Cell Reports: Methods (Feb 2023)
Inferring cell cycle phases from a partially temporal network of protein interactions
Abstract
Summary: 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 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. Motivation: In many biological processes, such as the cell cycle, molecules are produced and translocated, interact with each other, and are destroyed following a strict temporal order to ensure proper execution of molecular events. An increasing amount of time-resolved data is becoming available trying to capture these ordered molecular states or phases of a biological system. To understand or predict these phases would yield crucial insight into the temporal organization of such systems.
