Multiscale cancer modeling

Thomas S. Deisboeck; Zhihui Wang; Paul Macklin; Vittorio Cristini

doi:10.1146/annurev-bioeng-071910-124729

Multiscale cancer modeling

Thomas S. Deisboeck
, Zhihui Wang
, Paul Macklin
, Vittorio Cristini

Research output: Chapter in Book/Report/Conference proceeding › Other chapter contribution

341 Citations (Scopus)

Abstract

Simulating cancer behavior across multiple biological scales in space and time, i.e., multiscale cancer modeling, is increasingly being recognized as a powerful tool to refine hypotheses, focus experiments, and enable more accurate predictions. A growing number of examples illustrate the value of this approach in providing quantitative insights in the initiation, progression, and treatment of cancer. In this review, we introduce the most recent and important multiscale cancer modeling works that have successfully established a mechanistic link between different biological scales. Biophysical, biochemical, and biomechanical factors are considered in these models. We also discuss innovative, cutting-edge modeling methods that are moving predictive multiscale cancer modeling toward clinical application. Furthermore, because the development of multiscale cancer models requires a new level of collaboration among scientists from a variety of fields such as biology, medicine, physics, mathematics, engineering, and computer science, an innovative Web-based infrastructure is needed to support this growing community.

Original language	English
Title of host publication	Annual review of biomedical engineering
Place of Publication	Palo Alto
Publisher	Annual Reviews
Pages	127-155
Number of pages	29
Volume	13
DOIs	https://doi.org/10.1146/annurev-bioeng-071910-124729
Publication status	Published - 2011

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Cancer systems biology
Discrete
Continuum
Hybrid
Clinical translation
Personalized medicine
Epidermal growth factor
Tumor-induced angiogenesis
Cell lung cancer
Semantic web technologies
Agent-based model
Systems biology
Factor receptor
Nonlinear simulation
Multicellular patterns
Biochemical pathways

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10.1146/annurev-bioeng-071910-124729

Cite this

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title = "Multiscale cancer modeling",

abstract = "Simulating cancer behavior across multiple biological scales in space and time, i.e., multiscale cancer modeling, is increasingly being recognized as a powerful tool to refine hypotheses, focus experiments, and enable more accurate predictions. A growing number of examples illustrate the value of this approach in providing quantitative insights in the initiation, progression, and treatment of cancer. In this review, we introduce the most recent and important multiscale cancer modeling works that have successfully established a mechanistic link between different biological scales. Biophysical, biochemical, and biomechanical factors are considered in these models. We also discuss innovative, cutting-edge modeling methods that are moving predictive multiscale cancer modeling toward clinical application. Furthermore, because the development of multiscale cancer models requires a new level of collaboration among scientists from a variety of fields such as biology, medicine, physics, mathematics, engineering, and computer science, an innovative Web-based infrastructure is needed to support this growing community.",

keywords = "Cancer systems biology, Discrete, Continuum, Hybrid, Clinical translation, Personalized medicine, Epidermal growth factor, Tumor-induced angiogenesis, Cell lung cancer, Semantic web technologies, Agent-based model, Systems biology, Factor receptor, Nonlinear simulation, Multicellular patterns, Biochemical pathways",

author = "Deisboeck, \{Thomas S.\} and Zhihui Wang and Paul Macklin and Vittorio Cristini",

year = "2011",

doi = "10.1146/annurev-bioeng-071910-124729",

language = "English",

volume = "13",

pages = "127--155",

booktitle = "Annual review of biomedical engineering",

publisher = "Annual Reviews",

}

TY - CHAP

T1 - Multiscale cancer modeling

AU - Deisboeck, Thomas S.

AU - Wang, Zhihui

AU - Macklin, Paul

AU - Cristini, Vittorio

PY - 2011

Y1 - 2011

N2 - Simulating cancer behavior across multiple biological scales in space and time, i.e., multiscale cancer modeling, is increasingly being recognized as a powerful tool to refine hypotheses, focus experiments, and enable more accurate predictions. A growing number of examples illustrate the value of this approach in providing quantitative insights in the initiation, progression, and treatment of cancer. In this review, we introduce the most recent and important multiscale cancer modeling works that have successfully established a mechanistic link between different biological scales. Biophysical, biochemical, and biomechanical factors are considered in these models. We also discuss innovative, cutting-edge modeling methods that are moving predictive multiscale cancer modeling toward clinical application. Furthermore, because the development of multiscale cancer models requires a new level of collaboration among scientists from a variety of fields such as biology, medicine, physics, mathematics, engineering, and computer science, an innovative Web-based infrastructure is needed to support this growing community.

AB - Simulating cancer behavior across multiple biological scales in space and time, i.e., multiscale cancer modeling, is increasingly being recognized as a powerful tool to refine hypotheses, focus experiments, and enable more accurate predictions. A growing number of examples illustrate the value of this approach in providing quantitative insights in the initiation, progression, and treatment of cancer. In this review, we introduce the most recent and important multiscale cancer modeling works that have successfully established a mechanistic link between different biological scales. Biophysical, biochemical, and biomechanical factors are considered in these models. We also discuss innovative, cutting-edge modeling methods that are moving predictive multiscale cancer modeling toward clinical application. Furthermore, because the development of multiscale cancer models requires a new level of collaboration among scientists from a variety of fields such as biology, medicine, physics, mathematics, engineering, and computer science, an innovative Web-based infrastructure is needed to support this growing community.

KW - Cancer systems biology

KW - Discrete

KW - Continuum

KW - Hybrid

KW - Clinical translation

KW - Personalized medicine

KW - Epidermal growth factor

KW - Tumor-induced angiogenesis

KW - Cell lung cancer

KW - Semantic web technologies

KW - Agent-based model

KW - Systems biology

KW - Factor receptor

KW - Nonlinear simulation

KW - Multicellular patterns

KW - Biochemical pathways

U2 - 10.1146/annurev-bioeng-071910-124729

DO - 10.1146/annurev-bioeng-071910-124729

M3 - Other chapter contribution

C2 - 21529163

VL - 13

SP - 127

EP - 155

BT - Annual review of biomedical engineering

PB - Annual Reviews

CY - Palo Alto

ER -

Multiscale cancer modeling

Abstract

UN SDGs

Keywords

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Cite this