Scalable and flexible inference framework for stochastic dynamic single-cell models.

ABSTRACT: Understanding the inherited nature of how biological processes dynamically change over time and exhibit intra- and inter-individual variability, due to the different responses to environmental stimuli and when interacting with other processes, has been a major focus of systems biology. The rise of single-cell fluorescent microscopy has enabled the study of those phenomena. The analysis of single-cell data with mechanistic models offers an invaluable tool to describe dynamic cellular processes and to rationalise cell-to-cell variability within the population. However, extracting mechanistic information from single-cell data has proven difficult. This requires statistical methods to infer unknown model parameters from dynamic, multi-individual data accounting for heterogeneity caused by both

SUBMITTER: Persson S 

PROVIDER: S-EPMC9159578 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature