Diverse metabolic model parameters generate similar methionine cycle dynamics

Area of Science:

• Systems Biology
• Computational Biology
• Metabolic Network Modeling

Background:

• Parameter estimation is a key challenge in dynamic metabolic network modeling.
• System nonlinearity and data type influence parameter identification and model predictability.

Purpose of the Study:

• To investigate how system nonlinearity and data availability affect parameter estimation in dynamic metabolic models.
• To assess the predictive capability of models derived from potentially uncertain parameters.

Main Methods:

• Computational simulations using the methionine cycle as a model system.
• Inversion of simulated metabolite concentration data (with and without flux data) to identify model parameters.
• Analysis of parameter distribution, correlations, and model predictive performance.

Main Results:

• Thousands of diverse parameter families were consistent with data, often spanning over 1000-fold ranges.
• Model predictions remained reliable due to strong parameter correlations, even with broad individual parameter ranges.
• Inclusion of flux data significantly improved parameter correlations and flux prediction reliability.

Conclusions:

• Dynamic metabolic models can be predictive even when individual parameters are not uniquely determined.
• System-level data, particularly flux data, is valuable for robust parameter estimation and reliable model predictions.
• This approach supports the development of predictive dynamic metabolic models despite parameter uncertainty.