"Prevention is so much better than healing because it saves the labour of being sick." Thomas Adams, 1618"Prevention is so much better than healing because it saves the labour of being sick."
Thomas Adams, 1618

Publication of Mathematical Oncology 2013 book

Publication of Mathematical Oncology 2013 book

October 28, 2014

iPRI is pleased to announce the publication of the book “Mathematical Oncology 2013”, co-edited by Alberto d’Onofrio, Research Director at iPRI, and Alberto Gandolfi, Research Director for the National Research Council of Italy.

Cover page of Mathematical Oncology 2013 co-edited by Alberto d'Onofrio (iPRI) and Alberto Gandolfi (National Research Council of Italy)

Cover page of Mathematical Oncology 2013 co-edited by Alberto d’Onofrio (iPRI) and Alberto Gandolfi (National Research Council of Italy)

With chapters on free boundaries, constitutive equations, stochastic dynamics, nonlinear diffusion–consumption, structured populations, and applications of optimal control theory, this volume presents the most significant recent results in the field of mathematical oncology. It highlights the work of world-class research teams, and explores how different researchers approach the same problem in various ways.

Tumors are complex entities that present numerous challenges to the mathematical modeler. First and foremost, they grow. Thus their spatial mean field description involves a free boundary problem. Second, their interiors should be modeled as nontrivial porous media using constitutive equations. Third, at the end of anti-cancer therapy, a small number of malignant cells remain, making the post-treatment dynamics inherently stochastic. Fourth, the growth parameters of macroscopic tumors are non-constant, as are the parameters of anti-tumor therapies. Changes in these parameters may induce phenomena that are mathematically equivalent to phase transitions. Fifth, tumor vascular growth is random and self-similar. Finally, the drugs used in chemotherapy diffuse and are taken up by the cells in nonlinear ways.

Mathematical Oncology 2013 will appeal to graduate students and researchers in biomathematics, computational and theoretical biology, biophysics, and bioengineering.

To cite the book, please use:

Mathematical Oncology 2013. d’Onofrio A, Gandolfi A (Eds.). Springer, 2014, ISBN 978-1-4939-0457-0.

 

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