AI Insight
This study introduces a Bayesian framework for parameter balancing in kinetic models used in systems biology, addressing key limitations of classical approaches such as incomplete data, inconsistency, and poor uncertainty quantification. The framework enforces thermodynamic constraints while estimating full posterior uncertainty, and extends beyond standard Gaussian assumptions by incorporating robust Student-t distributions and skewed error models to handle outliers and model misspecification. Random effects are also integrated to account for variability across different experimental sources, and calibration is validated through leave-one-out cross-validation and posterior-predictive coverage analysis.
Why it matters
Kinetic models are essential for simulating and understanding biological systems, and this framework could enable more reliable and reproducible metabolic modeling by providing well-calibrated parameter estimates even when experimental data are sparse, heterogeneous, or of variable quality. This has practical relevance for applications in metabolic engineering, drug target identification, and the construction of large-scale genome-scale kinetic models.
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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 or group 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. Keywords: Parameter balancing; calibrated uncertainty; kinetic modelling; Bayesian inference; systems biology; mixed effects.