Computer Science ETDs

Publication Date

Spring 4-26-2024

Abstract

As we are entering the era of multi-omic datasets, it is increasingly evident that the complexities of natural systems exceed the capabilities of current computational methodologies. My dissertation research aims to reduce this gap by using computational frameworks to investigate the evolution of complex systems. I apply a model of metabolic networks to predict human embryonic metabolism during peri-implantation (Chapter I), offering insights into the foundations of early human development. This dissertation further examines the basis of the phenotypic evolution of polygenic traits when subjected to directional (Chapter II) and temporally varying environmental shifts (Chapter III), revealing the roles of allele frequency distributions, their shifts, and the resulting genetic variance in the process of rapid phenotypic adaptation. This dissertation elucidates the role of evolution from the micro-scale of developing organisms to the adaptability of whole populations in response to rapid environmental changes, contributing to the understanding of complex biological systems.

Language

English

Keywords

#Computational Biology #Polygenic Traits #Phenotypic Evolution #Metabolic Networks #Human Embryonic Development #Flux Balance Analysis #Slowly Continuous Environmental Shifts #Temporal Environmental Change

Document Type

Dissertation

Degree Name

Computer Science

Level of Degree

Doctoral

Department Name

Department of Computer Science

First Committee Member (Chair)

Dr. Davorka Gulisija

Second Committee Member

Dr. Shuang Luan

Third Committee Member

Dr. Matthew Lakin

Fourth Committee Member

Dr. Chris Kempes

Fifth Committee Member

Dr. Mitchell Newberry

Download

Share

COinS