Conformal prediction for uncertainty quantification in dynamic biological systems

Uncertainty quantification (UQ) is the process of systematically determining and characterizing the degree of confidence in computational model predictions. In systems biology, and particularly with dynamic models, UQ is critical due to the nonlinearities and parameter sensitivities that influence the behavior of complex biological systems. Addressing these issues through robust UQ enables a deeper understanding of system dynamics and more reliable extrapolation beyond observed conditions. Many state-of-the-art UQ approaches in this field are grounded in Bayesian statistical methods. While these frameworks naturally incorporate uncertainty quantification, they often require the specification of parameter distributions as priors and may impose parametric assumptions that do not always reflect biological reality. Additionally, Bayesian methods can be computationally expensive, posing significant challenges when dealing with large-scale models and seeking rapid, reliable uncertainty calibration. As an alternative, we propose using conformal predictions methods and introduce two novel algorithms designed for dynamic biological systems. These approaches can provide non-asymptotic guarantees, improving robustness and scalability across various applications, even when the predictive models are misspecified. Through several illustrative scenarios, we demonstrate that these conformal algorithms can serve as powerful complements—or even alternatives—to conventional Bayesian methods, delivering effective uncertainty quantification for predictive tasks in systems biology.Author summary: Uncertainty quantification involves determining how confident we are in the predictions made by mathematical models. This process is vital in the field of systems biology because it helps us understand and predict how these systems behave, despite their complexity. Typically, Bayesian statistics are used for this task. Although powerful, these methods often require specific prior information and make assumptions that may not always hold true for biological systems. Additionally, they struggle when we have limited data, and can be slow for large models. To address these issues, here we have developed two new algorithms based on conformal inference methods. These algorithms offer excellent reliability and scalability. Testing in various scenarios has demonstrated that they outperform traditional Bayesian methods, particularly when applied to large models. Our approach provides a new, general, and flexible method for quantifying uncertainty in dynamic biological models.