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Strategies and Tactics in Multiscale Modeling of Cell-to-Organ Systems.
James B Bassingthwaighte1, Howard Jay Chizeck, Les E Atlas
1Departments of Bioengineering and Electrical Engineering, University of Washington, Seattle, WA 98195-7962 USA.
Proceedings of the IEEE. Institute of Electrical and Electronics Engineers
|September 28, 2011
Summary
Physiological modeling integrates knowledge across biological scales. Adaptive modeling strategies are crucial for maintaining accuracy as system complexity changes, ensuring robust simulations.
Area of Science:
- Computational Biology
- Systems Physiology
- Mathematical Modeling
Background:
- Physiological knowledge integration relies heavily on mathematical modeling.
- Models represent biological systems from molecular levels to integrated organismal behavior.
- Model complexity varies, with simplifications necessary for computational feasibility.
Purpose of the Study:
- To highlight the importance of adaptive modeling in physiology.
- To address the challenges of maintaining model accuracy under changing conditions.
- To emphasize the need for error detection and complexity shifting in simulations.
Main Methods:
- Developing quantitative mathematical descriptions of physiological systems.
- Implementing strategies for error recognition and model adaptation.
- Utilizing methods for mapping between different model complexity levels.
Main Results:
- Model simplifications can reduce robustness and accuracy when conditions change.
- Detecting deviations from model validity range is critical for adaptive systems.
- Adaptive modeling requires dynamic adjustment of model complexity.
Conclusions:
- Adaptive modeling and complexity shifting are essential for accurate large-scale physiological simulations.
- Robustness in physiological models depends on the ability to adapt to dynamic environmental changes.
- Error recognition and dynamic model formulation are key to maintaining simulation integrity.
