INFERENCE, SIMULATION, MODELING, AND ANALYSIS OF COMPLEX NETWORKS, WITH SPECIAL EMPHASIS ON COMPLEX NETWORKS IN SYSTEMS BIOLOGY

Open Access
- Author:
- Christensen, Claire Petra
- Graduate Program:
- Physics
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- October 05, 2007
- Committee Members:
- Reka Z Albert, Committee Chair/Co-Chair
Dezhe Jin, Committee Member
Peter E Schiffer, Committee Member
Costas D Maranas, Committee Member - Keywords:
- systems biology
gene regulatory networks
graph theory
networks
disease dynamics
computational modeling - Abstract:
- Abstract Claire Petra Christensen Across diverse fields ranging from physics to biology, sociology, and economics, the technological advances of the past decade have engendered an unprecedented explosion of data on highly complex systems with thousands, if not millions of interacting components. These systems exist at many scales of size and complexity, and it is becoming ever-more apparent that they are, in fact, universal, arising in every field of study. Moreover, they share fundamental properties—chief among these, that the individual interactions of their constituent parts may be well-understood, but the characteristic behaviour produced by the confluence of these interactions— by these complex networks-- is unpredictable; in a nutshell, the whole is more than the sum of its parts. By virtue of the fact that there are literally countless complex systems, not to mention tools and techniques used to infer, simulate, analyze, and model these systems, it is impossible to give a truly comprehensive account of the history and study of complex systems. The author’s own publications have contributed network inference, simulation, modeling, and analysis methods to the much larger body of work in systems biology, and indeed, in network science. The aim of this thesis is therefore twofold: to present this original work in the historical context of network science, but also to provide sufficient review and reference regarding complex systems (with an emphasis on complex networks in systems biology) and tools and techniques for their inference, simulation, analysis, and modeling, such that the reader will be comfortable in seeking out further information on the subject.