corresponding mechanisms of regulation. Biochemical flux through the pathway was analyzed under dynamic conditions using middle-exponential growth and steady state cultures. The fluctuations of the biochemical pathway intermediates and end products transitions were quantified in response to physiological perturbation. Different growth rates allowed the comparison of rapid versus long-term equilibrium shifts in metabolic adaptation. Finally, monitoring enzymatic activity levels during metabolic transitions provided insight into the interaction of genetic and biochemical mechanisms of regulation. Thus, it was possible to construct a robust mathematical model that faithfully represented, with a remarkable predictability, the nature of the metabolic response to specific environmental perturbations. These studies constitute a significant contribution to the fields of quantitative biochemistry and metabolic control, which can be extended to other cellular processes as well as different organisms.