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Modeling Biomolecular Networks in Cells: Structures and Dynamics

Modeling Biomolecular Networks in Cells: Structures and Dynamics

by Luonan Chen (Author), RuiqiWang (Author), KazuyukiAihara (Author), Chunguang Li (Author)

  • New

    Hardcover 2010

    $187.45
Synopsis

Modeling Biomolecular Networks in Cells shows how the interaction between the molecular components of basic living organisms can be modelled mathematically and the models used to create artificial biological entities within cells. Such forward engineering is a difficult task but the nonlinear dynamical methods espoused in this book simplify the biology so that it can be successfully understood and the synthesis of simple biological oscillators and rhythm-generators made feasible. Such simple units can then be co-ordinated using intercellular signal biomolecules. The formation of such man-made multicellular networks with a view to the production of biosensors, logic gates, new forms of integrated circuitry based on gene-chips and even biological computers is an important step in the design of faster and more flexible electronics . The book also provides theoretical frameworks and tools with which to analyze the nonlinear dynamical phenomena which arise from the connection of building units in a biomolecular network.

$187.45

Quantity

20+ in stock

More Information

Format: Hardcover
 Pages: 330
 Edition: 1st Edition.
 Publisher: Springer
Published: 15 Jul 2010
ISBN 10: 1849962138
ISBN 13: 9781849962131

Author Bio
Luonan Chen received his M.E. and Ph.D. degrees in electrical engineering from Tohoku University, Sendai, Japan, in 1988 and 1991, respectively. From 1997, he was a member of the faculty of Osaka Sangyo University, Osaka, Japan, and then became a full Professor in the Department of Electrical Engineering and Electronics. He was also the founding director of Institute of Systems Biology, Shanghai University. Since 2010, he has been a professor at Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences. His fields of interest are systems biology, bioinformatics, and nonlinear dynamics. He serves as associate editor or editorial board member for many systems biology related journals, e.g. BMC Systems Biology, IEEE/ACM Trans. on Computational Biology and Bioinformatics, IET Systems Biology, Mathematical Biosciences, International Journal of Systems and Synthetic Biology, and the Journal of Systems Science and Complexity. He also serves as Chair of Technical Committee of Systems Biology at the IEEE SMC Society. Ruiqi Wang received an M.S. degree in mathematics from Yunnan University, Kunming, China, in 1999, and a Ph. D. degree in mathematics from the Academy of Mathematics and Systems Science, CAS, Beijing, China, in 2002. Since 2007, he has been a member of the faculty of Shanghai University, Shanghai, China, where he is currently an Associate Professor at Institute of Systems Biology. His fields of interest are systems biology and nonlinear dynamics. Chunguang Li received an M.S. degree in Pattern Recognition and Intelligent Systems and a Ph.D. degree in Circuits and Systems from the University of Electronic Science and Technology of China, Chengdu, China, in 2002 and 2004, respectively. Currently, he is a Professor with the Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China. His current research interests include computational neuroscience, statistical signal processing, and machine intelligence. Kazuyuki Aihara received a B.E. degree of electrical engineering in 1977 and a Ph.D. degree of electronic engineering 1982 from the University of Tokyo, Japan. Currently, he is Professor of the Institute of Industrial Science, Professor of the Graduate School of Information Science and Technology, and Director of Collaborative Research Center for Innovative Mathematical Modelling at the University of Tokyo. His research interests include mathematical modeling of complex systems, parallel distributed processing with spatio-temporal chaos, and time series analysis of complex data.