Introduction to Mathematical Oncology

Nonfiction, Health & Well Being, Medical, Ailments & Diseases, Infectious Diseases, Epidemiology, Science & Nature, Mathematics, Applied, Science
Cover of the book Introduction to Mathematical Oncology by Yang Kuang, John D. Nagy, Steffen E. Eikenberry, CRC Press
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Yang Kuang, John D. Nagy, Steffen E. Eikenberry ISBN: 9781498785532
Publisher: CRC Press Publication: June 14, 2016
Imprint: Chapman and Hall/CRC Language: English
Author: Yang Kuang, John D. Nagy, Steffen E. Eikenberry
ISBN: 9781498785532
Publisher: CRC Press
Publication: June 14, 2016
Imprint: Chapman and Hall/CRC
Language: English

Introduction to Mathematical Oncology presents biologically well-motivated and mathematically tractable models that facilitate both a deep understanding of cancer biology and better cancer treatment designs. It covers the medical and biological background of the diseases, modeling issues, and existing methods and their limitations. The authors introduce mathematical and programming tools, along with analytical and numerical studies of the models. They also develop new mathematical tools and look to future improvements on dynamical models.

After introducing the general theory of medicine and exploring how mathematics can be essential in its understanding, the text describes well-known, practical, and insightful mathematical models of avascular tumor growth and mathematically tractable treatment models based on ordinary differential equations. It continues the topic of avascular tumor growth in the context of partial differential equation models by incorporating the spatial structure and physiological structure, such as cell size. The book then focuses on the recent active multi-scale modeling efforts on prostate cancer growth and treatment dynamics. It also examines more mechanistically formulated models, including cell quota-based population growth models, with applications to real tumors and validation using clinical data. The remainder of the text presents abundant additional historical, biological, and medical background materials for advanced and specific treatment modeling efforts.

Extensively classroom-tested in undergraduate and graduate courses, this self-contained book allows instructors to emphasize specific topics relevant to clinical cancer biology and treatment. It can be used in a variety of ways, including a single-semester undergraduate course, a more ambitious graduate course, or a full-year sequence on mathematical oncology.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

Introduction to Mathematical Oncology presents biologically well-motivated and mathematically tractable models that facilitate both a deep understanding of cancer biology and better cancer treatment designs. It covers the medical and biological background of the diseases, modeling issues, and existing methods and their limitations. The authors introduce mathematical and programming tools, along with analytical and numerical studies of the models. They also develop new mathematical tools and look to future improvements on dynamical models.

After introducing the general theory of medicine and exploring how mathematics can be essential in its understanding, the text describes well-known, practical, and insightful mathematical models of avascular tumor growth and mathematically tractable treatment models based on ordinary differential equations. It continues the topic of avascular tumor growth in the context of partial differential equation models by incorporating the spatial structure and physiological structure, such as cell size. The book then focuses on the recent active multi-scale modeling efforts on prostate cancer growth and treatment dynamics. It also examines more mechanistically formulated models, including cell quota-based population growth models, with applications to real tumors and validation using clinical data. The remainder of the text presents abundant additional historical, biological, and medical background materials for advanced and specific treatment modeling efforts.

Extensively classroom-tested in undergraduate and graduate courses, this self-contained book allows instructors to emphasize specific topics relevant to clinical cancer biology and treatment. It can be used in a variety of ways, including a single-semester undergraduate course, a more ambitious graduate course, or a full-year sequence on mathematical oncology.

More books from CRC Press

Cover of the book Photovoltaic Laboratory by Yang Kuang, John D. Nagy, Steffen E. Eikenberry
Cover of the book Deconstructing the Elements with 3ds Max by Yang Kuang, John D. Nagy, Steffen E. Eikenberry
Cover of the book Modeling in Fluid Mechanics by Yang Kuang, John D. Nagy, Steffen E. Eikenberry
Cover of the book Neurological Rehabilitation by Yang Kuang, John D. Nagy, Steffen E. Eikenberry
Cover of the book Computer System and Network Security by Yang Kuang, John D. Nagy, Steffen E. Eikenberry
Cover of the book Optical Nano and Micro Actuator Technology by Yang Kuang, John D. Nagy, Steffen E. Eikenberry
Cover of the book Construction Collaboration Technologies by Yang Kuang, John D. Nagy, Steffen E. Eikenberry
Cover of the book Critical Incident Stress Management in Aviation by Yang Kuang, John D. Nagy, Steffen E. Eikenberry
Cover of the book Dikes and Revetments by Yang Kuang, John D. Nagy, Steffen E. Eikenberry
Cover of the book Analysis of Energy Systems by Yang Kuang, John D. Nagy, Steffen E. Eikenberry
Cover of the book Neurovision Rehabilitation Guide by Yang Kuang, John D. Nagy, Steffen E. Eikenberry
Cover of the book Liquid Crystal Sensors by Yang Kuang, John D. Nagy, Steffen E. Eikenberry
Cover of the book SPICE for Power Electronics and Electric Power by Yang Kuang, John D. Nagy, Steffen E. Eikenberry
Cover of the book Get Through MRCPsych CASC by Yang Kuang, John D. Nagy, Steffen E. Eikenberry
Cover of the book Statistical Inference Based on the likelihood by Yang Kuang, John D. Nagy, Steffen E. Eikenberry
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy