Alberto Portela | Searcharxiv

arXiv2026

A Hybrid Framework for Uncertainty Quantification in Partially Observed Dynamic Biological Systems

Mechanistic ordinary differential equation (ODE) models are widely used in systems biology, but uncertainty quantification (UQ) remains difficult when only a subset of state variables is experimentally observed. Existing Bayesian and likelihood-based approaches can be computationally demanding for nonlinear, weakly identifiable, or high-dimensional systems. We present a framework, and its corresponding software CUQDyn1 Plus, for UQ in partially observed ODE systems. Our method combines leave-one-out jackknife+-style empirical calibration for observed states with sensitivity-based Gaussian uncertainty propagation for hidden states. The software supports global parameter estimation, covariance propagation, bootstrap trajectory uncertainty and simulation-based calibration. It also facilitates comparison with Bayesian workflows, automated reporting, and reproducibility diagnostics. Validation on six benchmark systems shows accurate behavior in well-conditioned cases and model-dependent degradation under nonlinearity, weak identifiability, or global branch-switching non-identifiability. CUQDyn1 Plus provides a practical and computationally efficient UQ workflow for systems biology models with observed and latent states. Its diagnostic outputs help identify when local Gaussian propagation is reliable and when uncertainty bands should be interpreted cautiously, making it a useful complement to fully Bayesian workflows.

q-bio.QM↗

arXiv2024

Conformal Prediction in Dynamic Biological Systems

Uncertainty quantification (UQ) is the process of systematically determining and characterizing the degree of confidence in computational model predictions. In the context of systems biology, especially with dynamic models, UQ is crucial because it addresses the challenges posed by nonlinearity and parameter sensitivity, allowing us to properly understand and extrapolate the behavior of complex biological systems. Here, we focus on dynamic models represented by deterministic nonlinear ordinary differential equations. Many current UQ approaches in this field rely on Bayesian statistical methods. While powerful, these methods often require strong prior specifications and make parametric assumptions that may not always hold in biological systems. Additionally, these methods face challenges in domains where sample sizes are limited, and statistical inference becomes constrained, with computational speed being a bottleneck in large models of biological systems. As an alternative, we propose the use of conformal inference methods, introducing two novel algorithms that, in some instances, offer non-asymptotic guarantees, enhancing robustness and scalability across various applications. We demonstrate the efficacy of our proposed algorithms through several scenarios, highlighting their advantages over traditional Bayesian approaches. The proposed methods show promising results for diverse biological data structures and scenarios, offering a general framework to quantify uncertainty for dynamic models of biological systems.The software for the methodology and the reproduction of the results is available at https://zenodo.org/doi/10.5281/zenodo.13644870.

stat.ML↗