New Challenges for Cancer Systems Biomedicine

Cover......Page 1
Title Page......Page 4
Copyright Page......Page 5
Preface......Page 6
Table of Contents......Page 9
1 Introduction......Page 12
2 Role of Social Interactions in Complex Biological Systems......Page 13
4 Evolutionary Dynamics of Graphs......Page 15
5 Computational Models of Cancer Formation......Page 16
6 A Dynamical Tissue Model of Cell to Cell Interactions......Page 17
7 Possible Extensions of the Model......Page 21
References......Page 22
Growth as the Root of all Evil in Carcinomas: Synergy between pH Buffering and Anti-Angiogenesis Prevents Emergence of Hallmarks......Page 28
1 Introduction......Page 29
2 Materials and Methods......Page 32
3 Results......Page 34
4 Discussion......Page 37
References......Page 42
1 Cancer as a Complex System......Page 44
2 Phase Transitions in Physics and Biology......Page 45
3 Phase Changes in Unstable Tumors......Page 49
4 Tumor Decay under Immune Attack......Page 53
References......Page 58
1 Introduction......Page 61
2 Hes1: The Canonical Transcription Factor......Page 62
3 Discussion......Page 82
Appendix......Page 84
References......Page 85
1 Motivation......Page 89
2 Molecular Biology and Systems Theory......Page 90
3 Parameter Estimation with Observers......Page 95
4 Application to TrkA Induced MAPK Signalling......Page 101
References......Page 108
1 Introduction......Page 111
2 The Calculus of Looping Sequences (CLS)......Page 113
3 Modelling the EGFR Signalling Pathway......Page 118
4 Conclusion......Page 128
References......Page 129
Dynamic Simulations of Pathways Downstream of TGF ,Wnt and EGF-Family Growth Factors, in Colorectal Cancer, including Mutations and Treatments with Onco-Protein Inhibitors......Page 132
1 Introduction......Page 133
2 Our Mathematical Model......Page 137
3 What Happens in the Presence of a Work in Progress?......Page 138
4 Results from our Mathematical Model......Page 139
5 Robustness/Sensitivity of our Network to Perturbations......Page 143
6 Final Comments and Conclusion......Page 144
References......Page 145
Some Results on the Population Behavior of Cancer Stem Cells......Page 148
1 Introduction......Page 149
2 The Concept Model......Page 151
3 Underlying Field......Page 157
4 Simulations......Page 163
5 The case q2 D 0......Page 164
6 Conclusion......Page 172
References......Page 174
1 Introduction......Page 176
2 ATP-Regulated Cell Apoptosis......Page 178
3 Necrosis by Acidity and Tumor Invasiveness......Page 182
4 A Model of Acid-Mediated Tumor Invasion Based on TravellingWaves......Page 183
5 Conclusion......Page 190
References......Page 191
1 Introduction......Page 194
2 A Cellular Automaton Model of Cancer Stem Cell-driven Tumor Growth......Page 196
3 A Cellular Potts Model of Cancer Stem Cell-Driven Tumor Growth......Page 201
4 Self-Metastatic Tumor Progression as a Function of Model Parameters......Page 203
5 Discussion......Page 204
References......Page 205
1 Introduction......Page 208
2 Mathematical Model......Page 210
3 Simulation Details and Results......Page 216
4 Model Developments......Page 222
References......Page 224
Computational Models as Novel Tools for Cancer Vaccines......Page 228
2 Immune System, Tumor Immunology and Cancer Vaccines: A Brief Priming......Page 229
3 The Computational Method......Page 234
4 Discussion and Conclusion......Page 244
5 The 21st Century Alliance......Page 245
References......Page 247
1 Introduction......Page 250
2 A Metamodel for Tumor-Immune System Interactions......Page 252
3 Optimal Control for Mathematical Models of Tumor-Immune System Interactions......Page 257
4 Optimal Controls for a Modified Stepanova Model......Page 259
References......Page 265
1 Introduction......Page 268
2 The Mathematical Model......Page 269
3 Model Calibration and Validation......Page 273
4 Optimization of Vaccine Protocols......Page 277
5 Conclusion and Challenges for the Future......Page 279
6 Supplementary Material......Page 281
References......Page 282
Optimizing Cancer Chemotherapy: from Mathematical Theories to Clinical Treatment......Page 284
1 Introduction......Page 285
2 Universal Resonance Phenomenon Suggests a New Method for Cancer Chemotherapy......Page 286
3 Optimizing Chemotherapy Regimens......Page 292
4 From Theory to the Clinic......Page 294
in Vivo......Page 295
5 Conclusion......Page 296
References......Page 297
A Systems Biomedicine Approach for Chronotherapeutics Optimization: Focus on the Anticancer Drug Irinotecan......Page 299
1 Chronotherapeutics of Cancer......Page 300
2 Focus on the Anticancer Drug Irinotecan......Page 304
3 Optimization of Irinotecan Exposure in Cell Culture......Page 306
4 Optimization of Irinotecan Administration in Mice......Page 308
5 Discussion and Perspectives......Page 314
Appendix......Page 318
References......Page 323
1 Introduction......Page 326
2 New Insights on HCV Infected Cell Dynamics......Page 327
3 Mathematical Description of the Model......Page 328
4 Clinical Application of the Model......Page 330
5 Potential for Modeling Hepatocellular Carcinoma Cell Dynamics......Page 331
6 Conclusion and Perspectives......Page 332
References......Page 333
1 Introduction......Page 334
2 Development of a Transport Model for Tirapazamine......Page 337
3 Extension of the Model to Tumors......Page 338
4 Tirapazamine Analogue Screening......Page 342
5 Discussion......Page 346
References......Page 347
Multiscale Simulation of Tumor Response to Treatment in the Context of In Silico Oncology. The Notion of......Page 351
1 Introduction......Page 352
2 The......Page 354
Method......Page 356
Top-Down......Page 357
Methods and Results......Page 360
5 Discussion......Page 363
References......Page 368
Challenges in the Integration of Flow Cytometry and Time-Lapse Live Cell Imaging Data Using a Cell Proliferation Model......Page 372
1 Introduction......Page 373
2 Experiments and Data......Page 374
3 Modelling......Page 376
4 Discussion......Page 391
References......Page 392