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Elements of computational systems biology / edited by Huma M. Lodhi, Stephen H. Muggleton. — Hoboken, NJ : Wiley, c2010. – (58.1483/E38) |
Contents
CONTENTS
PREFACE
CONTRIBUTORS
PART I OVERVIEW
1 Advances in Computational Systems Biology
1.1 Introduction / 3
1.2 Multiscale Computational Modeling / 4
1.3 Proteomics / 7
1.4 Computational Systems Biology and Aging / 8
1.5 Computational Systems Biology in Drug Design / 9
1.6 Software Tools for Systems Biology / 11
1.7 Conclusion / 13
References / 13
PART II BIOLOGICAL NETWORK MODELING
2 Models in Systems Biology: The Parameter Problem and the Meanings of Robustness
2.1 Introduction / 21
2.2 Models as Dynamical Systems / 23
2.3 The Parameter Problem / 26
2.4 The Landscapes of Dynamics / 29
2.5 The Meanings of Robustness / 35
2.6 Conclusion / 42
References / 43
3 In Silico Analysis of Combined Therapeutics Strategy for Heart Failure
3.1 Introduction / 49
3.2 Materials and Methods / 50
3.3 Results / 54
3.4 Discussion / 61
Acknowledgment / 61
3A.1 Appendix / 62
References / 80
4 Rule-Based Modeling and Model Refinement
4.1 Kappa， Briefly / 84
4.2 Refinement， Practically / 85
4.3 Rule-Based Modeling / 98
4.4 Refinement， Theoretically / 103
4.5 Conclusion / 113
References / 114
5 A (Natural) Computing Perspective on Cellular Processes 115
5.1 Natural Computing and Computational Biology / 115
5.2 Membrane Computing / 116
5.3 Formal Languages Preliminaries / 118
5.4 Membrane Operations with Peripheral Proteins / 119
5.5 Membrane Systems with Peripheral Proteins / 122
5.6 Cell Cycle and Breast Tumor Growth Control / 126
References / 138
6 Simulating Filament Dynamics in Cellular Systems 141
6.1 Introduction / 141
6.2 Background: The Roles of Filaments within Cells / 142
6.3 Examples of Filament Simulations / 145
6.4 Overview of Filament Simulation / 147
6.5 Changing Filament Length / 149
6.6 Forces on Filaments / 151
6.7 Imposing Constraints / 154
6.8 Solver / 158
6.9 Conclusion / 159
References / 160
PART III BIOLOGICAL NETWORK INFERENCE
7 Reconstruction of Biological Networks by Supervised Machine Learning Approaches
7.1 Introduction / 165
7.2 Graph Reconstruction as a Pattern Recognition Problem / 168
7.3 Examples / 181
7.4 Discussion / 185
References / 186
8 Supervised Inference of Metabolic Networks from the Integration of Genomic Data and Chemical Information 189
8.1 Introduction / 189
8.2 Materials / 192
8.3 Supervised Network Inference with Metric Learning / 194
8.4 Algorithms for Supervised Network Inference / 196
8.5 Data Integration / 203
8.6 Experiments / 204
8.7 Discussion and Conclusion / 207
References / 209
9 Integrating Abduction and Induction in Biological Inference Using CF-Induction
9.1 Introduction / 213
9.2 Logical Modeling of Metabolic Flux Dynamics / 215
9.3 CF-induction / 218
9.4 Experiments / 224
9.5 Related Work / 230
9.6 Conclusion and Future Work / 232
Acknowledgments / 232
References / 233
10 Analysis and Control of Deterministic and Probabilistic Boolean Networks
10.1 Introduction / 235
10.2 Boolean Network / 236
10.3 Identification of Attractors / 238
10.4 Control of Boolean Network / 241
10.5 Probabilistic Boolean Network / 246
10.6 Computation of Steady States of PBN / 248
10.7 Control of Probabilistic Boolean Networks / 250
10.8 Conclusion / 253
Acknowledgments / 254
References 254
11 Probabilistic Methods and Rate Heterogeneity 257
l1.1 Introduction to Probabilistic Methods / 257
11.2 Sequence Evolution is Described Using Markov Chains / 258
11.3 Among-site Rate Variation / 263
11.4 Distribution of Rates Across Sites / 266
11.5 Site-specific Rate Estimation / 271
11.6 Tree Reconstruction Using Among-site Rate Variation Models / 272
11.7 Dependencies of Evolutionary Rates Among Sites / 274
11.8 Related Works / 275
References / 276
PART IV GENOMICS AND COMPUTATIONAL SYSTEMS BIOLOGY
12 From DNA Motifs to Gene Networks: A Review of Physical Interaction Models
12.1 Introduction / 283
12.2 Fundamentals of Gene Transcription / 286
12.3 Physical Interaction Algorithms / 289
12.4 Conclusion / 301
Acknowledgments / 304
References / 305
13 The Impact of Whole Genome In Silico Screening for Nuclear Receptor-Binding Sites in Systems Biology 309
13.1 Introduction / 309
13.2 Nuclear Receptors / 310
13.3 The PPAR Subfamily / 313
13.4 Methods for in Silico Screening of Transcription Factor-Binding Sites / 315
13.5 Binding Dataset of PPREs and the Classifier Method / 317
13.6 Clustering of Known PPAR Target Genes / 318
13.7 Conclusion / 319
Acknowledgments / 320
References / 321
14 Environmental and Physiological Insights from Microbial Genome Sequences
14.1 Some Background， Motivation， and Open Questions / 325
14.2 A First Statistical Glimpse to Genomic Sequences / 328
14.3 An Automatic Detection of Codon Bias in Genes / 329
14.4 Genomic Signatures and a Space of Genomes for Genome Comparison / 330
14.5 Study of Metabolic Networks Through Sequence Analysis and Transcriptomic Data / 331
14.6 From Genome Sequences to Genome Synthesis: Minimal Gene Sets and Essential Genes / 332
14.7 A Chromosomal Organization of Essential Genes / 333
14.8 Viral Adaptation to Microbial Hosts and Viral Essential Genes / 334
14A.1 Appendix / 335
References / 336
PART V SOFTWARE TOOLS FOR SYSTEMS BIOLOGY
15 ALI BABA: A Text Mining Tool for Systems Biology 343
15.1 Introduction to Text Mining / 343
15.2 ALI BABA as a Tool for Mining Biological Facts from Literature / 346
15.3 Components and usage of ALI BABA / 349
15.4 ALI BABA'S Approach to Text Mining / 354
15.5 Related Biomedical Text Mining Tools / 361
15.6 Conclusions and Future Perspectives / 362
Acknowledgments / 364
References / 364
16 Validation Issues in Regulatory Module Discovery 369
16.1 Introduction / 369
16.2 Data Types / 370
16.3 Data Integration / 371
16.4 Validation Approaches / 374
16.5 Conclusions / 378
References / 378
17 Computational Imaging and Modeling for Systems Biology 381
17.1 Bioinformatics / 383
17.2 Bioimage Informatics of High-Content Screening / 385
17.3 Connecting Bioinformatics and Biomedical Imaging / 389
17.4 Summary / 394
Acknowledgments / 394
References / 394
INDEX
SERIES INFORMATION
