The chronODE framework for modelling multi-omic time series with ordinary differential equations and machine learning.

ABSTRACT: Many genome-wide studies capture isolated moments in cell differentiation or organismal development. Conversely, longitudinal studies provide a more direct way to study these kinetic processes. Here, we present an approach for modeling gene-expression and chromatin kinetics from such studies: chronODE, an interpretable framework based on ordinary differential equations. chronODE incorporates two parameters that capture biophysical constraints governing the initial cooperativity and later saturation in gene expression. These parameters group genes into three major kinetic patterns: accelerators, switchers, and decelerators. Applying chronODE to bulk and single-cell time-series data from mouse brain development reveals that most genes (~87%) follow simple logistic kinetics. Among them, genes

SUBMITTER: Borsari B 

PROVIDER: S-EPMC12365117 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature