Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data.

ABSTRACT: Variability is an intrinsic property of biological systems and is often at the heart of their complex behaviour. Examples range from cell-to-cell variability in cell signalling pathways to variability in the response to treatment across patients. A popular approach to model and understand this variability is nonlinear mixed effects (NLME) modelling. However, estimating the parameters of NLME models from measurements quickly becomes computationally expensive as the number of measured individuals grows, making NLME inference intractable for datasets with thousands of measured individuals. This shortcoming is particularly limiting for snapshot datasets, common e.g. in cell biology, where high-throughput measurement techniques provide large numbers of single cell measurements. We introduce a n

SUBMITTER: Augustin D 

PROVIDER: S-EPMC10237648 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature