A Nonlinear Modeling Framework Using Michaelis-Menten Kinetics for Reconstruction of Gene Regulatory Network

Cells are the basic building blocks of all living organisms. Cellular activities are regulated at genetic level via the underlying genetic regulatory networks (GRN). Unraveling complex living systems necessitates understanding of GRNs. Computational methods along with biological (wet lab) experiments trigger this process. The biological experiments provide the expression data of genes. Inferring GRN is to construct the GRN from the data. This thesis proposes biologically relevant and computationally efficient methods for inferring GRNs. The model is based on fundamental biochemical theories of Michaelis-Menten kinetics. The optimization employed for parameter estimation of the model is developed using principles of statistics and biology for enhanced computational efficiency.