Minimally sufficient experimental design using identifiability analysis.

ABSTRACT: Mathematical models are increasingly being developed and calibrated in tandem with data collection, empowering scientists to intervene in real time based on quantitative model predictions. Well-designed experiments can help augment the predictive power of a mathematical model but the question of when to collect data to maximize its utility for a model is non-trivial. Here we define data as model-informative if it results in a unique parametrization, assessed through the lens of practical identifiability. The framework we propose identifies an optimal experimental design (how much data to collect and when to collect it) that ensures parameter identifiability (permitting confidence in model predictions), while minimizing experimental time and costs. We demonstrate the power of the method by

SUBMITTER: Gevertz JL 

PROVIDER: S-EPMC10771435 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature