Dynamic Bayesian sensitivity analysis of a myocardial metabolic model
D Calvetti1, R Hageman, R Occhipinti
1Case University, Department of Mathematics and Center for Modelling Integrated Metabolic Systems, Cleveland, OH 44106, USA. daniela.calvetti@case.edu
Mathematical Biosciences
|February 22, 2008
Summary
This study introduces a new method for analyzing dynamic metabolic models, accounting for parameter uncertainty and time-varying sensitivities. This approach enhances understanding of model behavior and predictions, especially when parameters are hard to measure.
Area of Science:
- Systems Biology
- Computational Biology
- Metabolic Modeling
Background:
- Dynamic metabolic models often have numerous parameters that are difficult to measure or non-physical.
- Insufficient data typically prevents full model identification, leading to parameter uncertainty.
Purpose of the Study:
- To develop a robust methodology for sensitivity analysis of dynamic metabolic models.
- To address the challenge of parameter uncertainty and its impact on model output sensitivity.
- To provide effective visualization tools for interpreting complex sensitivity analyses.
Main Methods:
- Proposed a novel sensitivity analysis methodology for dynamic metabolic models.
- Sampled from the posterior density of model parameters to account for their variability.
- Developed visualization techniques to display sensitivity variations over time and across parameter samples.
Main Results:
- Demonstrated a method to analyze sensitivity considering parameter distributions and time-varying effects.
- Applied the methodology to a myocardial metabolism model during ischemia.
- Analyzed sensitivity of key metabolites (lactate, glycogen, ATP, ADP, NAD+, NADH) in different cellular compartments.
Conclusions:
- The proposed methodology offers a comprehensive approach to sensitivity analysis for under-identified dynamic metabolic models.
- Effective visualization is crucial for interpreting the results of this doubly varying sensitivity analysis.
- This work advances the understanding of myocardial metabolism during ischemia by quantifying parameter sensitivities.
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