Identifiability and Uncertainty Quantification in Complex Biological Systems

As biology has become quantitative, mechanistic and hybrid models have grown more intricate, but parameter identifiability, the ability to determine model parameters uniquely from experimental data, remains a major hurdle: non-identifiable parameters make mechanistic insight ambiguous and predictions overconfident, especially in stochastic, hybrid and multiscale models where deterministic tools fall short. This workshop unites mathematicians, statisticians and biologists to establish a unified workflow for model validation: re-examining identifiability as a foundational constraint on inference, developing methods to assess identifiability in stochastic and multiscale settings, and creating algorithmic tools for identifiability-aware experimental design, with the aim of setting new standards for uncertainty quantification in areas such as precision medicine and synthetic biology. Participation is by registration and acceptance through NITMB's workshop form. Participants based more than 50 miles from NITMB register by 2 July 2027; NITMB covers their travel, lodging, breakfast and lunch. Local participants (within 50 miles) register by 6 August 2027.