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 language | English (US) |
|---|---|
| Title of host publication | Systems Cell Biology |
| Publisher | Elsevier |
| Pages | 80-85 |
| Number of pages | 6 |
| Volume | 4 |
| ISBN (Electronic) | 9780123944474 |
| ISBN (Print) | 9780123947963 |
| DOIs | |
| State | Published - 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
- General Medicine