Mathematical models in cancer research
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Mathematical models in cancer research

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Published by Hilger in Bristol, Eng, Philadelphia, PA .
Written in English

Subjects:

  • Cancer -- Mathematical models,
  • Carcinogenesis -- Mathematical models,
  • Tumors -- Growth -- Mathematical models,
  • Biological models,
  • Tumors

Book details:

Edition Notes

StatementT. E. Wheldon.
SeriesMedical science series
The Physical Object
Paginationxvi, 247 p. ;
Number of Pages247
ID Numbers
Open LibraryOL23749786M

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Additional Physical Format: Online version: Wheldon, T.E. Mathematical models in cancer research. Bristol ; Philadelphia: A. Hilger, © (OCoLC) This book chapter reviews the scope of mathematical and computational models in cancer research. Mathematical models have substantially improved our ability to predict the response of a.   In their original research article, Yamamoto et al 22 used data from a rapid autopsy program for patients with pancreatic cancer to estimate parameters of a stochastic mathematical model of individual cell growth. Using this model, the authors derived expectation times for primary tumor growth, time to metastasis, and even : Russell C. Rockne, Jacob G. Scott. The cancer cell model is realized by the deregulation in the model of healthy cell. In addition, potential therapy targets are predicted by using mathematical simulation. This model has been done entirely within an in silico research work. The theoretical foundations with which we constructed the differential equations for the quantitative model are strongly connected with biochemistry fields for many years, and not only allow one to apply mathematics Cited by: 3.

A book about mathematical models that describe the dynamics of tumor growth and the evolution of tumor cells has been recently published: "Mathematically, the book starts with relatively simple ordinary differential equation models, and subsequently explores more complex stochastic and spatial models.   Facing complex biological data in cancer research A mathematical model relates the dependent variables (such as a population growth or a particular molecular concentration growth) by means of a mathematical equation demonstrating the output cell response for a given : Abdallah K Alameddine, Frederick Conlin, Frederick Conlin, Brian Binnall. Synthesizing many years experience with all the major in vivo models currently available for the study of malignant disease, Tumor Models in Cancer Research 2nd edition, 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. Chapter 2. Biology of Cancer Tumors 3 Chapter 3. PDE Models of Cancer Tumors 5 1. Background PDEs and Physical Laws 5 2. The Di usion Equation/Heat Equation 5 3. Continuity Equation 5 4. Darcy’s Law 7 5. Mixed Models M 3 7 6. Mixed Models M 2 11 7. Models with only Proliferating Cells 13 Chapter 4. Mathematical Results for a Spherically Author: Alicia Marinis.

  A new mathematical model developed by researchers at the Broad Institute of MIT and Harvard and Massachusetts General Hospital (MGH) could .   Platinum-based chemotherapy constitutes the backbone of clinical care in advanced solid cancers such as high-grade serous ovarian cancer (HGSOC) and has prolonged survival of millions of cancer patients. Most of these patients, however, become resistant to chemotherapy, which generally leads to a fatal refractory disease. We present a comprehensive stochastic mathematical model and Cited by: 5. Mathematical modeling, analysis and simulation are set to play crucial roles in explaining tumor behavior, and the uncontrolled growth of cancer cells over multiple time and spatial scales. This book, the first to integrate state-of-the-art numerical techniques with experimental data, provides an in-depth assessment of tumor cell modeling at multiple by: A good survey of mathematical models of cancer growth and development can be found in the excellent book , and an excellent survey of the range of mathematical and computational modelling techniques used for biological problems on different scales can be found in the book .Cited by: