Multiscale Cancer Modeling (Chapman & Hall/crc Mathematical & Computational Biology)

DESCRIPTION

Cancer is a complex disease process that spans multiple scales in space and time. Driven by cutting-edge mathematical and computational techniques, in silico biology provides powerful tools to investigate the mechanistic relationships of genes, cells, and tissues. It enables the creation of experimentally testable hypotheses, the integration of data across scales, and the prediction of tumor progression and treatment outcome (in silico oncology).

Drawing on an interdisciplinary group of distinguished international experts, Multiscale Cancer Modeling discusses the scientific and technical expertise necessary to conduct innovative cancer modeling research across scales. It presents contributions from some of the top in silico modeling groups in the United States and Europe.

The ultimate goal of multiscale modeling and simulation approaches is their use in clinical practice, such as supporting patient-specific treatment optimization. This volume covers state-of-the-art methods of multiscale cancer modeling and addresses the field’s potential as well as future challenges. It encourages collaborations among researchers in various disciplines to achieve breakthroughs in cancer modeling.

Features

• Presents modeling methods and results at the forefront of cancer simulation at all levels of biocomplexity
• Explores computational cancer research ranging from experimentally testable hypothesis generation and cross-scale data integration to patient-specific prediction of progression and treatment planning
• Fosters transnational research interactions by bringing together many leading in silico modeling groups from around the world
• Explains how combinations of multilevel cancer models can enhance our understanding of cancer and can help optimize its treatment

TABLE OF CONTENTS

• Evolution, Regulation and Disruption of Homeostasis and Its Role in Carcinogenesis, A.R.A. Anderson, D. Basanta, P. Gerlee, and K.A. Rejniak
• Cancer Cell: Linking Oncogenic Signaling to Molecular Structure, J.E. Purvis, A.J. Shih, Y. Liu, and R. Radhakrishnan
• Has Cancer Sculpted the Genome? Modeling Linkage and the Role of Tetraploidy in Neoplastic Progression, C.C. Maley, W. Lewis, and B.J. Reid
• Catastrophes and Complex Networks in Genomically Unstable Tumorigenesis, R. Sole
• A Stochastic Multiscale Model Framework for Intestinal Stem Cell Homeostasis, L.W. Jean and E.G. Luebeck
• Multiscale Modeling of Colonic Crypts and Early Colorectal Cancer, A.G. Fletcher, G.R. Mirams, P.J. Murray, A. Walter, J.-W. Kang, K.-H. Cho, P.K. Maini, and H.M. Byrne
• The Physical Microenvironment in Somatic Evolution of Cancer, R.A. Gatenby
• Multiscale Modeling of Cell Motion in Three-Dimensional Environments, D. Harjanto and M.H. Zaman
• Simulating Cancer Growth with Agent-Based Models, Z. Wang, V. Bordas, J. Sagotsky, and T.S. Deisboeck
• Diffusional Instability as a Mechanism of Tumor Invasion, H.B. Frieboes, J. Lowengrub, and V. Cristini
• Continuum Models of Mesenchymal Cell Migration and Sprouting Angiogenesis, M. Bergdorf, F. Milde, and P. Koumoutsakos
• Do Tumor Invasion Strategies Follow Basic Physical Laws?, C. Guiot, P.P. Delsanto, and A.S. Gliozzi
• Multiscale Mathematical Modeling of Vascular Tumor Growth: An Exercise in Transatlantic Cooperation, M.A.J. Chaplain, P. Macklin, S. McDougall, A.R.A. Anderson, V. Cristini, and J. Lowengrub
• A Multiscale Simulation Framework for Modeling Solid Tumor Growth with an Explicit Vessel Network, S. Hirsch, B. Lloyd, D. Szczerba, and G. Székely
• Building Stochastic Models for Cancer Growth and Treatment, N.L. Komarova
• Bridging from Multiscale Modeling to Practical Clinical Applications in the Study of Human Gliomas, G. Chakraborty, R. Sodt, S. Massey, S. Gu, R. Rockne, E.C. Alvord, Jr., and K.R. Swanson
• Personalization of Reaction-Diffusion Tumor Growth Models in MR Images: Application to Brain Gliomas Characterization and Radiotherapy Planning, E. Konukoglu, O. Clatz, H. Delingette, and N. Ayache
• In Silico Oncology Part I: Clinically Oriented Cancer Multilevel Modeling Based on Discrete Event Simulation, G.S. Stamatakos
• In Silico Oncology Part II: Clinical Requirements, N. Graf

AUTHOR BIOGRAPHY

Thomas S. Deisboeck, M.D., is an associate professor of radiology at Massachusetts General Hospital, where he directs the Complex Biosystems Modeling Laboratory. He is also an affiliated faculty member of the Harvard-MIT Health Sciences and Technology Division and a member of the Dana Farber/Harvard Cancer Center.

Georgios S. Stamatakos, Ph.D., is a research professor of biological systems analysis and simulation in the Institute of Communication and Computer Systems as well as founder and leader of the In Silico Oncology Group, Laboratory of Microwaves and Fiber Optics at the National Technical University of Athens.