Metabolic disorders such as obesity and diabetes are nowadays regarded as diseases affecting majority of population. These diseases develop gradually over time in an individual. Recently, systematic experiments tracking disease progression are conducted giving a high-throughput complex data. There is a pressing need for developing methods to analyze this complex data to capture the disease mechanism at molecular level. Diseases usually develop through perturbations of biological processes in an organism. In this study, we have tried to capture the interlinking between different biological processes that work together to regulate the disease phenotype. Here, we have considered a temporal microarray data from an experiment conducted to study obesity and diabetes in mice. We have analyzed the data to obtain perturbed biological processes and developed methods to establish link between these perturbed biological processes. We have derived a mathematical formula to score genes and identified a significant set of genes regulating such a complex process network. The methods developed in our study are also applicable to a broad array of data types.