Dynamic Phenotypes and Model Discrimination in Systems Biology
Analysis
This paper advocates for a shift in focus from steady-state analysis to transient dynamics in understanding biological networks. It emphasizes the importance of dynamic response phenotypes like overshoots and adaptation kinetics, and how these can be used to discriminate between different network architectures. The paper highlights the role of sign structure, interconnection logic, and control-theoretic concepts in analyzing these dynamic behaviors. It suggests that analyzing transient data can falsify entire classes of models and that input-driven dynamics are crucial for understanding, testing, and reverse-engineering biological networks.
Key Takeaways
- •Focus on transient dynamics is crucial for understanding biological networks.
- •Dynamic phenotypes like overshoots and adaptation kinetics are key.
- •Sign structure and interconnection logic are important for model discrimination.
- •Control-theoretic concepts can be used as mathematical tools.
- •Input-driven dynamics are essential for reverse-engineering.
“The paper argues for a shift in emphasis from asymptotic behavior to transient and input-driven dynamics as a primary lens for understanding, testing, and reverse-engineering biological networks.”