Computational Approaches for Multiscale Modeling

Z. Wang, V. Cristini, T. S. Deisboeck

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Quantitative, predictive multiscale models show promise in accurately representing the behavior of complex biological systems across a wide range of spatial and temporal scales based on experimental and clinical data. Eventually, this allows for data integration, hypothesis generation, and prediction, and thus has the potential to significantly impact biomedical research. Biological processes can occur at the same scale as well as between scales, forming a complex system with multiple feedback and feed-forward loops. Advanced multiscale methods are therefore needed to begin to address these challenges. In this article, we discuss current advances in the development of multiscale modeling methods. We also discuss some key points in multiscale modeling-aided biomedical research.

Original languageEnglish (US)
Title of host publicationSystems Cell Biology
PublisherElsevier
Pages80-85
Number of pages6
Volume4
ISBN (Electronic)9780123944474
ISBN (Print)9780123947963
DOIs
StatePublished - Jan 1 2016

Keywords

  • Adaptive hybrid modeling
  • Agent-based modeling
  • Computer simulation
  • Continuum modeling
  • Discrete modeling
  • Drug target
  • Dynamic density functional theory
  • Equation-free approach
  • Hypothesis generation
  • Integrative approach
  • Mean field theory
  • Molecular signaling network
  • Tumor growth and development

ASJC Scopus subject areas

  • Medicine(all)

Fingerprint Dive into the research topics of 'Computational Approaches for Multiscale Modeling'. Together they form a unique fingerprint.

Cite this