Contributors xiii

1. Qualitative Knowledge Models in Functional Genomics and Proteomics 1
Mor Peleg, Irene S. Gabashvili, and Russ B. Altman

1.1. Introduction 1

1.2. Methods and Tools 3

1.3. Modeling Approach and Results 6

1.4. Discussion 19

1.5. Conclusion 20

References 21

2. Interpreting Microarray Data and Related Applications Using Nonlinear System Identification 25
Michael Korenberg

2.1. Introduction 25

2.2. Background 25

2.3. Parallel Cascade Identification 30

2.4. Constructing Class Predictors 34

2.5. Prediction Based on Gene Expression Profiling 35

2.6. Comparing Different Predictors Over the Same Data Set 46

2.7. Concluding Remarks 48

References 49

3. Gene Regulation Bioinformatics of Microarray Data 55
Gert Thijs, Frank De Smet, Yves Moreau, Kathleen Marchal, and Bart De Moor

3.1. Introduction 55

3.2. Introduction to Transcriptional Regulation 57

3.3. Measuring Gene Expression Profiles 59

3.4. Preprocessing of Data 61

3.5. Clustering of Gene Expression Profiles 63

3.6. Cluster Validation 70

3.7. Searching for Common Binding Sites of Coregulated Genes 76

3.8. Inclusive: Online Integrated Analysis of Microarray Data 87

3.9. Further Integrative Steps 89

3.10. Conclusion 90

References 91

4. Robust Methods for Microarray Analysis 99
George S. Davidson, Shawn Martin, Kevin W. Boyack, Brian N. Wylie, Juanita Martinez, Anthony Aragon, Margaret Werner-Washburne, Mónica Mosquera-Caro, and Cheryl Willman

4.1. Introduction 99

4.2. Microarray Experiments and Analysis Methods 100

4.3. Unsupervised Methods 103

4.4. Supervised Methods 117

4.5. Conclusion 127

References 128

5. In Silico Radiation Oncology: A Platform for Understanding Cancer Behavior and Optimizing Radiation Therapy Treatment 131
G. Stamatakos, D. Dionysiou, and N. Uzunoglu

5.1. Philosophiae Tumoralis Principia Algorithmica: Algorithmic Principles of Simulating Cancer on Computer 131

5.2. Brief Literature Review 133

5.3. Paradigm of Four-Dimensional Simulation of Tumor Growth and Response to Radiation Therapy In Vivo 135

5.4. Discussion 148

5.5. Future Trends 150

References 150

6. Genomewide Motif Identification Using a Dictionary Model 157
Chiara Sabatti and Kenneth Lange

6.1. Introduction 157

6.2. Unified Model 160

6.3. Algorithms for Likelihood Evaluation 164

6.4. Parameter Estimation via Minorization–Maximization Algorithm 167

6.5. Examples 170

6.6. Discussion and Conclusion 171

References 172

7. Error Control Codes and the Genome 173
Elebeoba E. May

7.1. Error Control and Communication: A Review 173

7.2. Central Dogma as Communication System 180

7.3. Reverse Engineering the Genetic Error Control System 184

7.4. Applications of Biological Coding Theory 203

References 205

8. Complex Life Science Multidatabase Queries 209
Zina Ben Miled, Nianhua Li, Yue He, Malika Mahoui, and Omran Bukhres

8.1. Introduction 209

8.2. Architecture 212

8.3. Query Execution Plans 214

8.4. Related Work 219

8.5. Future Trends 222

References 223

9. Computational Analysis of Proteins 227
Dimitrios I. Fotiadis, Yorgos Goletsis, Christos Lampros, and Costas Papaloukas

9.1. Introduction: Definitions 227

9.2. Databases 229

9.3. Sequence Motifs and Domains 232

9.4. Sequence Alignment 235

9.5. Modeling 241

9.6. Classification and Prediction 242

9.7. Natural Language Processing 248

9.8. Future Trends 252

References 252

10. Computational Analysis of Interactions Between Tumor and Tumor Suppressor Proteins 257
E. Pirogova, M. Akay, and I. Cosic

10.1. Introduction 257

10.2. Methodology: Resonant Recognition Model 261

10.3. Results and Discussions 265

10.4. Conclusion 284

References 285

Index 289

About the Editor 299