Biological Computation

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.

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.

Introduction and Biological BackgroundBiological ComputationThe Influence of Biology on Mathematics—Historical ExamplesBiological IntroductionModels and Simulations
Cellular Automata Biological BackgroundThe Game of Life General Definition of Cellular Automata One-Dimensional AutomataExamples of Cellular AutomataComparison with a Continuous Mathematical Model Computational UniversalitySelf-Replication Pseudo Code
Evolutionary ComputationEvolutionary Biology and Evolutionary ComputationGenetic AlgorithmsExample ApplicationsAnalysis of the Behavior of Genetic AlgorithmsLamarckian Evolution Genetic Programming A Second Look at the Evolutionary ProcessPseudo Code
Artificial Neural Networks Biological BackgroundLearning Artificial Neural NetworksThe PerceptronLearning in a Multilayered NetworkAssociative MemoryUnsupervised Learning
Molecular Computation Biological Background Computation Using DNAEnzymatic Computation
The Never-Ending Story: Additional Topics at the Interface between Biology and ComputationSwarm IntelligenceArtificial Immune SystemsArtificial LifeSystems BiologyRecommendations for Additional Reading
A Summary, Further Reading, Exercises, and Answers appear at the end of each chapter.