Gradient matching accelerates mixed-effects inference for biochemical networks.

Area of Science:

• Systems Biology
• Computational Biology
• Biophysics

Background:

• Single-cell time series data display significant population variability.
• Modeling intracellular processes requires parameter distributions reflecting this variability, not just population averages.
• Global Two-Stage (GTS) is a common method for nonlinear mixed-effects models (NLME) but is computationally intensive.

Purpose of the Study:

• To develop an efficient alternative to the GTS method for parameter estimation in NLME models.
• To reduce the computational burden associated with analyzing single-cell data variability.
• To enhance the applicability of complex NLME models in systems biology research.

Main Methods:

• Propose Gradient Matching GTS (GMGTS), an integration-free parameter estimation technique.
• Incorporate gradient matching into the GTS framework for uncertainty propagation and iterative estimation.
• Utilize systems linear in parameters, such as biochemical networks with mass action kinetics.

Main Results:

• GMGTS significantly reduces computational demands compared to the standard GTS approach.
• The method facilitates the application of complex NLME models for analyzing cellular variability.
• GMGTS enables uncertainty propagation and iterative estimation for partially observed systems.

Conclusions:

• GMGTS offers a computationally efficient alternative for parameter estimation in NLME models.
• This method is particularly beneficial for analyzing single-cell time series data with inherent variability.
• The developed approach expands the capabilities of NLME modeling in systems biology.