Inferring cell cycle phases from a partially temporal network of protein interactions.

ABSTRACT: The temporal organization of biological systems is key for understanding them, but current methods for identifying this organization are often ad hoc and require prior knowledge. We present Phasik, a method that automatically identifies this multiscale organization by combining time series data (protein or gene expression) and interaction data (protein-protein interaction network). Phasik builds a (partially) temporal network and uses clustering to infer temporal phases. We demonstrate the method's effectiveness by recovering well-known phases and sub-phases of the cell cycle of budding yeast and phase arrests of mutants. We also show its general applicability using temporal gene expression data from circadian rhythms in wild-type and mutant mouse models. We systematically test Phas

SUBMITTER: Lucas M 

PROVIDER: S-EPMC10014271 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature