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

Zhihui Wang, Thomas S. Deisboeck

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationScientific Modeling and Simulations
EditorsSidney Yip, Tomas Diaz de la Rubia
Number of pages13
StatePublished - Dec 1 2009

Publication series

NameLecture Notes in Computational Science and Engineering
Volume68 LNCSE
ISSN (Print)1439-7358


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

ASJC Scopus subject areas

  • Modeling and Simulation
  • Engineering(all)
  • Discrete Mathematics and Combinatorics
  • Control and Optimization
  • Computational Mathematics


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