| Summary: | 博士 === 國立陽明大學 === 生物醫學資訊研究所 === 102 === A better understanding of how fundamental biological systems work is a major step toward understanding nature's design principles. Mathematical modeling is a key component of this endeavor in systems biology research. One major challenge in mathematical modeling of biological systems is to determine how model parameters contribute to system dynamics. In this thesis, we propose a simple methodology using statistical tests to identify values of kinetic parameters with which a network model of a biological system can produce the required system dynamics, and, furthermore, elucidate how these kinetic parameters affect different parts of the system's dynamics. In essence, this approach enables us to identify functional parameter values, called kinetic motifs, of the system, and to construct a kinetic-functionality network that elucidates the functional roles and constraints of the kinetic parameters. We demonstrated our methodology on the chemotaxis model of bacteria Escherichia coli, a prototype biological system of perfect adaptation dynamics. Our results agreed well with those derived from experimental and theoretical studies reported in the literature.
The proposed concept of kinetic motif and results obtained have implications for synthetic biology. An increasing number of genetic components are now available in several depositories of such components to facilitate synthetic biology research, but picking out those that will allow a designed gene circuit to achieve the specified function still requires multiple cycles of experimental testing. Extending the study of kinetic motifs, we established a computational pipeline and showed that this pipeline can facilitate rational design and improvement of synthetic gene circuits to achieve user-desired system dynamics with robustness. These results showed that this study has taken a step further in the direction of defining the relationship between kinetic parameters and dynamics of biological systems, and the methodologies developed should be useful for systems biology and synthetic biology research.
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