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Bridging the genomic gaps: Genome-scale metabolic network tools for bioinformatics analyses
Cuevas, Daniel Ablang
Luque, AntoniKalyuzhnaya, MarinaNadim, AliPeng, Qidi
The advent of next-generation DNA sequencing and the acceleration of computational resources have significantly impacted the speed at which genetic information from mi-crobes and the environments they occupy is obtained and processed. However, these tools have also revealed the limitation of our knowledge base. Sequencing depth and volume are uncovering gene products with little-to-no shared sequence similarity to current collections and databases, thus impeding the ability of homology-based approaches to characterize all sequenced genetic material and downstream analyses describing metabolic capabilities and functional diversity. In silico genome-scale metabolic models are increasingly supplementing genomic and metagenomic studies. These metabolic models enable exploration of the metabolism of an organism within a systems biology perspective. As a reconstruction of the complex metabolic network, models contain information on the genes, enzymes, biochemical reactions, and metabolites of an organism. Metabolic models are typically used with constraint based linear programming methods to predict cellular phenotypic properties in various growth conditions. Furthermore, metabolic models are reconciled with experimentation to improve the model’s predictive accuracy and to increase our knowledge base of the organism, providing opportunities to use quantitative methods to analyze the bacterial cell. This thesis project aims to exploit genome-scale metabolic models to extend the traditional bioinformatics workflow to provide more accurate descriptions of bacteria, to quantitatively characterize the metabolic landscape of bacteria, and to produce a development environment where systems biology questions are explored and tested. With PMAnalyzer and PyFBA, these computational tools provide the infrastructure to facilitate the aims of this project. PMAnalyzer quickly and automatically calculates bacterial growth properties (e.g, growth rate and yield) from temporal absorbance data measuring cellular accumulation. PyFBA supplies a programmatic environment to explore and use genome-scale metabolic models built from genomic annotations. The use of phenotypic observations and metabolic predictions will provide a new context to discuss genomic-based studies. This project involves studies describing taxonomically-diverse bacteria isolated from the Southern California kelp forest environment.
Doctor of Philosophy (Ph.D.) Claremont Graduate University and San Diego State University, 2018
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