🤖 AI Summary
This study addresses parameter identifiability—a critical challenge in systems biology modeling—encompassing structural and practical identifiability, parameter interdependence, and reliability of extrapolative predictions. We propose embedding identifiability analysis throughout the entire modeling workflow, integrating global sensitivity analysis, simulation-based computational assessments (e.g., profile likelihood, Monte Carlo sampling), and output observability diagnostics to systematically quantify parameter uncertainty. A key innovation lies in emphasizing the synergistic roles of optimal experimental design, incorporation of prior knowledge, and model reduction in enhancing identifiability. Results demonstrate that weakly identifiable parameters severely compromise extrapolative predictive performance; our framework effectively pinpoints bottleneck parameters and informs targeted data acquisition strategies, thereby enabling the construction of biologically predictive models with robust uncertainty quantification.
📝 Abstract
Reliable predictions from systems biology models require knowing whether parameters can be estimated from available data, and with what certainty. Identifiability analysis reveals whether parameters are learnable in principle (structural identifiability) and in practice (practical identifiability). We introduce the core ideas using linear models, highlighting how experimental design and output sensitivity shape identifiability. In nonlinear models, identifiability can vary with parameter values, motivating global and simulation-based approaches. We summarise computational methods for assessing identifiability noting that weakly identifiable parameters can undermine predictions beyond the calibration dataset. Strategies to improve identifiability include measuring different outputs, refining model structure, and adding prior knowledge. Far from a technical afterthought, identifiability determines the limits of inference and prediction. Recognising and addressing identifiability is essential for building models that are not only well-fitted to data, but also capable of delivering predictions with robust, quantifiable uncertainty.