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Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
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Charting a New Frontier Integrating Mathematical Modeling in Complex Biological Systems from Molecules to Ecosystems
Katharine A White1, Kira D McEntire2, Nicole R Buan3
1Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame IN 46656.
Integrative and Comparative Biology
|July 20, 2021
Summary
Quantitative biology advances enable sophisticated mathematical modeling across scales. Integrating experimental biology with robust models is crucial for understanding complex biological systems.
Area of Science:
- Quantitative biology
- Mathematical modeling
- Systems biology
Background:
- Quantitative data collection and analysis across molecular, cellular, organismal, and ecological scales have transformed biological understanding.
- Increased computational power has facilitated the development of sophisticated mathematical models for biological systems.
- A disconnect persists between mathematical modeling and experimental biology, hindering full integration.
Purpose of the Study:
- To highlight the urgent need for integrating mathematical models across biological scales.
- To advocate for developing models robust to biological heterogeneity.
- To promote interdisciplinary collaboration between modeling and experimental biology.
Main Methods:
- Exploration of existing mathematical models across biological scales.
- Development of novel modeling approaches robust to biological heterogeneity.
- Emphasis on harnessing feedback loops in iterative modeling processes.
Main Results:
- Identified opportunities to apply and develop mathematical models for complex biological systems.
- Highlighted the potential of novel modeling schemes to deepen understanding of biological principles.
- Emphasized the need for a cultural shift towards interdisciplinary interactions.
Conclusions:
- Enhanced integration of biological experimentation and mathematical modeling is essential.
- This integration will transform the ability to understand and predict complex biological systems.
- Fostering cross-field interactions is key to advancing quantitative biology.
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