This volume contains 25 peer-reviewed papers based on the presentations at the 8th Annual International Workshop on Bioinformatics and Systems Biology (IBSB 2008) held at the Teikyo Hotel, Zeuten Lake, near Berlin, from June 9 to June 10, 2008. This workshop started in 2001 as an event for doctoral students and young researchers to present and discuss their research results and approaches in bioinformatics and systems biology. It is part of a collaborative educational program involving leading institutions and leaders committed to the following programs and partner institutions: Boston (Gary Benson) - Graduate Program in Bioinformatics, Boston University; Berlin (Herman-Georg Holzhutter) - The International Research Training Group (IRTG) Genomics and Systems Biology of Molecular Networks; and, Kyoto/Tokyo (Minoru Kanehisa/Satoru Miyano) - Joint Bioinformatics Education Program of Kyoto University and University of Tokyo.
Author(s): Ernst-walter Knapp, Gary Benson, Herman-georg Holzhutter, Minoru Kanehisa, Satoru Miyano
Series: Genome Informatics Series
Edition: 1
Publisher: World Scientific Publishing Company
Year: 2008
Language: English
Pages: 301
CONTENTS......Page 6
Preface......Page 10
Program Committee......Page 12
1. Introduction......Page 14
2.2. Mathematical model......Page 16
2.3. Transcriptional regulation and external metabolites......Page 19
2.4. Parameter estimation......Page 21
2.5. Genetic algorithm and semi global search......Page 22
3.1. Parameter estimation......Page 23
3.2. Comparison to experimental data......Page 24
4. Discussion......Page 25
Appendix......Page 26
References......Page 27
1. Introduction......Page 28
2.1. IPaR Model......Page 30
2.2. CellModel......Page 31
2.3. Results......Page 33
3. Discussion......Page 36
References......Page 37
1. Introduction......Page 38
2. Cell System Ontology......Page 39
3. Rule-Based Reasoning for Ontology Validation......Page 41
3.2. Biologically correct models......Page 42
3.3. Systematically correct models......Page 44
5. Conclusions......Page 46
References......Page 48
1. Introduction......Page 50
2.1.1. Vector autoregressive model......Page 51
2.1.3. Bayesian information criterion......Page 52
2.2. L1 regularized spline additive model for gene regulatory network estimation......Page 53
2.3. Bayesian information criterion for nonparametric group LASSO regression......Page 54
2.4. Wald test for Granger causality......Page 56
3.1. Simulation data examples......Page 57
3.2. Application of expression data of human hela cell......Page 59
4. Discussion......Page 61
References......Page 62
Appendix A.......Page 63
1. Introduction......Page 65
1.1. The Idea......Page 66
2.1. Generating Alternative Models......Page 67
2.1.1. Removing Reactions and Modifiers......Page 68
2.1.2. Removing Species......Page 69
2.2. Model Discrimination......Page 70
3.1. Example......Page 72
3.2. Conclusions......Page 74
References......Page 75
1. Introduction......Page 77
2.1. Notation......Page 78
2.2. Related Work......Page 79
3. Framework......Page 80
4. Outlier Detection......Page 81
5. Probe Cleaning......Page 82
6. Statistical Methods......Page 83
7.1. Simulated Microarray Data......Page 84
7.3. Results......Page 85
8. Conclusion......Page 87
References......Page 88
1. Introduction......Page 90
2.1. Details of the model......Page 91
2.2. Experimental data and parameter estimation......Page 94
3.1. Simulation of model......Page 95
3.2. Time-varying response coefficients......Page 97
4. Conclusion......Page 101
References......Page 102
1. Introduction......Page 104
2. Theory......Page 105
3. Results......Page 108
4. Discussion......Page 112
References......Page 113
1. Introduction......Page 115
2.2. Graph Cut Indices......Page 117
3. Data and Methodology......Page 118
4. Results and Discussion......Page 121
5. Conclusions......Page 123
References......Page 124
1.1. Correlations of metabolite concentration data......Page 125
1.3. Entropy, mutual information and statistical (in)dependence......Page 126
2.1. Non-linear correlations are captured by mutual information......Page 128
2.2. Significance of the coefficients given the limited sample size......Page 129
3.1. Correlations among metabolite concentrations from Arabidopsis thaliana......Page 131
4. Conclusion......Page 133
References......Page 134
1. Introduction......Page 136
2.1. Experimentaljlux data......Page 137
3.1. Experimentally determinedjluxes......Page 138
3.2.1. Growth maximization......Page 140
3.2.4. Alternate maximization criteria......Page 141
3.3. Correlations between experimental and predicted fluxes......Page 142
3.4. Prediction of absolute flUX changes......Page 144
4. Discussion......Page 145
References......Page 146
1. Introduction......Page 148
2.1. Species-specific networks......Page 150
2.2. Biosynthetic potential of metabolites via scope......Page 151
2.5. Evaluation of parameter values......Page 152
3.1. Scope size distributions......Page 153
3.2. Cluster agglomeration......Page 155
3.3. Influence of cut-off and seed size......Page 157
4. Discussion......Page 159
References......Page 160
1. Introduction......Page 162
2.2.1. KEGG RP AIR database......Page 163
2.2.2. KEGG Atomtype......Page 164
2.2.3. RDMpattern......Page 165
2.5. Generalization of RDM patterns......Page 166
3.1. Relationship between Ee sub-subclasses and RDM patterns......Page 168
3.2. Hierarchical clustering and generalization of RDM patterns......Page 169
4. Discussion......Page 170
References......Page 171
1. Introduction......Page 172
2. A Constraint-based Model of Regulation......Page 173
3.1 Toy linear metabolic pathway......Page 176
3.2. Single flux perturbations......Page 177
3.3. Single perturbation robustness......Page 178
3.4. Single flux perturbation trajectories......Page 179
4. Glycolysis......Page 180
5. Discussion......Page 181
References......Page 182
1. Introduction......Page 184
2.2. Objective inference......Page 186
3. Results......Page 188
3.1. Conserved biomass coefficients across different glucose supply rates......Page 189
3.2. Influence of single gene deletion in pentose phosphate pathway......Page 190
4. Discussion......Page 192
References......Page 195
1. Introduction......Page 196
2. Methods......Page 198
2.1.3. RNADB05 and HIRES sets......Page 199
2.2. Calculation of RNA backbone string representation......Page 200
2.3. Suffix tree and array implementation......Page 201
3. Results......Page 202
3.1. Analysis of SCOR motifs......Page 203
3.2. Similarities among tRNA......Page 204
3.3. Similarities in the representative RNA sets......Page 207
4. Discussion......Page 208
Acknowledgements......Page 209
References......Page 210
1. Introduction......Page 212
2.1. DNA sequence andfunctional annotation data sources......Page 213
2.2. Local DNA structure prediction and GC content analysis......Page 214
3.1. Correlation between GC content and local DNA structure......Page 215
3.2. High hydroxyl radical cleavage regions overlap with functional elements......Page 217
4. Discussion......Page 220
References......Page 222
1. Introduction......Page 225
2.1. Coexpressing gene sets......Page 226
2.3. Transcription factor finding site (TFBS) prediction......Page 227
2.4. Bootstrap method......Page 228
2.6. Association rule data mining......Page 230
References......Page 232
1. Introduction......Page 235
2. Methods......Page 236
3. Results......Page 237
References......Page 242
1. Introduction......Page 244
2.1. Data......Page 245
2.2. Methods......Page 246
3.1. Chemical properties......Page 247
3.2. Functional properties......Page 251
3.4. Case study......Page 252
4. Conclusion and Future Perspectives......Page 253
References......Page 254
1. Introduction......Page 256
2.1. Compound database......Page 257
2.2. Two-dimensional searching......Page 258
2.4. Homology modeling......Page 259
3.1. Sequence alignment and homology modeling......Page 260
3.2. In silico screening......Page 261
3.4. Experimental validation......Page 262
References......Page 263
1. Introduction......Page 265
2.2. Drug interaction network......Page 266
3.1. Interaction/actors......Page 267
4. Discussion......Page 269
Acknowledgments......Page 270
References......Page 272
1. Introduction......Page 273
2.1. Preparing surface and grid representation......Page 275
3.1. Docking performance......Page 276
3.2. Sampling of a serine-protease-inhibitor complex......Page 278
Acknowledgments......Page 280
References......Page 281
1. Introduction......Page 283
2. Methods......Page 284
3. Results......Page 286
References......Page 288
1. Introduction......Page 290
2. Methods......Page 292
3. Results......Page 293
4. Discussion......Page 294
References......Page 295
Author Index......Page 298
