Big Mechanisms in Systems Biology (eBook)

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. - Written in a didactic manner in order to explain how to investigate Big Mechanisms by big data mining and system identification - Provides more than 140 diagrams to illustrate Big Mechanism in systems biology - Presents worked examples in each chapter

Bor-Sen Chen received B.S. degree of electrical Engineering from Tatung Institute of Technology in 1970, M.S. degree of Geophysics from National Central University in 1973, and PhD in Electrical Engineering from University of Southern California in 1982. He is an expert on the topic of nonlinear robust control and filter designs based on stochastic Nash game theory to override the influence of intrinsic random fluctuations and attenuate the effect of environmental disturbances, which can be applied to evolutionary game strategies of biological networks under natural selection to respond to random genetic variations and environmental disturbances in the evolutionary process. Prof. Chen had audited more than 10 courses of biology before his research in systems biology. He has published about 100 papers in bioinformatics and systems biology. Further, he have published more than 100 papers in system theory and control, and more than 80 papers of signal processing and communication. In the last three years, he has also published 7 monographs. He was elected to an IEEE Fellow in 2001 and became an IEEE Life Fellow in 2014.

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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. - Written in a didactic manner in order to explain how to investigate Big Mechanisms by big data mining and system identification- Provides more than 140 diagrams to illustrate Big Mechanism in systems biology- Presents worked examples in each chapter