Bayesian Parameter Balancing Enables Robust and Consistent Estimation of Kinetic Parameter Uncertainty

Motivation

Kinetic models are central to systems biology, but enzyme-kinetic parameters compiled from the literature and databases are often incomplete, inconsistent, and measured under heterogeneous conditions. Classical parameter balancing helps infer missing parameters, yet it often lacks calibrated uncertainty, robustness to misspecification, and explicit treatment of source-level heterogeneity.

Results

We develop a formal Bayesian parameter balancing framework that enforces thermodynamic constraints, estimates full posterior uncertainty, and validates calibration using leave-one-out cross-validation and posterior-predictive coverage. Beyond the classical Gaussian formulation, we introduce robust Student- t and skewed error models to improve reliability under outliers and model misspecification, and incorporate random effects to account for source-level or group-level variability across studies. The resulting approach yields thermodynamically consistent parameter sets with well-calibrated credible intervals on held-out data, offering a Bayesian parameter balancing approach useful to systems biology researchers.

Availability and implementation

Source code, data, workflows, a Julia package and command-line usage are available at the project GitHub repository .

Graphical Abstract