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Biological Computation [Kõva köide]

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(Tel-Aviv University, Israel), (Bar-Ilan University, Ramat-Gan, Israel)
  • Formaat: Hardback, 343 pages, kõrgus x laius: 234x156 mm, kaal: 800 g, 46 Tables, black and white; 50 Illustrations, black and white
  • Sari: Chapman & Hall/CRC Computational Biology Series
  • Ilmumisaeg: 25-May-2011
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 1420087959
  • ISBN-13: 9781420087956
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Biological ComputationSuurem pilt
  • Formaat: Hardback, 343 pages, kõrgus x laius: 234x156 mm, kaal: 800 g, 46 Tables, black and white; 50 Illustrations, black and white
  • Sari: Chapman & Hall/CRC Computational Biology Series
  • Ilmumisaeg: 25-May-2011
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 1420087959
  • ISBN-13: 9781420087956
Teised raamatud teemal:
Teised raamatud sellest sooduspakkumisest: Taylor & Francis teadusraamatud International Student Editions hindadega
Püsilink: https://www.kriso.ee/db/9781420087956.html
Märksõnad:
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.

Arvustused

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 breathit 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

Preface xv
Chapter 1 Introduction and Biological Background
1(38)
1.1 Biological Computation
1(3)
1.2 The Influence Of Biology On Mathematics---Historical Examples
4(3)
1.3 Biological Introduction
7(19)
1.3.1 The Cell and Its Activities
12(2)
1.3.2 The Structure of DNA
14(2)
1.3.3 The Genetic Code
16(2)
1.3.4 Protein Synthesis and Gene Regulation
18(5)
1.3.5 Reproduction and Heredity
23(3)
1.4 Models And Simulations
26(7)
1.5 Summary
33(1)
1.6 Further Reading
34(1)
1.7 Exercises
34(3)
1.7.1 Biological Computation
34(1)
1.7.2 History
35(1)
1.7.3 Biological Introduction
35(2)
1.7.4 Models and Simulations
37(1)
1.8 Answers To Selected Exercises
37(2)
Chapter 2 Cellular Automata
39(48)
2.1 Biological Background
39(5)
2.1.1 Bacteria Basics
39(1)
2.1.2 Genetic Inheritance---Downward and Sideways
40(1)
2.1.3 Diversity and the Species Question
41(1)
2.1.4 Bacteria and Humans
42(1)
2.1.5 The Sociobiology of Bacteria
42(2)
2.2 The "Game Of Life"
44(4)
2.3 General Definition Of Cellular Automata
48(2)
2.4 1-Dimensional Automata
50(4)
2.5 Examples Of Cellular Automata
54(5)
2.5.1 Fur Color
54(3)
2.5.2 Ecological Models
57(1)
2.5.3 Food Chain
58(1)
2.6 Comparison With A Continuous Mathematical Model
59(2)
2.7 Computational Universality
61(12)
2.7.1 What Is Universality?
61(4)
2.7.2 Cellular Automata as a Computational Model
65(2)
2.7.3 How to Prove That a CA Is Universal
67(1)
2.7.4 Universality of a Two-Dimensional Cellular Automaton---Proof Sketch
68(3)
2.7.5 Universality of the "Game of Life"---Proof Sketch
71(2)
2.8 Self-Replication
73(4)
2.9 Summary
77(1)
2.10 Pseudo-Code
78(1)
2.11 Further Reading
79(1)
2.12 Exercises
79(5)
2.12.1 "Game of Life"
79(1)
2.12.2 Cellular Automata
80(2)
2.12.3 Computing Using Cellular Automata
82(1)
2.12.4 Self-Replication
82(1)
2.12.5 Programming Exercises
83(1)
2.13 Answers To Selected Exercises
84(3)
Chapter 3 Evolutionary Computation
87(56)
3.1 Evolutionary Biology And Evolutionary Computation
87(7)
3.1.1 Natural Selection
87(6)
3.1.2 Evolutionary Computation
93(1)
3.2 Genetic Algorithms
94(14)
3.2.1 Selection and Fitness
98(4)
3.2.2 Variations on Fitness Functions
102(2)
3.2.3 Genetic Operators and the Representation of Solutions
104(4)
3.3 Example Applications
108(3)
3.3.1 Scheduling
108(1)
3.3.2 Engineering Optimization
109(1)
3.3.3 Pattern Recognition and Classification
109(1)
3.3.4 Designing Cellular Automata
110(1)
3.3.5 Designing Neural Networks
110(1)
3.3.6 Bioinformatics
110(1)
3.4 Analysis Of The Behavior Of Genetic Algorithms
111(8)
3.4.1 Holland's Building Blocks Hypothesis
115(1)
3.4.2 The Schema Theorem
116(2)
3.4.3 Corollaries of the Schema Theorem
118(1)
3.5 Lamarckian Evolution
119(2)
3.6 Genetic Programming
121(5)
3.7 A Second Look At The Evolutionary Process
126(4)
3.7.1 Mechanisms for the Generation and Inheritance of Variations
126(3)
3.7.2 Selection
129(1)
3.8 Summary
130(1)
3.9 Pseudo-Code
131(1)
3.10 Further Reading
132(1)
3.11 Exercises
132(8)
3.11.1 Evolutionary Computation
132(1)
3.11.2 Genetic Algorithms
133(1)
3.11.3 Selection and Fitness
133(1)
3.11.4 Genetic Operators and the Representation of Solutions
134(1)
3.11.5 Analysis of the Behavior of Genetic Algorithms
135(1)
3.11.6 Genetic Programming
136(1)
3.11.7 Programming Exercises
136(4)
3.12 Answers To Selected Exercises
140(3)
Chapter 4 Artificial Neural Networks
143(72)
4.1 Biological Background
143(3)
4.1.1 Neural Networks as Computational Model
146(1)
4.2 Learning
146(2)
4.3 Artificial Neural Networks
148(4)
4.3.1 General Structure of Artificial Neural Networks
148(3)
4.3.2 Training an Artificial Neural Network
151(1)
4.4 The Perceptron
152(10)
4.4.1 Definition of a Perceptron
152(4)
4.4.2 Formal Description of the Behavior of a Perceptron
156(2)
4.4.3 The Perceptron Learning Rule
158(1)
4.4.4 Proving the Convergence of the Perceptron Learning Algorithm
159(3)
4.5 Learning In A Multilayered Network
162(18)
4.5.1 The Backpropagation Algorithm
162(8)
4.5.2 Analysis of Learning Algorithms
170(2)
4.5.3 Network Design
172(2)
4.5.4 Examples of Applications
174(6)
4.6 Associative Memory
180(14)
4.6.1 Biological Memory
180(1)
4.6.2 Hopfield Networks
181(1)
4.6.3 Memorization in a Hopfield Network
181(2)
4.6.4 Data Retrieval in a Hopfield Network
183(2)
4.6.5 The Convergence of the Process of Updating the Neurons
185(1)
4.6.6 Analyzing the Capacity of a Hopfield Network
186(3)
4.6.7 Application of a Hopfield Network
189(2)
4.6.8 Further Uses of the Hopfield Network
191(3)
4.7 Unsupervised Learning
194(6)
4.7.1 Self-Organizing Maps
195(3)
4.7.2 WEBSOM: Example of Using SOMs for Document Text Mining
198(2)
4.8 Summary
200(1)
4.9 Further Reading
201(1)
4.10 Exercises
202(8)
4.10.1 Single-Layer Perceptrons
202(1)
4.10.2 Multilayer Networks
203(2)
4.10.3 Hopfield Networks
205(3)
4.10.4 Self-Organizing Maps
208(1)
4.10.5 Summary
208(2)
4.11 Answers To Selected Exercises
210(5)
Chapter 5 Molecular Computation
215(44)
5.1 Biological Background
217(3)
5.1.1 PCR: Polymerase Chain Reaction
217(2)
5.1.2 Gel Electrophoresis
219(1)
5.1.3 Restriction Enzymes
219(1)
5.1.4 Ligation
220(1)
5.2 Computation Using Dna
220(17)
5.2.1 Hamiltonian Paths
220(10)
5.2.2 Solving SAT
230(3)
5.2.3 DNA Tiling
233(3)
5.2.4 DNA Computing---Summary
236(1)
5.3 Enzymatic Computation
237(11)
5.3.1 Finite Automata
238(4)
5.3.2 Enzymatic Implementation of Finite Automata
242(6)
5.4 Summary
248(2)
5.5 Further Reading
250(1)
5.6 Exercises
250(4)
5.6.1 Biological Background
250(1)
5.6.2 Computing with DNA
250(3)
5.6.3 Enzymatic Computation
253(1)
5.7 Answers To Selected Exercises
254(5)
Chapter 6 The Never-Ending Story: Additional Topics at the Interface between Biology and Computation
259(52)
6.1 Swarm Intelligence
261(9)
6.1.1 Ant Colony Optimization Algorithms
262(2)
6.1.2 Cemetery Organization, Larval Sorting, and Clustering
264(3)
6.1.3 Particle Swarm Optimization
267(3)
6.2 Artificial Immune Systems
270(3)
6.2.1 Identifying Intrusions in a Computer Network
271(2)
6.3 Artificial Life
273(11)
6.3.1 Avida
276(5)
6.3.2 Evolvable Virtual Creatures
281(3)
6.4 Systems Biology
284(10)
6.4.1 Evolution of Modularity
287(2)
6.4.2 Robustness of Biological Systems
289(1)
6.4.3 Formal Languages for Describing Biological Systems
290(4)
6.5 Summary
294(3)
6.6 Recommendations For Additional Reading
297(4)
6.6.1 Biological Introduction
297(1)
6.6.2 Personal Perspectives
298(1)
6.6.3 Modeling Biological Systems
298(1)
6.6.4 Biological Computation
299(1)
6.6.5 Cellular Automata
299(1)
6.6.6 Evolutionary Computation
300(1)
6.6.7 Neural Networks
300(1)
6.6.8 Molecular Computation
300(1)
6.6.9 Swarm Intelligence
300(1)
6.6.10 Systems Biology
301(1)
6.6.11 Bioinformatics
301(1)
6.7 Further Reading
301(1)
6.8 Exercises
302(5)
6.8.1 Swarm Intelligence
302(1)
6.8.2 Artificial Immune Systems
303(2)
6.8.3 Artificial Life
305(1)
6.8.4 Systems Biology
306(1)
6.8.5 Programming Exercises
306(1)
6.9 Answers To Selected Exercises
307(4)
Index 311
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.