Inferring Cell Cycle Phases From a Partially Temporal Network of Protein Interactions

Abstract

The temporal organisation of biological systems into phases and sub-phases is often crucial to their functioning. Identifying this multiscale organisation can yield insight into the underlying biological mechanisms at play. To date, however, this identification requires a priori biological knowledge of the system. Here, we put forward a new method to identify the temporal organisation of the cell cycle into phases and sub-phases, in an automated way. To do so, we model the cell cycle as a partially temporal network of protein-protein interactions (PPIs) by combining a traditional static PPI network with PPI time series data. Then, we cluster the snapshots of this temporal network to obtain phases. We first apply the method to the well known cell cycle of budding yeast, obtaining a good agreement with our biological knowledge of the cell cycle. We systematically test the robustness of the approach and investigate the effect of having only partial temporal information. In addition, we identify the phase arrests of cell cycle mutants, and infer mitochondrial state transitions during flight muscle development in Drosophila. The generality of the method makes it suitable for application to other, less well known biological systems for which the temporal organisation of processes plays an important role.