Off-campus UNL users: To download campus access dissertations, please use the following link to log into our proxy server with your NU ID and password. When you are done browsing please remember to return to this page and log out.
Non-UNL users: Please talk to your librarian about requesting this dissertation through interlibrary loan.
Scale vs. Detail: Modeling Metabolic Networks Using Varying Levels of Complexity
Abstract
Computational analysis of biological systems aims to predict cell behavior under different environmental and genetic contexts. Due to the complexity of living organisms, it is not possible to construct a comprehensive mathematical description of context-specific cellular phenotypes. However, with the advent of genome sequencing techniques and the accelerated rate of gene function discovery, the phenotypic landscape of an organism can be determined with the aid of metabolic network reconstructions. These networks describe the biochemical transformations occurring in the cell as a system of mathematical equations. By utilizing concepts from optimization and numerical analysis, different modeling techniques can be used to solve this system of equations. Recently, new frameworks aiming to improve the predictive accuracy of conventional models through incorporation of additional physical and biological considerations have been proposed. In this dissertation, I demonstrate the utility of such frameworks and discuss how they fit within the overall landscape of metabolic modeling. In the first chapter, I highlight the underlying concepts that form the backbone of each framework. In subsequent chapters, I illustrate how these concepts can be utilized to answer biologically relevant questions. The research works presented in this dissertation serve as a stepping-stone towards achieving the ultimate goal of systems biology.
Subject Area
Systematic biology
Recommended Citation
Alsiyabi, Adil, "Scale vs. Detail: Modeling Metabolic Networks Using Varying Levels of Complexity" (2022). ETD collection for University of Nebraska-Lincoln. AAI29163957.
https://digitalcommons.unl.edu/dissertations/AAI29163957