D'Onofrio A., Cerrai P., Gandolfi A. (Eds.) New Challenges for Cancer Systems Biomedicine

Springer-Verlag Italia, 2012. — 399 р. — ISBN: 978-88-470-2570-7The future of oncology has a name: Molecular Medicine (MM). Molecular Medicine is a new science based on three pillars. Two of them are well known and evident in its very name: medical science and molecular biology. However, there is a general unawareness that MM is firmly based on a third but equally important pillar: Sys- tems Biomedicine. Currently this term mainly evokes Bioinformatics and modern Applied Statistics, but increasingly it shall have to include (as in part it already does) the interacting complex of scientific fields such as Mathematical Biology, Systems Biology, Theoretical Biophysics.
The data from MM of tumors are complex and heterogeneous (e.g. clinical data paired with -omics data) but – and this is their most important feature are unified by their dynamical nature. Indeed, cancers are a family of dynamic diseases, endowed by multiple temporal and spatial scales, and their polymorphic macroscopic instances are emergent properties originating from a wide number of microscopic interplays at intracellular and intercellular level. The complexity of these multiscale data cannot be deciphered by natural language reasoning, or by classical data analysis based on static data mining and model-unrelated time series analysis. These classical tools no longer suffice to cope with MM data in order to understand them and to produce meaningful and useful predictions.
As a consequence, it is mandatory to build mechanistic mathematical models of biomedical phenomena with complex outputs. These models could allow a deeper understanding of the “internal dynamics” of single patients or classes of patients, hopefully opening the road for tailored therapies. This is a huge challenge at the frontier of contemporary mathematical modeling, since dynamic modeling in MM is what allows to bridge the bench to the bedside, and in perspective it will be increasingly instrumental in aiding the cure of patients. By no means this implies that future medical doctors will be like electronic engineers, skilfully using special software to cure patients. Nevertheless, in a realistic perspective, future generation of oncologists will be more similar to cardiologists that rely on basic knowledge of the physics of heart and circulation, and use devices from bioengineering in their everyday clinical work.
The aim of this book is not only to illustrate the state of the art of tumor systems biomedicine, but also (and especially) to explicitly capture and collect results of the above-mentioned collaborative trends. Indeed, this volume is characterized by a well-structured presence of a large number of life scientists working directly in Systems Biomedicine, and a number of mathematical biology researchers working in biomedical institutions. With this book we wish to provide a coherent view of tumor modeling, based on the concept that mathematical modeling is the third pillar of molecular medicine. We hope that these features give to this work an unprecedented tone, providing an original interdisciplinary insight into the biomedical applications. We also hope the book may foster and encourage new fruitful communications and cooperations.
The present volume covers five basic topics of interest in oncology: compre- hensive theories of cancer growth, systems biology of cancer, basic mechanisms of tumor progression, tumor-immune system interplay and immunotherapy, computa- tional methods for improving chemotherapies. All the scales are so addressed, from the intracellular molecular networks to the therapy of patients. Moreover, relevance is given to recent mathematical methodologies such as nonlinear analysis, control and optimization theory, cellular automata and cellular-Potts modeling, agent-based modeling, and formal methods of computer science.