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On the parameter combinations that matter and on those that do not: data-driven studies of parameter
Nikolaos Evangelou1, Noah J Wichrowski2, George A Kevrekidis3
1Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA.
PNAS Nexus
|January 30, 2023
Summary
This study introduces a data-driven method to identify essential model parameters, simplifying complex chemical systems. It uses Diffusion Maps and neural networks to find effective parameters for better prediction and estimation.
Area of Science:
- Computational Chemistry
- Systems Biology
- Data Science
Background:
- Model nonidentifiability poses challenges in chemical kinetics and systems biology.
- Identifying essential parameters is crucial for accurate model behavior prediction and estimation.
Purpose of the Study:
- To develop a data-driven approach for characterizing model parameter nonidentifiability.
- To identify minimal sets of effective parameters that capture system behavior.
- To disentangle redundant parameters from behavior-influencing ones.
Main Methods:
- Diffusion Maps and their extensions for parameter characterization.
- Conformal Autoencoder Neural Networks for disentangling parameters.
- Kernel-based Jointly Smooth Function technique.
- Validation on a multisite phosphorylation model.
Main Results:
- Discovery of effective parameters, which are minimal parameter combinations characterizing model output.
- Successful disentanglement of redundant versus behavior-influencing parameter combinations.
- Demonstrated utility for behavior prediction and parameter estimation.
Conclusions:
- The data-driven approach effectively characterizes model nonidentifiability.
- Effective parameters offer interpretable and reduced representations for complex systems.
- The method provides a powerful tool for analyzing kinetic models and parameter estimation.
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