Computational Cell Biology (Interdisciplinary Applied Mathematics, 20)
Language: English
Published by Springer, 2002
Series: Book 9 of 53 - Interdisciplinary Applied Mathematics
- Hardcover
- New

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- Title
- Computational Cell Biology (Interdisciplinary Applied Mathematics, 20)
- Publisher
- Springer
- Publication year
- 2002
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 0387953698
- ISBN 13
- 9780387953694
- Edition
- 3rd Edition
- Series
- Book 9 of 53: Interdisciplinary Applied Mathematics
"Synopsis" may belong to another edition of this title.
From the Back Cover
This textbook provides an introduction to dynamic modeling in cell biology, emphasizing computational approaches based on realistic molecular mechanisms. It is designed to introduce cell biology and neuroscience students to computational modeling, and applied mathematics students, theoretical biologists, and engineers to many of the problems in dynamical cell biology. This volume was conceived of and begun by Professor Joel Keizer based on his many years of teaching and research together with his colleagues. The project was expanded and finished by his students and friends after his untimely death in 1999.
Carefully selected examples are used to motivate the concepts and techniques of computational cell biology, through a progression of increasingly more complex and demanding cases. Illustrative exercises are included with every chapter, and mathematical and computational appendices are provided for reference. This textbook will be useful for advanced undergraduate and graduate theoretical biologists, and for mathematic students and life scientists who wish to learn about modeling in cell biology.
"What better tribute to the late Joel Keizer than to expand his unfinished accounts of teaching and research to a splendid book. Computational Cell Biology performs much more than it promises, for it also deals with considerable analytical material and with aspects of molecular biology. There's something for everybody interested in how modeling leads to greater understanding in the core of the biological sciences."
-Lee Segel (Weizmann Institute)
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