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

Abstract

The temporal organisation of biological systems into phases and subphases 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 under study. Here, we recover the temporal organisation of the cell cycle of budding yeast into phases and subphases, 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 protein concentration or RNA expression time series data. Then, we cluster the snapshots of this temporal network to infer phases, which show 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. 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.

Embedding and trajectories of temporal networks

“…In particular, the location and dynamics of the network trajectory in the embedding space may give us a compact summary of brain dynamics without the need of explicitly defining and detecting discrete dynamical states of the brain network or their transient dynamics [32,44,57]. State-transition dynamics has also been applied to data of cell cycle dynamics [69]. In future works, it could be interesting to apply our methods to these data to characterize periodicity and fluctuations around periodic dynamics in cell cycles.…”