Hierarchical Bayesian models of transcriptional and translational regulation processes with delays.

ABSTRACT:

Motivation

Simultaneous recordings of gene network dynamics across large populations have revealed that cell characteristics vary considerably even in clonal lines. Inferring the variability of parameters that determine gene dynamics is key to understanding cellular behavior. However, this is complicated by the fact that the outcomes and effects of many reactions are not observable directly. Unobserved reactions can be replaced with time delays to reduce model dimensionality and simplify inference. However, the resulting models are non-Markovian, and require the development of new inference techniques.

Results

We propose a non-Markovian, hierarchical Bayesian inference framework for quantifying the variability of cellular processes within and across cells in a population. We

SUBMITTER: Cortez MJ 

PROVIDER: S-EPMC8696106 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature