Type of Document Dissertation Author Fu, Yan Author's Email Address email@example.com URN etd-07262012-102649 Title Computational Systems Biology Analysis of Cell Reprogramming and Activation Dynamics Degree PhD Department Genetics, Bioinformatics, and Computational Biology Advisory Committee
Advisor Name Title Xing, Jianhua Committee Chair Tyson, John J. Committee Co-Chair Li, Liwu Committee Member Lu, Chang-Tien Committee Member Keywords
- computational modeling
- network motifs
- LPS priming and tolerance
- bacterial phenotypic transition
Date of Defense 2012-07-17 Availability unrestricted AbstractIn the past two decades, molecular cell biology has transitioned from a traditional descriptive science into a quantitative science that systematically measures cellular dynamics on different levels of genome, transcriptome and proteome. Along with this transition emerges the interdisciplinary field of systems biology, which aims to unravel complex interactions in biological systems through integrating experimental data into qualitative or quantitative models and computer simulations. In this dissertation, we applied various systems biology tools to investigate two important problems with respect to cellular activation dynamics and reprograming.
Specifically, in the first section of the dissertation, we focused on lipopolysaccharide (LPS)-mediated priming and tolerance: a reprogramming in cytokine production in macrophages pretreated with specific doses of LPS. Though both priming and tolerance are important in the immune system’s response to pathogens, the molecular mechanisms still remain unclear. We computationally investigated all network topologies and dynamics that are able to generate priming or tolerance in a generic three-node model. Accordingly, we found three basic priming mechanisms and one tolerance mechanism. Existing experimental evidence support these in silico found mechanisms.
In the second part of the dissertation, we applied stochastic modeling and simulations to investigate the phenotypic transition of bacteria E.coli between normally-growing cells and persister cells (growth-arrested phenotype), and how this process can contribute to drug resistance. We built up a complex computational model capturing the molecular mechanism on both single cell level and population level. The paper also proposed a novel way to accelerate the phenotypic transition from persister cells to normally growing cell under resonance activation. The general picture of phenotypic transitions should be applicable to a broader context of biological systems, such as T cell differentiation and stem cell reprogramming.
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