The understanding of the biology of disease has been greatly improved based on studies and applications of computational biology in maintenance and analysis of patient details, history of disease epidemiology, and therapy. The demographic data of human behavior specific to a disease can predict infection risk and the spread of disease. There is a need to integrate disease pathophysiology from experimental study with clinical studies together with computational and mathematical models in understanding of the biology of disease. Design of disease databases is important in understanding the disease pathology and in design of vaccines. Systems medicine  and mathematical models allow researchers to investigate complex regulatory and signaling processes [2,3] and predict disease targets and are a step toward personalized molecular medicine . Disease models using computational and statistical methods enhance our understanding of pathogen evolution and thus aid in designing therapeutic agents and help predict success rates of therapy. Computational approaches applied to modeling of metagenomic-based analysis of microorganisms at the cellular, ecological, and supra-organism levels are important in both understanding the disease pathology caused by microbes and designing of control measures. Computational approaches in understanding host-pathogen and macromolecular interactions, immune responses, and diseases including cancer are discussed in this chapter.