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

About this book

This book surveys theoretical models in three broad areas of biology (the origin of life, the immune system, and memory in the brain), introducing mathematical and (mainly) computational models that have been used to construct simulations. Most current books on theoretical biology fall into one of two categories: (a) books that specialize in one area of biology and treat theoretical models in considerable depth; and (b) books that concentrate on purely mathematical models, with computers used only to find numerical solutions to differential equations, for example. Although some mathmatical models are considered in this book, the main emphasis is on stochastic computer modles of biological systems. Such techniques have a much greater potential for producting detailed, realistic models of individual systems, and are likely to be the preferred modelling methods of the future. By considering three different areas in biology, the book shows how several of these modelling techniques have been successfully applied in diverse areas. Put simply, this book is important becuase it shows how the power of modern computers is allowing researchers in theoretical biology to break free of the constraints on modelling that were imposed by the traditional differential equation approach. Anyone who is interested in the theoretical models of complicated living systems should have this in his or her library. G. B. Ermentrout, Bulletin of Mathematical Biology

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 McCullogh-Pitts neural net; 18. Perceptrons; 19. Connectionism; 20. Attractor neural networks; 21. Unsupervised learning; 22. Evolutionary learning; A. Differential equations; B. A statistics primer; C. Computer simulation; Bibliography; Index

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