Biological Computation

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ISBN: 9781420087956 | 1420087959
Cover: Hardcover
Copyright: 5/25/2011

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Created for advanced undergraduates in computer science programs, Biological Computation covers major themes of bio-inspired computing, including cellular automata, molecular computation, genetic algorithms, and neural networks. Providing theoretical and coding exercises, this self-contained text requires no previous knowledge of biology. The book provides valuable insight to researchers and students from biomedical backgrounds looking to gain the computational skills needed to make entry into the fields of systems biology, biological modeling, and simulations.

Preface	p. xv
Introduction and Biological Background	p. 1
Biological Computation	p. 1
The Influence of Biology on Mathematics-Historical Examples	p. 4
Biological Introduction	p. 7
The Cell and Its Activities	p. 12
The Structure of DNA	p. 14
The Genetic Code	p. 16
Protein Synthesis and Gene Regulation	p. 18
Reproduction and Heredity	p. 23
Models and Simulations	p. 26
Summary	p. 33
Further Reading	p. 34
Exercises	p. 34
Biological Computation	p. 34
History	p. 35
Biological Introduction	p. 35
Models and Simulations	p. 37
Answers to Selected Exercises	p. 37
Cellular Automata	p. 39
Biological Background	p. 39
Bacteria Basics	p. 39
Genetic Inheritance-Downward and Sideways	p. 40
Diversity and the Species Question	p. 41
Bacteria and Humans	p. 42
The Sociobiology of Bacteria	p. 42
The ôGame of Lifeö	p. 44
General Definition of Cellular Automata	p. 48
1-Dimensional Automata	p. 50
Examples of Cellular Automata	p. 54
Fur Color	p. 54
Ecological Models	p. 57
Food Chain	p. 58
Comparison With a Continuous Mathematical Model	p. 59
Computational Universality	p. 61
What Is Universality?	p. 61
Cellular Automata as a Computational Model	p. 65
How to Prove That a CA Is Universal	p. 67
Universality of a Two-Dimensional Cellular Automaton-Proof Sketch	p. 68
Universality of the ôGame of Lifeö-Proof Sketch	p. 71
Self-Replication	p. 73
Summary	p. 77
Pseudo-Code	p. 78
Further Reading	p. 79
Exercises	p. 79
ôGame of Lifeö	p. 79
Cellular Automata	p. 80
Computing Using Cellular Automata	p. 82
Self-Replication	p. 82
Programming Exercises	p. 83
Answers to Selected Exercises	p. 84
Evolutionary Computation	p. 87
Evolutionary Biology and Evolutionary Computation	p. 87
Natural Selection	p. 87
Evolutionary Computation	p. 93
Genetic Algorithms	p. 94
Selection and Fitness	p. 98
Variations on Fitness Functions	p. 102
Genetic Operators and the Representation of Solutions	p. 104
Example Applications	p. 108
Scheduling	p. 108
Engineering Optimization	p. 109
Pattern Recognition and Classification	p. 109
Designing Cellular Automata	p. 110
Designing Neural Networks	p. 110
Bioinformatics	p. 110
Analysis of the Behavior of Genetic Algorithms	p. 111
Holland's Building Blocks Hypothesis	p. 115
The Schema Theorem	p. 116
Corollaries of the Schema Theorem	p. 118
Lamarckian Evolution	p. 119
Genetic Programming	p. 121
A Second Look at the Evolutionary Process	p. 126
Mechanisms for the Generation and Inheritance of Variations	p. 126
Selection	p. 129
Summary	p. 130
Pseudo-Code	p. 131
Further Reading	p. 132
Exercises	p. 132
Evolutionary Computation	p. 132
Genetic Algorithms	p. 133
Selection and Fitness	p. 133
Genetic Operators and the Representation of Solutions	p. 134
Analysis of the Behavior of Genetic Algorithms	p. 135
Genetic Programming	p. 136
Programming Exercises	p. 136
Answers to Selected Exercises	p. 140
Artificial Neural Networks	p. 143
Biological Background	p. 143
Neural Networks as Computational Model	p. 146
Learning	p. 146
Artificial Neural Networks	p. 148
General Structure of Artificial Neural Networks	p. 148
Training an Artificial Neural Network	p. 151
The Perceptron	p. 152
Definition of a Perceptron	p. 152
Formal Description of the Behavior of a Perceptron	p. 156
The Perceptron Learning Rule	p. 158
Proving the Convergence of the Perceptron Learning Algorithm	p. 159
Learning in a Multilayered Network	p. 162
The Backpropagation Algorithm	p. 162
Analysis of Learning Algorithms	p. 170
Network Design	p. 172
Examples of Applications	p. 174
Associative Memory	p. 180
Biological Memory	p. 180
Hopfield Networks	p. 181
Memorization in a Hopfield Network	p. 181
Data Retrieval in a Hopfield Network	p. 183
The Convergence of the Process of Updating the Neurons	p. 185
Analyzing the Capacity of a Hopfield Network	p. 186
Application of a Hopfield Network	p. 189
Further Uses of the Hopfield Network	p. 191
Unsupervised Learning	p. 194
Self-Organizing Maps	p. 195
WEBSOM: Example of Using SOMs for Document Text Mining	p. 198
Summary	p. 200
Further Reading	p. 201
Exercises	p. 202
Single-Layer Perceptrons	p. 202
Multilayer Networks	p. 203
Hopfield Networks	p. 205
Self-Organizing Maps	p. 208
Summary	p. 208
Answers to Selected Exercises	p. 210
Molecular Computation	p. 215
Biological Background	p. 217
PCR: Polymerase Chain Reaction	p. 217
Gel Electrophoresis	p. 219
Restriction Enzymes	p. 219
Ligation	p. 220
Computation Using DNA	p. 220
Hamiltonian Paths	p. 220
Solving SAT	p. 230
DNA Tiling	p. 233
DNA Computing-Summary	p. 236
Enzymatic Computation	p. 237
Finite Automata	p. 238
Enzymatic Implementation of Finite Automata	p. 242
Summary	p. 248
Further Reading	p. 250
Exercises	p. 250
Biological Background	p. 250
Computing with DNA	p. 250
Enzymatic Computation	p. 253
Answers to Selected Exercises	p. 254
The Never-Ending Story: Additional Topics at the Interface between Biology and Computation	p. 259
Swarm Intelligence	p. 261
Ant Colony Optimization Algorithms	p. 262
Cemetery Organization, Larval Sorting, and Clustering	p. 264
Particle Swarm Optimization	p. 267
Artificial Immune Systems	p. 270
Identifying Intrusions in a Computer Network	p. 271
Artificial Life	p. 273
Avida	p. 276
Evolvable Virtual Creatures	p. 281
Systems Biology	p. 284
Evolution of Modularity	p. 287
Robustness of Biological Systems	p. 289
Formal Languages for Describing Biological Systems	p. 290
Summary	p. 294
Recommendations For Additional Reading	p. 297
Biological Introduction	p. 297
Personal Perspectives	p. 298
Modeling Biological Systems	p. 298
Biological Computation	p. 299
Cellular Automata	p. 299
Evolutionary Computation	p. 300
Neural Networks	p. 300
Molecular Computation	p. 300
Swarm Intelligence	p. 300
Systems Biology	p. 301
Bioinformatics	p. 301
Further Reading	p. 301
Exercises	p. 302
Swarm Intelligence	p. 302
Artificial Immune Systems	p. 303
Artificial Life	p. 305
Systems Biology	p. 306
Programming Exercises	p. 306
Answers to Selected Exercises	p. 307
Index	p. 311
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Biological Computation

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