What can we learn when fitting a simple telegraph model to a complex gene expression model?

ABSTRACT: In experiments, the distributions of mRNA or protein numbers in single cells are often fitted to the random telegraph model which includes synthesis and decay of mRNA or protein, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by crucial biological mechanisms such as feedback regulation, non-exponential gene inactivation durations, and multiple gene activation pathways. Here we investigate the dynamical properties of four relatively complex gene expression models by fitting their steady-state mRNA or protein number distributions to the simple telegraph model. We show that despite the underlying complex biological mechanisms, the telegraph model with three effective parameters can accurately capt

SUBMITTER: Jiao F 

PROVIDER: S-EPMC11125521 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature