Biological Computation

Biological Computation

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Features

  • Covers cellular automata, evolutionary computation, artificial neural networks, and molecular computation
  • Presents easy-to-follow explanations of several important proof techniques as well as detailed explanations of all derivations
  • Provides all necessary biological background
  • Introduces emerging topics, such as swarm intelligence and artificial immune systems
  • Includes plenty of theoretical and programming exercises in each chapter
  • Contains suggestions for further reading on each topic, along with an annotated bibliography
  • Offers PowerPoint slides for instructors online

Summary

The area of biologically inspired computing, or biological computation, involves the development of new, biologically based techniques for solving difficult computational problems. A unified overview of computer science ideas inspired by biology, Biological Computation presents the most fundamental and significant concepts in this area. In the book, students discover that bacteria communicate, that DNA can be used for performing computations, how evolution solves optimization problems, that the way ants organize their nests can be applied to solve clustering problems, and what the human immune system can teach us about protecting computer networks. The authors discuss more biological examples such as these, along with the computational techniques developed from these scenarios.

The text focuses on cellular automata, evolutionary computation, neural networks, and molecular computation. Each chapter explores the biological background, describes the computational techniques, gives examples of applications, discusses possible variants of the techniques, and includes exercises and solutions. The authors use the examples and exercises to illustrate key ideas and techniques.

Clearly conveying the essence of the major computational approaches in the field, this book brings students to the point where they can either produce a working implementation of the techniques or effectively use one of the many available implementations. Moreover, the techniques discussed reflect fundamental principles that can be applied beyond bio-inspired computing. Supplementary material is available on Dr. Unger's website.

Table of Contents

Introduction and Biological Background
Biological Computation
The Influence of Biology on Mathematics—Historical Examples
Biological Introduction
Models and Simulations

Cellular Automata
Biological Background
The Game of Life
General Definition of Cellular Automata
One-Dimensional Automata
Examples of Cellular Automata
Comparison with a Continuous Mathematical Model
Computational Universality
Self-Replication
Pseudo Code

Evolutionary Computation
Evolutionary Biology and Evolutionary Computation
Genetic Algorithms
Example Applications
Analysis of the Behavior of Genetic Algorithms
Lamarckian Evolution
Genetic Programming
A Second Look at the Evolutionary Process
Pseudo Code

Artificial Neural Networks
Biological Background
Learning
Artificial Neural Networks
The Perceptron
Learning in a Multilayered Network
Associative Memory
Unsupervised Learning

Molecular Computation
Biological Background
Computation Using DNA
Enzymatic Computation

The Never-Ending Story: Additional Topics at the Interface between Biology and Computation
Swarm Intelligence
Artificial Immune Systems
Artificial Life
Systems Biology
Recommendations for Additional Reading

A Summary, Further Reading, Exercises, and Answers appear at the end of each chapter.

Author Bio(s)

Ehud Lamm is on the faculty of The Cohn Institute for the History and Philosophy of Science and Ideas at Tel-Aviv University. Along with his co-author, he previously developed a course on biological computation for the Open University of Israel. He earned his Ph.D. in philosophy of science from Tel-Aviv University.

Ron Unger is a professor and head of the computational biology program at Bar-Ilan University. His current research is focused on protein folding models, genetic algorithms, analysis of biological sequences, and noncoding RNA molecules. He earned his Ph.D. from the Weizmann Institute of Science.

Editorial Reviews

Biological computing, the three-billion-year-old goldmine of information processing concepts, is ready for our educational mainstream. This beautiful undergraduate text by Lamm and Unger may be the first step. This book expertly presents fundamental concepts of molecular biology in its first chapter, and then goes on to develop many computing classics from biology. … I enjoyed reading this text. The exercises flex the imagination, the definitions are clear and precise, and the explanations are unusually powerful. I have been searching for a text like this for years, and now I look forward to using it.
Computing Reviews, August 2011

I read this book in one breath—it opens vistas on how the fields of computation and biology can inspire each other. I particularly enjoyed the analogies between immune systems and software that fights computer viruses.
—Uri Alon, Weizmann Institute of Science, Rehovot, Israel, and author of An Introduction to Systems Biology: Design Principles of Biological Circuits

The book by Lamm and Unger methodically covers exciting developments in biological computation, offering for the first time a broad perspective of this important cutting-edge field of research.
—Ehud Shapiro, The Harry Weinrebe Professorial Chair of Computer Science and Biology, Weizmann Institute of Science, Rehovot, Israel

This is a wonderful treatise on bio-inspired computation, written from a computer science perspective. The authors are extremely knowledgeable about their subject, and the material they cover is both broad and deep. The book should benefit anyone interested in the connection between computer science and biology, a connection that is poised to become dramatically central to the science of the 21st century.
—David Harel, The William Sussman Professorial Chair, Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel

 
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