Date of Award
Doctor of Philosophy
Electrical and Computer Engineering
Bioinformatics and Computational Biology
Julie A. Dickerson
A key aim of current systems biology research is to understand biology at the system level, to systematically catalogue all molecules and their interactions within a living cell, rather than the characteristics of isolated parts of a cell or organism. Network modeling is characterized by viewing cells in terms of their underlying network structure at many different levels of detail is a cornerstone of systems biology. Two emerging methodologies
in network modeling provide invaluable insights into biological systems: static large-scale biological network modeling and dynamic quantitative modeling. Static large-scale biological network modeling focuses on integrating, visualizing and topologically modeling
To facilitate application of these methods in biological research and improve existing network modeling software, this work presents: i) OmicsViz and OmicsAnalyzer, software tools, dedicated to integrating and analyzing omics data sets in network context. ii)
CytoModeler, software tool, dedicated to providing a bridge between static large-scale biological network modeling and dynamic quantitative modeling methods. It not only facilitates network design, model creation, and computational simulation but provides
advanced visualization for simulation results. iii) Comparative network modeling application in the systems biology of the SM-SNARE protein regulation in exocytotic membrane fusion.
This work presents applications of biological network modeling methods to understand regulation mechanisms in complex biological systems. all kinds of omics data sets which are produced by innovative high throughput screening biotechnologies. Dynamic quantitative modeling focuses on exploring dynamics of biological
systems by applying computational simulation and mathematical modeling.
Xia, Tian, "Network modeling in systems biology" (2010). Graduate Theses and Dissertations. 11594.