Theoretical Models in Biology: The Origin of Life, the Immune System, and the Brain

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This book surveys the state of the art in theoretical and computer modeling for biological sciences. Using both mathematical and stochastic computer models of biological systems, the author focuses in particular on three central topics: the origin of life, the immune system, and memory in the brain. Throughout the book, the author emphasizes how the power of modern computers is allowing researchers in theoretical biology to break free of the constraints on modeling that were imposed by the traditional differential equation approach. Unique in its emphasis on modeling and computer simulation, Theoretical Models in Biology: The Origin of Life, the Immune System, and the Brain will be welcomed by students and researchers of mathematical biology.

Emphasizes stochastic computer models for detailed models of the immune system & AIDS, the brain & neurons & synapses etc

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Product Details

  • ISBN-13: 9780198596875
  • Publisher: Oxford University Press, USA
  • Publication date: 1/28/1998
  • Pages: 440
  • Product dimensions: 9.10 (w) x 6.10 (h) x 1.00 (d)

Meet the Author

University of Dundee
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Table of Contents

I. The Origin of Life
1. The molecular basis of life
2. Molecular evolution and quasi-species
3. Stochastic processes
4. A spin glass model of the origin of life
5. The origin of the genetic code
6. Hypercycles
7. Artificial life
II. The Immune System
8. The immune system
9. Bell's model
10. Adaptive walks
11. Maturation of the immune response
12. The symmetric immune network model
13. A shape space network model
14. AIDS
III. The Brain
15. Neurons and synapses
16. Memory
17. The McCulloch-Pitts neural net
18. Perceptrons
19. Connectionism
20. Attractor neural networks
21. Unsupervised learning
22. Evolutionary learning
A. Differential equations
C. Computer simulation

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  • Anonymous

    Posted September 22, 2000


    Rowe is one of the best computer science writers today and this book is in keeping with his usually high standards. He draws together the worlds of computer science, biology, and mathematics to present clearly understandable and meaningful models. Any reader with a reasonable background in these fields (decent college freshman courses in programming, calculus, and general biology should suffice, though I'd say that as far as the math goes, some exposure to differential equations would be very helpful) will be able to understand and learn from this material.

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