Optimization and model reduction in the high dimensional parameter space of a budding yeast cell cycle model

Area of Science:

• Systems Biology
• Computational Biology
• Biophysics

Background:

• Parameter estimation is crucial for mathematical modeling of protein regulatory networks.
• Estimating parameters for large, realistic networks with many species and reactions is challenging.
• Accurate models require fitting to experimental data, such as genetic strain phenotypes.

Purpose of the Study:

• To develop and present an effective parameter estimation approach for complex biological models.
• To fit a detailed model of the budding yeast cell cycle to experimental data.
• To improve the accuracy and efficiency of parameter estimation in systems biology.

Main Methods:

• The study employed a parameter estimation algorithm combining Latin hypercube sampling and differential evolution.
• An initial parameter guess was refined by exploring surrounding regions and selecting optimal sets.
• The algorithm was tested on a 26-nonlinear ordinary differential equation model of the yeast cell cycle with 126 rate constants.

Main Results:

• The algorithm improved the model's ability to predict experimental phenotypes from 72 to 105-111 out of 119 genetic strains.
• This success rate is comparable to manual parameter tuning by expert modelers.
• The analysis identified critical parameters and strains, offering biological insights and suggesting avenues for computational simplification.

Conclusions:

• The developed approach is a valuable tool for systems biologists needing to fit complex dynamical models to large datasets.
• The fitting process revealed correlations between model components and experimental data.
• The method enhances the utility of mathematical models in understanding biological systems.