Modeling and evolutionary analysis of gene regulatory networks

In Systems Biology and more recently emerging field of Synthetic Biology, mathematical modeling has become an indispensable component of research. As com-plementary to the experimental studies, computer simulations are used to accelerate the hypothesis generation-validation cycle of research in biological systems. This thesis is mainly concerned with modeling and inference of gene regulatory dynamics on the basis of gene expression patterns. At first, we make a statistical analysis over randomly generated genetic networks, based on their oscillatory dynamics. Then, in our model problem, we aim to design a family of genetic networks that exhibit stable periodic oscillations with a prescribed period. Later, we investigate the temporal behaviour of a system utilizing a computer simulation. We design such circuits on the basis of in silico evolution of the corresponding network model. The approach starts with a randomized gene network. Then, structural rewiring mutations are applied to the networks. In this process, evolving networks are selected depending on their closer approach to the targeted dynamics, after a mutation. By us¬ing this method, networks with required oscillation periods are constructed by changing the architecture of regulatory connections between the genes. In addition, we choose a small genetic network that exhibits chaotic dynamics, and look at the change of its dynamics against a system parameter. Such an approach is useful in deriving the characteristics of these systems under specific variations. Keywords: Dynamical Systems, Gene Regulatory Networks, Evolutionary Algorithm, Mathematical Modeling, Inference, Optimization.