Table Of ContentEvolutionary Compt ration in Bioinformatics
About the Editors
Gary B. Fogel is a senior staff scientist at Natural Selection, Inc., in La Jolla, Cali-
fornia. His research interests include the application of evolutionary computation
to problems in the biomedical sciences and evolutionary biology. He received a
B.A. in biology from the University of California, Santa Cruz, in 1991 and a Ph.D.
in biology from the University of California, Los Angeles, in 1998 with a focus on
evolutionary biology. While at UCLA, Dr. Fogel was a fellow of the Center for the
Study of Evolution and the Origin of Life and earned several teaching and re-
search awards. He is a current member of the International Society for the Study
of the Origin of Life, the Society for the Study of Evolution, IEEE, Sigma Xi, and
the Evolutionary Programming Society. He currently serves as an associate editor
for IEEE Transactions on Evolutionary Computation and BioSystems and was a technical
co-chair for the recent 2000 Congress on Evolutionary Computation. He is also
a senior staff scientist at the Center for Excellence in Evolutionary Computation,
a nonprofit organization that promotes scientific research and development of
evolutionary algorithms.
David W. Corne is a reader in evolutionary computation (EC) at the University
of Reading. His early research on evolutionary timetabling (with Peter Ross) re-
suited in the first freely available and successful EC-based general timetabling
program for educational and other institutions. His later EC work has been in
such areas as DNA pattern mining, promoter modeling, phylogeny, scheduling,
layout design, telecommunications, data mining, algorithm comparison issues,
and multi-objective optimization. Recent funded work (with Douglas Kell, Roy
Goodacre, Leighton Pritchard, and Mike Winson) applies EC directly, using
experimental biology techniques, to the generation of novel proteins. Dr. Corne
is an associate editor of the IEF,E Transactions on Evolutionary Computation and a
founding coeditor of the Journal of Scheduling, and he serves on the editorial
boards of Applied Soft Computing and the International Journal of Systems Science. He
is also a director of Evosolve (United Kingdom registered charity number 1086384,
with Jeanne Lynch-Aird, Paul Marrow, Glenys Oates, and Martin Oates), a non-
profit organization that promotes the use of advanced computation technologies
to enhance the quality of life.
Evol utiona ry Com putation
in Bioinformatics
Edited by
Gary B. Fogel
Natural Selection, Inc.
David W. Corne
Department of Computer Science
University of Reading
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M ~4
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To the long list of advisers who helped educate me from high school through
college, and to my colleagues at Natural Selection, Inc.
mGBF
To the generation that, with tremendous courage in adversity, cradled my genes
safely into the twentieth century: Sophia Corne, Wolfe Corne, Rachel Wiesberg,
and Samuel Wiesberg.
mDWC
This Page Intentionally Left Blank
Contents
Preface xvii
Contributors xix
Color Plates Following page 202
PART I Introduction to the Concepts of Bioinformatics
and Evolutionary Computation
1 An Introduction to Bioinformatics
for Computer Scientists
By David W. Come and Gary B. Fogel
1.1 Introduction 3
1.2 BiologymThe Science of Life 5
1.3 The Central Dogma of Molecular Biology 6
1.3.1 Anatomy of a DNA Sequence 10
1.3.2 Transcription and Translation 11
1.3.3 Proteins 12
1.4 Gene Networks 15
1.5 Sequence Alignment 16
1.6 Concluding Remarks 17
Contents
viii
~ ~ ~ ~ ~ ~ ~ = ~ ~ ~ ~
2 An Introduction to Evolutionary Computation
for Biologists 19
By Gary B. Fogel and David W. Come
2.1 Introduction 19
2.2 Evolutionary Computation: History and Terminology 20
2.2.1 Initialization 22
2.2.2 Variation 22
2.2.3 Selection 24
2.2.4 Additional Parameters and Testing 25
2.2.5 Self-Adaptation 25
2.2.6 Theory 26
2.3 Evolutionary Computation within the Context
of Computer Science 26
2.3.1 LocalSearch 29
2.3.2 Simulated Annealing 32
2.3.3 Population-Based Search 33
2.4 Concluding Remarks 35
PART II Sequence and Structure Alignment 39
3 Determining Genome Sequences from Experimental
Data Using Evolutionary Computation 41
By Jacek Blazewicz and Marta Kasprzak
3.1 Introduction 41
3.1.1 Sequencing by Hybridization 41
3.1.2 Example: Reconstruction of Sequence from an Ideal Spectrum 43
3.1.3 Experimental Errors in the Spectrum 43
3.2 Formulation of the Sequence Reconstruction Problem 45
3.2.1 An Integer Programming Formulation 45
3.2.2 Example: Reconstruction of Sequence from a Spectrum
Containing Errors 46
3.3 A Hybrid Genetic Algorithm for Sequence Reconstruction 47
3.4 Results from Computational Experiments 49
3.5 Concluding Remarks 55
Contents
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4 Protein Structure Alignment Using
Evolutionary Computation 59
By Joseph D. Szustakowski and Zhipeng Weng
4.1 Introduction 59
4.1.1 Structure Alignment Algorithms 60
4.1.2 The K2 Algorithm 61
4.2 Methods 62
4.2.1 Stage 1: SSE Alignments 63
4.2.2 Stage 2: Detailed Alignments Using a Genetic Algorithm 65
4.2.3 Stage 3: Three-Dimensional Superposition 69
4.2.4 Evaluation of Statistical Significance 69
4.3 Results and Discussion 71
4.3.1 Difficult Cases 72
4.3.2 GA Performance Characteristics 73
4.3.3 Statistical Significance 79
4.4 Concluding Remarks 84
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5 Using Genetic Algorithms for Pairwise and
Multiple Sequence Alignments 87
By Cidric Notredame
5.1 Introduction 87
5.1.1 Standard Optimization Algorithms 88
5.1.2 The Objective Function 89
5.2 Evolutionary Algorithms and Simulated Annealing 91
5.3 SAGA: A Genetic Algorithm Dedicated to Sequence Aligmnent 92
5.3.1 Initialization 92
5.3.2 Evaluation 93
5.3.3 Reproduction, Variation, and Termination 94
5.3.4 Designing the Variation Operators 94
5.3.5 Crossover Operators 95
5.3.6 Mutation Operators: A Gap Insertion Operator 95
5.3.7 Dynamic Scheduling of Operators 95
5.3.8 Parallelization of SAGA 98
5.4 Applications: Choice of an Appropriate Objective Function 99
5.4.1 Weighted Sums of Pairs 100
5.4.2 Consistency-Based Objective Functions: The COFFEE Score 101
5.4.3 Taking Nonlocal Interactions into Account: RAGA 103
5.5 Concluding Remarks 105
