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.
State of the art in tumor modelling
It stresses the role of biomedical scientists working in modelling
Original interdisciplinary insight into the biomedical applications
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.
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.