Optimal experiment selection for parameter estimation in biological differential equation models.

Area of Science:

• Systems Biology
• Computational Biology
• Biophysics

Background:

• Parameter estimation in biological models, particularly gene regulatory networks (GRNs), presents significant challenges.
• Accurate estimation requires understanding parameters like decay rates, reaction rates, Michaelis-Menten constants, and Hill coefficients.

Purpose of the Study:

• To investigate the extent to which parameters in differential equation-based GRN models can be efficiently estimated.
• To determine the role of appropriate experimental selection in improving parameter estimation accuracy.

Main Methods:

• Utilized a minimization formulation to identify parameter values that best fit experimental data.
• Employed local Fisher Information derived from local minima to guide the selection of new, informative experiments.
• Iteratively applied minimization and experiment selection to enhance parameter estimation.

Main Results:

• Demonstrated that insufficient data leads to multiple local minima in parameter estimation.
• Showed that selecting experiments based on local Fisher Information effectively discriminates among these local minima.
• Achieved high accuracy in parameter estimation through iterative minimization and experiment selection, with experiment choices being robust across different local minima.

Conclusions:

• Appropriate experimental design enables efficient and accurate estimation of all GRN parameters.
• Strategic experiment selection can also constrain model predictions with fewer experiments, offering an alternative to parameter inference.
• Model prediction and parameter inference are distinct calibration approaches with differing data and cost requirements.