This book reviews how mathematical and computational approaches can be useful to help us understand how killer T-cell responses work to fight viral infections. It also demonstrates, in a writing style that exemplifies the point, that such mathematical and computational approaches are most valuable when coupled with experimental work through interdisciplinary collaborations. Designed to be useful to immunoligists and viroligists without extensive computational background, the book covers a broad variety of topics, including both basic immunological questions and the application of these insights to the understanding and treatment of pathogenic human diseases.

Countless medical researchers over the past century have been occupied by the search for a cure of cancer. So far, they have developed and implemented awide rangeof treatment techniques, including surgery, chemo- and radiotherapy, antiangiogenic drugs, small molecule inhibitors, and oncolytic viruses.However, patterns of these treatments' effectiveness remain largely unclear, and a better understanding of how cancer therapies work has become akey research goal."Cancer Treatment in Silico"provides the first in-depth study of approaching this understanding by modeling cancer treatments, both mathematically and through computer simulations.

The main goal of this book is to help expose students and researchers to in silico methods ofstudying cancer.It isintended forboth the applied mathematics and experimental oncology communities, asmathematicalmodelsare playing an increasingly important roleto supplement laboratory biology in the fight against cancer. Written at a level that generally requires little technical background, the workwill be a valuable resource for scientists and students alike."

The book aims to provide an introduction to mathematical models that describe the dynamics of tumor growth and the evolution of tumor cells. It can be used as a textbook for advanced undergraduate or graduate courses, and also serves as a reference book for researchers. The book has a strong evolutionary component and reflects the viewpoint that cancer can be understood rationally through a combination of mathematical and biological tools. It can be used both by mathematicians and biologists. Mathematically, the book starts with relatively simple ordinary differential equation models, and subsequently explores more complex stochastic and spatial models. Biologically, the book starts with explorations of the basic dynamics of tumor growth, including competitive interactions among cells, and subsequently moves on to the evolutionary dynamics of cancer cells, including scenarios of cancer initiation, progression, and treatment. The book finishes with a discussion of advanced topics, which describe how some of the mathematical concepts can be used to gain insights into a variety of questions, such as epigenetics, telomeres, gene therapy, and social interactions of cancer cells.

The book shows how mathematical and computational models can be used to study cancer biology. It introduces the concept of mathematical modeling and then applies it to a variety of topics in cancer biology. These include aspects of cancer initiation and progression, such as the somatic evolution of cells, genetic instability, and angiogenesis. The book also discusses the use of mathematical models for the analysis of therapeutic approaches such as chemotherapy, immunotherapy, and the use of oncolytic viruses.

Countless medical researchers over the past century have been occupied by the search for a cure of cancer. So far, they have developed and implemented a wide range of treatment techniques, including surgery, chemo- and radiotherapy, antiangiogenic drugs, small molecule inhibitors, and oncolytic viruses. However, patterns of these treatments' effectiveness remain largely unclear, and a better understanding of how cancer therapies work has become a key research goal. Cancer Treatment in Silico provides the first in-depth study of approaching this understanding by modeling cancer treatments, both mathematically and through computer simulations. The main goal of this book is to help expose students and researchers to in silico methods of studying cancer. It is intended for both the applied mathematics and experimental oncology communities, as mathematical models are playing an increasingly important role to supplement laboratory biology in the fight against cancer. Written at a level that generally requires little technical background, the work will be a valuable resource for scientists and students alike.

This book reviews how mathematical and computational approaches can be useful to help us understand how killer T-cell responses work to fight viral infections. It also demonstrates, in a writing style that exemplifies the point, that such mathematical and computational approaches are most valuable when coupled with experimental work through interdisciplinary collaborations. Designed to be useful to immunoligists and viroligists without extensive computational background, the book covers a broad variety of topics, including both basic immunological questions and the application of these insights to the understanding and treatment of pathogenic human diseases.