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Multiscale Modeling of Cancer: An Integrated Experimental and Mathematical Modeling Approach download ebook

by John Lowengrub,Vittorio Cristini

Multiscale Modeling of Cancer: An Integrated Experimental and Mathematical Modeling Approach download ebook
ISBN:
052188442X
ISBN13:
978-0521884426
Author:
John Lowengrub,Vittorio Cristini
Publisher:
Cambridge University Press; 1 edition (November 1, 2010)
Language:
Pages:
298 pages
ePUB:
1209 kb
Fb2:
1447 kb
Other formats:
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Category:
Medicine & Health Sciences
Subcategory:
Rating:
4.8

Vittorio Cristini is Professor of Health Information Sciences and Biomedical Engineering at the University of Texas, and of. .

Vittorio Cristini is Professor of Health Information Sciences and Biomedical Engineering at the University of Texas, and of Systems Biology at the M. D. Anderson Cancer Center, Houston. He is also Honorary Professor of Mathematics at the University of Dundee, Scotland. He has published several chapters in books and over 60 journal articles.

Vittorio Cristini, John Lowengrub. The correct functioning of the mammalian brain depends on the integrated activity of myriad neuronal and non-neuronal cells. Discrete areas serve discrete functions, and dispersed or distributed communities of cells serve others. Throughout, these networks of activity are under the control of neuromodulatory systems.

by Vittorio Cristini (Author), John Lowengrub (Author). Vittorio Cristini is Professor of Health Information Sciences and Biomedical Engineering at the University of Texas, and of Systems Biology at the M. Anderson Cancer Center, Houston

by Vittorio Cristini (Author), John Lowengrub (Author).

Multiscale Modeling of Cancer book. Goodreads helps you keep track of books you want to read. Start by marking Multiscale Modeling of Cancer: An Integrated Experimental and Mathematical Modeling Approach as Want to Read: Want to Read savin. ant to Read.

Vittorio Cristini, PhD, ISI Highly-Cited .SM Wise, JS Lowengrub, HB Frieboes, V Cristini. Multiscale modeling of cancer: an integrated experimental and mathematical modeling approach. V Cristini, J Lowengrub. Journal of theoretical biology 253 (3), 524-543, 2008. Cambridge University Press, 2010. A diffuse interface model for microstructural evolution in elastically stressed solids. PH Leo, JS Lowengrub, HJ Jou. Acta materialia 46 (6), 2113-2130, 1998.

Items related to Multiscale Modeling of Cancer: An Integrated Experimental. Home Vittorio Cristini, John Lowengrub Multiscale Modeling of Cancer: An Integrated Experimental and. 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 scales.

Multiscale Modeling of Cancer. An Integrated Experimental and Mathematical Modeling Approach. Chauviere, Arnaud Hatzikirou, Haralambos Kevrekidis, Ioannis G. Lowengrub, John S. and Cristini, Vittorio 2012. Dynamic density functional theory of solid tumor growth: Preliminary models. AIP Advances, Vol. 2, Issue.

Multiscale modelling of cancer. Chapter · July 2008 with 1 Reads. DOI: 1. 093/acprof:oso/9780198570912. In book: Models of Cellular Regulation, p. 55-175. Cite this publication. To elucidate the mechanism of such integrated genome system (genomic mechanism) which guides cell fate change through the complex spatio-temporal self-organization of the genome is a fundamental challenge in current bioscience.

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 scales. The first part of the text presents a detailed biological background with an examination of single-phase and multi-phase continuum tumor modeling, discrete cell modeling, and hybrid continuum-discrete modeling. In the final two chapters, the authors guide the reader through problem-based illustrations and case studies of brain and breast cancer, to demonstrate the future potential of modeling in cancer research. This book has wide interdisciplinary appeal and is a valuable resource for mathematical biologists, biomedical engineers and clinical cancer research communities wishing to understand this emerging field.