Inferring cell cycle phases from a partially network of interactions

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 recov-ering 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 Phasik's robustness and investigate the effect of having only partial temporal information. As time-resolved, multiomics datasets become more common, this method will allow the study of temporal regulation in lesser-known biological contexts, such as development, metabolism, and disease.

Automated summary

Phasik is an automatic method that identifies the temporal organization of biological systems by combining time series data and interaction data. It builds a (partially) temporal network and uses clustering to infer temporal phases. We demonstrate its effectiveness by studying different phases of the cell cycle and phase arrests of mutants, as well as temporal gene expression data from circadian rhythms in mouse models. This method will be valuable for studying temporal regulation in lesser-known biological contexts as high-resolution multiomics datasets become more common.