Computational modeling of brain tumors: discrete, continuum or hybrid?

Part of the Lecture Notes in Computational Science and Engineering book series (LNCSE, volume 68)

Abstract

In spite of all efforts, patients diagnosed with highly malignant brain tumors (gliomas), continue to face a grim prognosis. Achieving significant therapeutic advances will also require a more detailed quantitative understanding of the dynamic interactions among tumor cells, and between these cells and their biological microenvironment. Data-driven computational brain tumor models have the potential to provide experimental tumor biologists with such quantitative and cost-efficient tools to generate and test hypotheses on tumor progression, and to infer fundamental operating principles governing bidirectional signal propagation in multicellular cancer systems. This review highlights the modeling objectives of and challenges with developing such in silico brain tumor models by outlining two distinct computational approaches: discrete and continuum, each with representative examples. Future directions of this integrative computational neuro-oncology field, such as hybrid multiscale multiresolution modeling are discussed.

Keywords

Brain tumor Agent-based model Cellular automata Continuum Multi-scale 

Abbreviations

ABM

Agent-based model

CA

Cellular automata

EGFR

Epidermal growth factor receptor

ECM

Extracellular matrix

GBM

Glioblastoma

MRI

Magnetic resonance imaging

PLCγ

Phopholipase Cγ

ROI

Region of interest

2D

Two-dimensional

3D

Three-dimensional

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Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  1. 1.Complex Biosystems Modeling LaboratoryHarvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital-EastCharlestownUSA

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