Characterizing the higher order metabolism of oral streptococci
The metabolism of microbial species is very diverse, displaying a wide range of capabilities. This diversity is often explainable by diﬀerences in the metabolic networks that underly microbial metabolism. However, closely related microbial species that have structurally conserved metabolic networks with relatively few diﬀerences still display diverse growth proﬁles under the same growth conditions. This diversity cannot be explained by metabolic capabilities given the similarity in metabolism across these closely related species. In this dissertation, I explore this phenomenon by comparing the growth ﬁtness of a group of closely related oral streptococci using high throughput combinatorial growth experiments. I compare these experimental results to predictions from genome-scale metabolic models that I constructed for each species under study. These models capture the entire metabolism of a species and predict its metabolic capabilities. Disagreements between experimental results and model predictions point to diﬀerences in utilization of metabolism that can help explain diversity in growth phenotypes despite similarity in metabolic networks. I develop an algorithm that can analyze these diﬀerences and suggest gene suppressions that reconcile model predictions with experimental results, suggesting points of genetic or enzymatic regulation.
oral streptococci, genpme-scale metabolic models, gene regulation, prediction
University of Illinois - Urbana-Champaign
Networked Digital Library of Theses and Dissertations
University of Illinois at Urbana-Champaign