Abstract
Quantitative, predictive multiscale models have shown 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. This allows for data integration, hypothesis generation, and prediction, and has the potential to significantly impact biomedical research. Full characterization of multiscale spatiotemporal processes and the feedback processes between scales is often beyond the capacity of any single modeling method, making advanced multiscale methods necessary to address these challenges. In this article, we discuss current advances in the development of multiscale modeling methods and some key successes in multiscale modeling-aided biomedical research.
| Original language | English (US) |
|---|---|
| Title of host publication | Encyclopedia of Cell Biology |
| Subtitle of host publication | Volume 1-6, Second Edition |
| Publisher | Elsevier |
| Pages | 251-260 |
| Number of pages | 10 |
| Volume | 6 |
| ISBN (Electronic) | 9780128216248 |
| DOIs | |
| State | Published - Jan 1 2022 |
Keywords
- Adaptive hybrid modeling
- Agent-based modeling
- Clinical translation
- Computer simulation
- Continuum modeling
- Discrete modeling
- Drug target discovery
- Dynamic density functional theory
- Equation-free approach
- Integrative approach
- Invasion
- Mean field theory
- Molecular signaling network
- Tumor growth
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)