Modeling multiple experiments using regularized optimization: A case study on bacterial glucose utilization dynamics.

Area of Science:

• Systems Biology
• Biochemical Engineering
• Computational Biology

Background:

• Inverse modeling aims to define system dynamics using parameterized Ordinary Differential Equations (ODEs).
• Parameter estimation for multiple experimental conditions often results in complex, non-convex optimization problems.
• Accurate modeling of bacterial metabolism, like glucose utilization in Lactococcus lactis, is crucial for understanding cellular processes.

Purpose of the Study:

• To develop a flexible Ordinary Differential Equation (ODE) model for simulating glucose utilization in Lactococcus lactis.
• To simplify over-parameterized models using regularization and optimization techniques.
• To enable prediction of system dynamics across various experimental conditions.

Main Methods:

• Utilized in vivo Nuclear Magnetic Resonance (NMR) measurements from perturbation experiments.
• Developed a modified time-varying exponential decay ODE model.
• Employed particle swarm optimization (PSO) for parameter estimation with regularization penalties.
• Applied cross-validation for model robustness assessment.

Main Results:

• Successfully simplified an over-parameterized model through regularization, identifying shared and unique parameters across experiments.
• Developed a functional fit for experiment-specific parameters, enabling prediction for new conditions.
• Integrated the model with existing glycolysis pathways to reconstruct metabolite dynamics.

Conclusions:

• The proposed inverse modeling approach effectively reduces parameters in unidentifiable and over-parameterized models.
• This method supports feature selection for parametric models in systems biology.
• The technique offers a generalizable procedure for analyzing complex biological systems.