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Elements of Computational Systems Biology (Wiley Series in Bioinformatics)

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Groundbreaking, long-ranging research in this emergent field that enables solutions to complex biological problems

Computational systems biology is an emerging discipline that is evolving quickly due to recent advances in biology such as genome sequencing, high-throughput technologies, and the recent development of sophisticated computational methodologies. Elements of Computational Systems Biology is a comprehensive reference covering the computational frameworks and techniques needed to help research scientists and professionals in computer science, biology, chemistry, pharmaceutical science, and physics solve complex biological problems. Written by leading experts in the field, this practical resource gives detailed descriptions of core subjects, including biological network modeling, analysis, and inference; presents a measured introduction to foundational topics like genomics; and describes state-of-the-art software tools for systems biology.

  • Offers a coordinated integrated systems view of defining and applying computational and mathematical tools and methods to solving problems in systems biology

  • Chapters provide a multidisciplinary approach and range from analysis, modeling, prediction, reasoning, inference, and exploration of biological systems to the implications of computational systems biology on drug design and medicine

  • Helps reduce the gap between mathematics and biology by presenting chapters on mathematical models of biological systems

  • Establishes solutions in computer science, biology, chemistry, and physics by presenting an in-depth description of computational methodologies for systems biology

Elements of Computational Systems Biology is intended for academic/industry researchers and scientists in computer science, biology, mathematics, chemistry, physics, biotechnology, and pharmaceutical science. It is also accessible to undergraduate and graduate students in machine learning, data mining, bioinformatics, computational biology, and systems biology courses.

Author(s): Huma M. Lodhi, Stephen H. Muggleton
Series: Wiley Series in Bioinformatics
Publisher: Wiley
Year: 2010

Language: English
Pages: 435

ELEMENTS OF COMPUTATIONAL SYSTEMS BIOLOGY......Page 5
CONTENTS......Page 7
PREFACE......Page 17
CONTRIBUTORS......Page 19
PART I OVERVIEW......Page 23
1.1 Introduction......Page 25
1.2 Multiscale Computational Modeling......Page 26
1.3 Proteomics......Page 29
1.4 Computational Systems Biology and Aging......Page 30
1.5 Computational Systems Biology in Drug Design......Page 31
1.6 Software Tools for Systems Biology......Page 33
References......Page 35
PART II BIOLOGICAL NETWORK MODELING......Page 41
2.1 Introduction......Page 43
2.2 Models as Dynamical Systems......Page 45
2.2.1 Continuous Models......Page 46
2.2.2 Discrete Models......Page 47
2.3 The Parameter Problem......Page 48
2.3.2 Measuring and Calculating......Page 49
2.3.3 Counter Fitting......Page 50
2.4.1 Qualitative Dynamics......Page 51
2.4.2 Steady State Attractors of ODE Models......Page 55
2.5.1 Parameter Biology......Page 57
2.5.2 Robustness to Initial Conditions......Page 59
2.5.3 Robustness in Reality......Page 60
2.5.4 Structural Stability......Page 62
2.6 Conclusion......Page 64
References......Page 65
3.1 Introduction......Page 71
3.2.1 Model Construction and Validation......Page 72
3.2.2 Classification of Different Heart Failure Cases......Page 73
3.3.1 β-Adrenergic Receptor Antagonists......Page 76
3.3.2 β-Adrenergic Receptor Kinase Inhibitor......Page 78
3.3.3 Phosphodiesterase Inhibitor......Page 79
3.3.4 Combined Therapies......Page 80
Acknowledgment......Page 83
3A.1.1 Model Validation......Page 84
3A.1.2 The Mathematical Model Used for Simulations......Page 86
References......Page 102
4 Rule-Based Modeling and Model Refinement......Page 105
4.1 Kappa, Briefly......Page 106
4.2.1 A Simple Cascade......Page 107
4.2.2 Another Cascade......Page 111
4.2.3 The SSA Convention......Page 116
4.2.4 A Less Obvious Refinement......Page 118
4.3 Rule-Based Modeling......Page 120
4.3.2 Objects and Arrows......Page 121
4.3.4 Actions and Rules......Page 123
4.3.5 Events and Probabilities......Page 124
4.4.1 Growth Policies......Page 125
4.4.2 Simple Growth Policies......Page 126
4.4.3 Neutral Refinements......Page 127
4.4.5 Growth Policies, Concretely......Page 129
4.4.6 A Weakly Homogeneous Refinement......Page 131
4.4.7 Nonhomogeneous Growth Policies......Page 133
4.5 Conclusion......Page 135
References......Page 136
5.1 Natural Computing and Computational Biology......Page 137
5.2 Membrane Computing......Page 138
5.3 Formal Languages Preliminaries......Page 140
5.4 Membrane Operations with Peripheral Proteins......Page 141
5.5.1 Dynamics of the System......Page 144
5.5.2 Reachability in Membrane Systems......Page 146
5.6 Cell Cycle and Breast Tumor Growth Control......Page 148
5.6.1 Cell Cycle Progression Inhibition in G1/S......Page 149
5.6.2 Cell-Cycle Progression Inhibition in G2/M......Page 154
References......Page 160
6.1 Introduction......Page 163
6.2.1 The Actin Network......Page 164
6.2.2 Intermediate Filaments......Page 165
6.2.3 Microtubules......Page 166
6.3.1 Actin-Based Motility in Listeria......Page 167
6.3.3 Spindle Positioning in Caenorhabditis Elegans Embryos......Page 168
6.4 Overview of Filament Simulation......Page 169
6.5.1 Resegmenting Filament......Page 171
6.6.1 Brownian Forces......Page 173
6.6.2 Straightening Force......Page 174
6.7.1 Motivation......Page 176
6.7.2 Derivation......Page 177
6.7.3 Implementation......Page 178
6.7.4 State Equation......Page 179
6.8 Solver......Page 180
6.9 Conclusion......Page 181
References......Page 182
PART III BIOLOGICAL NETWORK INFERENCE......Page 185
7.1 Introduction......Page 187
7.2.1 Problem Formalization......Page 190
7.2.2 Pattern Recognition......Page 191
7.2.4 Graph Inference with Local Models......Page 193
7.2.5 Graph Inference with Global Models......Page 195
7.2.6 Remarks......Page 200
7.3.1 Reconstruction of a Metabolic Network......Page 203
7.3.2 Reconstruction of a PPI Network......Page 204
7.3.3 Reconstruction of Gene Regulatory Networks......Page 205
7.4 Discussion......Page 207
References......Page 208
8.1 Introduction......Page 211
8.2.2 Genomic Data......Page 214
8.2.4 Kernel Representation......Page 215
8.3.1 Formalism of the Problem......Page 216
8.3.2 From Metric Learning to Graph Inference......Page 217
8.4.1 Kernel Canonical Correlation Analysis (KCCA)......Page 218
8.4.2 Distance Metric Learning (DML)......Page 219
8.4.3 Kernel Matrix Regression (KMR)......Page 221
8.4.4 Penalized Kernel Matrix Regression (PKMR)......Page 222
8.4.5 Relationship with Kernel Matrix Completion and em-algorithm......Page 223
8.5.2 Chemical Compatibility Network......Page 225
8.6 Experiments......Page 226
8.7 Discussion and Conclusion......Page 229
References......Page 231
9.1 Introduction......Page 235
9.2.1 Metabolic Pathways......Page 237
9.2.2 Regulation of Enzymatic Activities......Page 239
9.3.1 Inductive Logic Programming......Page 240
9.3.2 Abduction and Induction in CF-induction......Page 243
9.4.1 A Simple Pathway......Page 246
9.4.2 A Metabolic Pathway of Pyruvate......Page 249
9.5 Related Work......Page 252
Acknowledgments......Page 254
References......Page 255
10.1 Introduction......Page 257
10.2 Boolean Network......Page 258
10.3.1 Definition of BN-ATTRACTOR......Page 260
10.3.2 Basic Recursive Algorithm......Page 261
10.4 Control of Boolean Network......Page 263
10.4.1 Definition of BN-CONTROL......Page 264
10.4.2 Dynamic Programming Algorithms for BN-CONTROL......Page 265
10.4.3 NP-hardness Results on BN-CONTROL......Page 266
10.5 Probabilistic Boolean Network......Page 268
10.6.1 Exact Computation of PBN-STEADY......Page 270
10.6.2 Approximate Computation of PBN-STEADY......Page 271
10.7.1 Dynamic Programming Algorithm for PBN-CONTROL......Page 272
10.7.2 Variants of PBN-CONTROL......Page 273
10.8 Conclusion......Page 275
References......Page 276
11.1 Introduction to Probabilistic Methods......Page 279
11.2 Sequence Evolution is Described Using Markov Chains......Page 280
11.2.1 Estimating Pairwise Distances......Page 282
11.2.2 Calculating the Likelihood of a Tree......Page 283
11.2.3 Extending the Basic Model......Page 284
11.3 Among-site Rate Variation......Page 285
11.4.1 The Gamma Distribution......Page 288
11.4.2 Numerical Approximation of the Continuous Gamma Distribution......Page 290
11.4.3 Alternative Rate Distributions......Page 291
11.5 Site-specific Rate Estimation......Page 293
11.6 Tree Reconstruction Using Among-site Rate Variation Models......Page 294
11.7 Dependencies of Evolutionary Rates Among Sites......Page 296
11.8 Related Works......Page 297
References......Page 298
PART IV GENOMICS AND COMPUTATIONAL SYSTEMS BIOLOGY......Page 303
12.1 Introduction......Page 305
12.2.1 Physical Basis of Transcription Regulation and Representation of DNA Patterns......Page 308
12.2.2 High-Throughput Data: Microarrays, Deep Sequencing, ChIP-chip, and ChIP-seq......Page 309
12.3.1 Basic Definitions......Page 311
12.3.3 Clustering-Based Approaches......Page 312
12.3.4 Sequence- or ChIP-Based Regression Methods......Page 315
12.3.5 Network Component Analysis Methods......Page 318
12.3.6 Factor Analysis Methods......Page 321
12.4 Conclusion......Page 323
Acknowledgments......Page 326
References......Page 327
13.1 Introduction......Page 331
13.2 Nuclear Receptors......Page 332
13.2.1 NRs as a Link between Nutrition Sensing and Inflammation Prevention......Page 333
13.2.2 NRs and System Biology......Page 334
13.3 The PPAR Subfamily......Page 335
13.3.1 Global Datasets that Identify a Central Role for PPARs in Disease Progression......Page 336
13.4 Methods for in Silico Screening of Transcription Factor-Binding Sites......Page 337
13.5 Binding Dataset of PPREs and the Classifier Method......Page 339
13.6 Clustering of Known PPAR Target Genes......Page 340
13.7 Conclusion......Page 341
Acknowledgments......Page 342
References......Page 343
14.1 Some Background, Motivation, and Open Questions......Page 347
14.2 A First Statistical Glimpse to Genomic Sequences......Page 350
14.3 An Automatic Detection of Codon Bias in Genes......Page 351
14.4 Genomic Signatures and a Space of Genomes for Genome Comparison......Page 352
14.5 Study of Metabolic Networks Through Sequence Analysis and Transcriptomic Data......Page 353
14.6 From Genome Sequences to Genome Synthesis: Minimal Gene Sets and Essential Genes......Page 354
14.7 A Chromosomal Organization of Essential Genes......Page 355
14.8 Viral Adaptation to Microbial Hosts and Viral Essential Genes......Page 356
14A.1.1 Some Comments on the Mathematical Methods......Page 357
References......Page 358
PART V SOFTWARE TOOLS FOR SYSTEMS BIOLOGY......Page 363
15.1 Introduction to Text Mining......Page 365
15.2 ALI BABA as a Tool for Mining Biological Facts from Literature......Page 368
15.2.2 What are the Risk Factors of Treating G6PD-Deficient Malaria Patients with Primaquine?......Page 369
15.3 Components and usage of ALI BABA......Page 371
15.3.1 Views......Page 372
15.3.2 Navigation......Page 373
15.3.3 Filters......Page 374
15.3.5 Graph Editor......Page 375
15.4.1 Natural Language Processing......Page 376
15.4.2 Named Entity Recognition and Identification......Page 377
15.4.3 Word Sense Disambiguation......Page 378
15.4.4 Relation Mining......Page 379
15.4.5 Evaluation of Components......Page 381
15.5.2 Entity Extraction......Page 383
15.6 Conclusions and Future Perspectives......Page 384
References......Page 386
16.1 Introduction......Page 391
16.2 Data Types......Page 392
16.3 Data Integration......Page 393
16.4.1 Internal Validation Methods......Page 396
16.4.2 Measures of Biological Significance......Page 398
References......Page 400
17 Computational Imaging and Modeling for Systems Biology......Page 403
17.1.2 Mass Spectrometry......Page 405
17.1.3 Molecular Networks and Pathways......Page 406
17.2 Bioimage Informatics of High-Content Screening......Page 407
17.2.2 Cell Detection, Segmentation, and Centerline Extraction......Page 408
17.2.4 Feature Extraction......Page 410
17.3.1 Cellular Networks Analysis by Using HCS......Page 411
17.3.3 Association Studies with Clinical Imaging Traits......Page 412
17.3.4 In Vivo Genomics Analysis......Page 413
17.3.5 In Vivo Proteomics Analysis......Page 414
17.3.7 In Vivo RNAi Experiments......Page 415
References......Page 416
INDEX......Page 425
SERIES INFORMATION......Page 435