Advisory Committee Chair
Xujing Wang
Advisory Committee Members
Martin Hessner
Renato Camata
James Patterson
Document Type
Dissertation
Date of Award
2016
Degree Name by School
Doctor of Philosophy (PhD) College of Arts and Sciences
Abstract
Systems biology is a rapidly expanding field used to tackle a wide range of biological problems. Systems biology arose to explain areas that detailed biological analysis, such as genetics and biopsies, struggled with. Systems biology is the study of the complexity within a biological system of multiple units that is due to the interaction among the units. In this dissertation systems biology will be combined with phase transition theory from physics to create a new analysis method for complex diseases and apply this new method to a longitudinal dataset on Type 1 Diabetes (T1D) to identify the key contributing molecules to the disease. Phase transition refers normally to the transformation of a state of matter into another state of matter. This definition has evolved within physics to apply to the alteration of an attribute within an object into a different attribute, such as occurs within ferromagnetic materials. This definition has broadened further to include the alteration of a state of any conceptual construct into another state; this has been applied not only within physics but within economics, geology, chemistry, and computer science. Complex diseases have been studied for decades and remain some of the leading causes of death in the elderly in first world countries despite extensive research. Much of this research identifies how a disease induces the symptoms that eventually prove fatal and what are the most significant genetic factors within that disease. While this approach has proved fruitful for many Mendelian diseases, for complex diseases this approach has proven to be a struggle to successfully interpret the results, and for most of these diseases their genetic architecture has yet to be dissected. Systems biology approaches have proven more fruitful with the realization that complex diseases are likely caused by a multitude of genes working together to initiate a phase transition within the body’s regulatory networks. Utilizing T1D as an example, this thesis is focused on further elucidation of the possible mechanisms of phase transition during disease progression and onset, its analysis, and identification of key disease driving networks through several novel methodologies employing this concept.
Supplemental Data
Recommended Citation
Wolanyk, Nathaniel, "Temporal Co-Expression Network Construction, Leading Sub-Network Identification, And Time Point Alignment In The Integration Of Phase Transition Theory And Systems Biology And The Application To Human Type 1 Diabetes" (2016). All ETDs from UAB. 3366.
https://digitalcommons.library.uab.edu/etd-collection/3366
