Computational biology involves the use of techniques including applied mathematics, informatics, statistics, computer science, artificial intelligence, chemistry, and biochemistry to solve biological problems usually on the molecular level. The core principle of these techniques is using computing resources in order to solve problems on scales of magnitude far too great for human discernment. Research in computational biology often overlaps with systems biology. Major research efforts in the field include sequence alignment, gene finding, genome assembly, protein structure alignment, protein structure prediction, prediction of gene expression and protein-protein interactions, and the modeling of evolution.
Author(s): Alona S. Russe
Year: 2008
Language: English
Pages: 441
Computational Biology: New Research......Page 3
Contents......Page 7
Preface......Page 11
Introduction......Page 19
Gene Expression......Page 20
Databases......Page 21
Conclusions......Page 22
References......Page 23
Short Commentaries......Page 27
Abstract......Page 29
Introduction......Page 30
Protein Sequence and Structure Analysis......Page 31
Antibody Modeling and Druggability......Page 33
References......Page 34
Abstract......Page 37
Materials and Methods......Page 38
Results......Page 41
Conclusion......Page 42
References......Page 43
Introduction......Page 47
The Main Results......Page 50
Proofs of Theorems 2.1 and 2.2......Page 52
References......Page 55
Introduction......Page 57
Research Design: The First Step......Page 58
Database and Tool for Manipulation......Page 59
Simulation Experiment......Page 61
References......Page 62
Abstract......Page 65
Methods......Page 66
Results......Page 69
Conclusion......Page 72
References......Page 73
Research and Review Studies......Page 75
Sample Size Calculation and Power in Genomics Studies......Page 77
Introduction......Page 78
Preliminaries......Page 81
General Simulation-Based Approaches to Sample Size and Power Planning......Page 87
Methods Assuming Independence in Gene Expression......Page 91
Methods Accounting for Dependence in Gene Expression......Page 93
Methods for Controlling Family-Wise Error......Page 95
Other Approaches and Relevant Literature......Page 96
Acknowledgment......Page 97
References......Page 98
Abstract......Page 107
DNA Microarray and Bioinformatics......Page 108
Computational Prediction of E2F Binding Site Locations......Page 109
ChIP-on-Chip......Page 110
Systems Biology......Page 111
References......Page 112
Abstract......Page 117
Literature Review......Page 118
Mathematical Formulation......Page 120
Reformulating the Stochastic GAP......Page 121
Branch-and-Price Algorithm......Page 127
Computational Results......Page 134
Summary and Conclusions......Page 141
References......Page 144
Introduction......Page 147
The Standard Phylogenetic Analysis Pipeline......Page 149
Scaling up the Pipeline......Page 153
Challenges in the Analysis and Interpretation of Large-Scale Datasets......Page 155
Addressing Biological Questions Through Phylogenomics......Page 158
References......Page 161
Abstract......Page 165
Nucleosome/Chromatosome as the Chromatin Fundamental Subunit......Page 166
Nucleosome Computation......Page 167
Nucleosomal Arrays and the "30 nm Fiber"......Page 168
From Hard Models to In Silico Chromatin Fibers......Page 170
Why So Many Models?......Page 174
References......Page 175
Introduction......Page 183
Representing Protein Structures......Page 186
Modelling Structures with Contact Maps......Page 191
Contact Map Prediction......Page 194
Conclusions......Page 204
References......Page 206
Introduction......Page 211
Molecular Simulation: Normal Mode Analysis (NMA)......Page 214
Trion's Model: Elastic Network Model (ENM)......Page 215
Coarse-Grained Elastic Network Model......Page 217
Conformational Fluctuation Dynamics......Page 218
Lowest-Frequency Normal Mode......Page 220
Collective and Correlated Motion of Proteins......Page 221
Conformational Transition......Page 223
Acknowledgement......Page 226
References......Page 227
Abstract......Page 233
Introduction......Page 234
Materials and Methods......Page 235
Results......Page 238
Discussion......Page 242
Conclusion......Page 244
References......Page 245
Abstract......Page 249
Introduction......Page 250
Sibling Reconstruction Problem......Page 252
Genetic of Sibship......Page 253
Methods for Full Sibling Reconstruction......Page 255
Experimental Validation......Page 265
Conclusion......Page 270
Acknowledgments......Page 271
References......Page 272
Abstract......Page 277
Introduction......Page 278
Current Issues in the Development of Prognostic Gene Signatures......Page 279
Current Computational Approaches for Developing Prognostic Gene Signatures......Page 282
Acknowledgements......Page 287
References......Page 288
Introduction......Page 295
Results......Page 304
Acknowledgments......Page 325
References......Page 326
Abstract......Page 333
Introduction......Page 334
Structural Class Definitions......Page 336
Prediction of Secondary Structure Content......Page 338
Methods of Secondary Structural Class Prediction......Page 339
Conclusion......Page 354
References......Page 355
Abstract......Page 359
Introduction: Grounds for Computability and the Peircean Paradigm......Page 360
Quantum Mechanical Basis of Computability......Page 362
Robert Rosen's Theory of (M,R) Systems and Autopoiesis......Page 364
Organizational Invariance as the Principle of Optimal Design......Page 368
Internal Computability of Metabolic Systems......Page 369
Computability of the Evolutionary Process......Page 371
Computable Structures in Psychology......Page 372
Conclusion: Rosen's "Central Result" and Computability......Page 374
References......Page 375
Introduction......Page 379
Procedure of LDSS Analysis......Page 383
Examples of LDSS Analysis......Page 387
References......Page 389
Abstract......Page 393
Introduction......Page 394
Mathematical Model......Page 396
Model Evaluation......Page 398
Support Vector Machine......Page 399
Bioinformatical Approach......Page 401
Acknowledgement......Page 404
References......Page 405
Introduction......Page 407
Introduction to Lua......Page 409
Embedding a Lua Interpreter......Page 412
An Application to Molecular Graphics......Page 417
Discussion......Page 420
Conclusion......Page 422
References......Page 423
Introduction to Computational Hematology......Page 425
Computational Medicine Research on Hemoglobin Disorder......Page 426
Computational Medicine Research on Prothrombin Disorder......Page 431
References......Page 433
Index......Page 437
