Includes bibliographical references and index.

Introduction to big mechanisms in systems biology -- System modeling and system identification methods for big mechanism in biological systems -- Procedure for exploring big mechanisms of systems biology through system identification and big database mining -- Big cellular mechanisms in the cell cycle by system identification and big data mining -- Big regulatory mechanisms in the transcriptional regulation control of gene expression using a stochastic system model and genome-wide experimental data -- Big mechanisms of information flow in cellular systems in response to environmental stress signals via system identification and data mining -- Big offensive and defensive mechanisms in systems immunity from system modeling and big data mining -- Big regeneration mechanisms via systems biology and big database mining methods -- Big tumorigenesis mechanisms in systems cancer biology via big data mining and network modeling -- Big evolutionary mechanisms of network robustness and signaling transductivity in aging and carcinogenic process by system modeling and database mining -- Big mechanisms of aging via system identification and big database mining -- Big drug design mechanisms via systems biology and big database mining.

"Big Mechanisms in Systems Biology: Big Data Mining, Network Modeling, and Genome-Wide Data Identification explains big mechanisms of systems biology by system identification and big data mining methods using models of biological systems. Systems biology is currently undergoing revolutionary changes in response to the integration of powerful technologies. Faced with a large volume of available literature, complicated mechanisms, small prior knowledge, few classes on the topics, and causal and mechanistic language, this is an ideal resource. This book addresses system immunity, regulation, infection, aging, evolution, and carcinogenesis, which are complicated biological systems with inconsistent findings in existing resources. These inconsistencies may reflect the underlying biology time-varying systems and signal transduction events that are often context-dependent, which raises a significant problem for mechanistic modeling since it is not clear which genes/proteins to include in models or experimental measurements. The book is a valuable resource for bioinformaticians and members of several areas of the biomedical field who are interested in an in-depth understanding on how to process and apply great amounts of biological data to improve research."--