Abstract: High-throughput technologies for biological measurements generate vast amounts of quantitative data, which necessitates the development of advanced approaches to data analysis to help understand the underlying processes and networks. Reconstruction of biological networks from measured data of different components is a significant challenge in systems biology, whose goal is to develop network models capable of the quantitative mapping of inputs to responses resulting in a given phenotype. In this presentation, I use an information theoretic approach to develop a data-driven parsimonious input-output model of the phosphoprotein-cytokine network. Then I propose an information theoretic approach to reconstruct dynamic biological networks from time-course data. The results of this study are essential for understanding the functional and dynamical behaviors of biological networks.