Multiscale cancer modeling

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

    Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

    205 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 languageEnglish
    Title of host publicationAnnual review of biomedical engineering
    Place of PublicationPalo Alto
    PublisherAnnual Reviews
    Pages127-155
    Number of pages29
    Volume13
    DOIs
    Publication statusPublished - 2011

    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

    Cite this

    Deisboeck, T. S., Wang, Z., Macklin, P., & Cristini, V. (2011). Multiscale cancer modeling. In Annual review of biomedical engineering (Vol. 13, pp. 127-155). Palo Alto: Annual Reviews. https://doi.org/10.1146/annurev-bioeng-071910-124729
    Deisboeck, Thomas S. ; Wang, Zhihui ; Macklin, Paul ; Cristini, Vittorio. / Multiscale cancer modeling. Annual review of biomedical engineering. Vol. 13 Palo Alto : Annual Reviews, 2011. pp. 127-155
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    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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    Deisboeck, TS, Wang, Z, Macklin, P & Cristini, V 2011, Multiscale cancer modeling. in Annual review of biomedical engineering. vol. 13, Annual Reviews, Palo Alto, pp. 127-155. https://doi.org/10.1146/annurev-bioeng-071910-124729

    Multiscale cancer modeling. / Deisboeck, Thomas S.; Wang, Zhihui; Macklin, Paul; Cristini, Vittorio.

    Annual review of biomedical engineering. Vol. 13 Palo Alto : Annual Reviews, 2011. p. 127-155.

    Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

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    AU - Wang, Zhihui

    AU - Macklin, Paul

    AU - Cristini, Vittorio

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    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

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    M3 - Other chapter contribution

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    Deisboeck TS, Wang Z, Macklin P, Cristini V. Multiscale cancer modeling. In Annual review of biomedical engineering. Vol. 13. Palo Alto: Annual Reviews. 2011. p. 127-155 https://doi.org/10.1146/annurev-bioeng-071910-124729