Speakers
Description
Modern single-cell technologies measure gene expression with unprecedented resolution, yet this data reflect multiple overlapping sources of variation. Separating technical noise from biologically meaningful fluctuations to reveal the underlying transcriptional dynamics remains a central challenge.
Mechanistic models offer a framework for proposing hypotheses about the molecular processes generating observed data. Unlike purely statistical or machine learning approaches, these models connect measurable parameters to specific biological mechanisms, enabling us to move from describing patterns to explaining their biophysical origins.
Applying such models to single-cell data, however, presents significant obstacles. Snapshot measurements provide limited information for parameter estimation, making it difficult to distinguish between competing mechanisms. Emerging temporal, multiomic and spatial profiling technologies offer new opportunities to resolve these ambiguities, but introduce model complexity and computational challenges.
This mini symposium will explore recent advances that address these challenges. Speakers will present new experimental designs, inference methods and modelling frameworks that improve identifiability, exploit structured single-cell datasets and enable robust validation of mechanistic conclusions. Our goal is to move the field toward a framework where single-cell data routinely yield quantitative, mechanistic understanding of cellular regulation.
