This book presents a comprehensive review of the interaction between cancer research and mathematical methods for both investigators in the field and newcomers just entering it. The book's primary focus is on the use of computer-assisted mathematical modeling in carcinogenesis and cancer prevention. The first two chapters include a general presentation of the carcinogenesis and anticarcinogenesis molecular mechanisms, followed by a discussion of mathematical models of triggers for gene regulation. A description of the prediction of both carcinogenicity and mutagenicity using quantum mechanical, topological or physico-chemical indices is presented, as well as a discussion of the QSAR analysis of carcinogenesis-inhibiting compounds (known as blocking or suppressive agents).

This collection of selected chapters offers a comprehensive overview of state-of-the-art mathematical methods and tools for modeling and analyzing cancer phenomena. Topics covered include stochastic evolutionary models of cancer initiation and progression, tumor cords and their response to anticancer agents, and immune competition in tumor progression and prevention. The complexity of modeling living matter requires the development of new mathematical methods and ideas. This volume, written by first-rate researchers in the field of mathematical biology, is one of the first steps in that direction.

Composed of contributions from an international team of leading researchers, this book pulls together the most recent research results in the field of cancer modeling to provide readers with the most advanced mathematical models of cancer and their applications.Topics included in the book cover oncogenetic trees, stochastic multistage models of carcinogenesis, effects of ionizing radiation on cell cycle and genomic instability, induction of DNA damage by ionizing radiation and its repair, epigenetic cancer models, bystander effects of radiation, multiple pathway models of human colon cancer, and stochastic models of metastasis. The book also provides some important applications of cancer models to the assessment of cancer risk associated with various hazardous environmental agents, to cancer screening by MRI, and to drug resistance in cancer chemotherapy. An updated statistical design and analysis of xenograft experiments as well as a statistical analysis of cancer occult clinical data are also provided.The book will serve as a useful source of reference for researchers in biomathematics, biostatistics and bioinformatics; for clinical investigators and medical doctors employing quantitative methods to develop procedures for cancer diagnosis, prevention, control and treatment; and for graduate students.

Beverly A. Teicher and a panel of leading experts comprehensively describe for the first time in many years the state-of-the-art in animal tumor model research. The wide array of models detailed form the basis for the selection of compounds and treatments that go into clinical testing of patients, and include syngeneic models, human tumor xenograft models, orthotopic models, metastatic models, transgenic models, and gene knockout models. Synthesizing many years experience with all the major in vivo models currently available for the study of malignant disease, Tumor Models in Cancer Research provides preclinical and clinical cancer researchers alike with a comprehensive guide to the selection of these models, their effective use, and the optimal interpretation of their results.

Cancer research deals with all aspects of malignant transformation, tumour growth and the effects of treatment. Mathematical models enable quantitative representations of the changes affecting cell state and cell number. This book provides a review of the scope of mathematical modelling in cancer research, bringing together for the first time a group of related mathematical topics including multistage carcinogenesis, tumour growth kinetics, growth control, radiotherapy, chemotherapy and biological targeting in cancer treatment. Physicists and mathematicians interested in medical research, biomathematicians, biostatisticians, radiation and medical oncologists and experimental and theoretical biologists will welcome this critical review of mathematical modelling in cancer research. This book will also be of interest to clinicians, basic cancer scientists and physicists working in radiotherapy departments, and to postgraduate students on courses in oncology and subjects.