New Challenges for Cancer Systems Biomedicine (SEMA SIMAI Springer Series Book 1)

"The aim of this book is not only to illustrate the state of the art of tumor systems biomedicine, but also and mainly to explicitly capture the fact that a increasing number of biomedical scientists is now directly working on mathematical modeling, and a larger number are collaborating with bio-mathematical scientists. Moreover, a number of biomathematicians started working in biomedical institutions. The book is characterized by a coherent view of tumor modeling, based on the concept that mathematical modeling is (with medicine and molecular biology) one of the three pillars of molecular medicine. Indeed this volume is characterized by a well-structured presence of a large number of biomedical scientists directly working in Mathematical or Systems Biomedicine, and of a number biomathematicians working in hospitals."--Publisher. Read more... Part I Towards a Comprehensive Theory of Cancer Growth.- Combining Game Theory and Graph Theory to Model Interactions between Cells in the Tumor Microenvironment.- Growth as the Root of all Evil in Carcinomas: Synergy between pH Buffering and Anti-Angiogenesis prevents Emergence of Hallmarks of Cancer.- Phase Transitions in Cancer.- Part II Cancer Related Signalling Pathways.- Spatio-Temporal Modelling of Intracellular Signalling Pathways: Transcription Factors, Negative Feedback Systems and Oscillations.- Understanding Cell Fate Decisions by Identifying Crucial System Dynamics.- Modelling Biochemical Pathways with the Calculus of Looping Sequences.- Dynamic Simulations of Pathways Downstream of TGFbeta, Wnt and EGF-Family Growth Factors, in Colorectal Cancer, including Mutations and Treatments with Onco-Protein Inhibitors.- Part III Basic Mechanisms of Tumor Progression.- Some Results on the Population Behavior of Cancer Stem Cells.- Glucose Metabolism in Multicellular Spheroids, ATP Production and Effects of Acidity.- Cell-Cell Interactions in Solid Tumors - the Role of Cancer Stem Cells.- Hybrid Cellular Potts Model for Solid Tumor Growth.- Part IV Tumor-Immune System Interplay and Immunotherapy.- Computational Models as Novel Tools for Cancer Vaccines.- On the Dynamics of Tumor-Immune System Interactions and Combined Chemo- and Immunotherapy.- Modeling the Kinetics of the Immune Response.- Part V Computational Method for Improving Chemotherapy.- Optimizing Cancer Chemotherapy: from Mathematical Theories to Clinical Treatment.- A Systems Biomedicine Approach for Chronotherapeutics Optimization: Focus on the Anticancer Drug Irinotecan.- Modeling the Dynamics of HCV Infected Cells to Tailor Antiviral Therapy in Clinical Practice: Can This Approach Fit for Neoplastic Cells?.- Introducing Drug Transport Early in the Design of Hypoxia Selective Anticancer Agents Using a Mathematical Modelling Approach.- Top-Down Multiscale Simulation of Tumor Response to Treatment in the Context of In Silico Oncology. The Notion of Oncosimulator.- Challenges in the Integration of Flow Cytometry and Time-Lapse Live Cell Imaging Data Using a Cell Proliferation Model 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 Part I Towards a Comprehensive Theory of Cancer Growth Combining Game Theory and Graph Theory to Model Interactions between Cells in the Tumor Microenvironment Growth as the Root of all Evil in Carcinomas: Synergy between pH Buffering and Anti-Angiogenesis prevents Emergence of Hallmarks of Cancer Phase Transitions in Cancer Part II Cancer Related Signalling Pathways Spatio-Temporal Modelling of Intracellular Signalling Pathways: Transcription Factors, Negative Feedback Systems and Oscillations Understanding Cell Fate Decisions by Identifying Crucial System Dynamics Modelling Biochemical Pathways with the Calculus of Looping Sequences Dynamic Simulations of Pathways Downstream of TGFβ, Wnt and EGF-Family Growth Factors, in Colorectal Cancer, including Mutations and Treatments with Onco-Protein Inhibitors Part III Basic Mechanisms of Tumor Progression Some Results on the Population Behavior of Cancer Stem Cells ^ Glucose Metabolism in Multicellular Spheroids, ATP Production and Effects of Acidity Cell-Cell Interactions in Solid Tumors - the Role of Cancer Stem Cells Hybrid Cellular Potts Model for Solid Tumor Growth Part IV Tumor-Immune System Interplay and Immunotherapy Computational Models as Novel Tools for Cancer Vaccines On the Dynamics of Tumor-Immune System Interactions and Combined Chemo- and Immunotherapy Modeling the Kinetics of the Immune Response Part V Computational Method for Improving Chemotherapy.- Optimizing Cancer Chemotherapy: from Mathematical Theories to Clinical Treatment A Systems Biomedicine Approach for Chronotherapeutics Optimization: Focus on the Anticancer Drug Irinotecan Modeling the Dynamics of HCV Infected Cells to Tailor Antiviral Therapy in Clinical Practice: Can This Approach Fit for Neoplastic Cells? Introducing Drug Transport Early in the Design of Hypoxia Selective Anticancer Agents Using a Mathematical Modelling Approach ^ ^^ Top-Down Multiscale Simulation of Tumor Response to Treatment in the Context of In Silico Oncology. The Notion of Oncosimulator Challenges in the Integration of Flow Cytometry and Time-Lapse Live Cell Imaging Data Using a Cell Proliferation Model. ^^ The aim of this book is not only to illustrate the state of the art of tumor systems biomedicine, but also and mainly to explicitly capture the fact that a increasing number of biomedical scientists is now directly working on mathematical modeling, and a larger number are collaborating with bio-mathematical scientists. Moreover, a number of biomathematicians started working in biomedical institutions. The book is characterized by a coherent view of tumor modeling, based on the concept that mathematical modeling is (with medicine and molecular biology) one of the three pillars of molecular medicine. Indeed this volume is characterized by a well-structured presence of a large number of biomedical scientists directly working in Mathematical or Systems Biomedicine, and of a number biomathematicians working in hospitals. This give to this book an unprecedented tone, providing an original interdisciplinary insight into the biomedical applications. Finally, all biomedical contributors were asked to briefly summarize in one section of their contributes their point of view on her/his own interactions with quantitative scientists working in Systems Biomedicine. The future of oncology seems to lie in Molecular Medicine (MM). MM is a new science based on three pillars. Two of them are evident in its very name and are well known: medical science and molecular biology. However, there is a general unawareness that MM is firmly based on a third, and equally important, pillar: Systems Biomedicine. Currently, this term denotes multilevel, hierarchical models integrating key factors at the molecular, cellular, tissue, through phenotype levels, analyzed to reveal the global behavior of the biological process under consideration. It becomes increasingly evident that the tools to construct such complex models include, not only bioinformatics and modern applied statistics, as is unanimously agreed, but also other interdisciplinary fields of science, notably, Mathematical Oncology, Systems Biology and Theoretical Biophysics.