Lamm / Unger Biological Computation

1. Auflage 2011
ISBN: 978-1-4200-8796-3
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 343 Seiten

Reihe: Chapman & Hall/CRC Mathematical & Computational Biology

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.

Zielgruppe

Advanced undergraduate and beginning graduate students in biocomputation and computational biology courses; computer scientists, mathematicians, and physicists in computational biology.

Autoren/Hrsg.

Lamm, Ehud

Unger, Ron

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik

Inhaltsverzeichnis

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.

Über Autor(innen)

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.