Title: The cell cycle as a temporal network of protein interactions
Biological systems often operate over multiple timescales and their temporal organisation is often crucial to their functioning. The cell cycle illustrates this well: before dividing into two cells, the cell must go through 4 consecutive phases. Each of these phases corresponds to specific and well-timed physiological processes, driven by protein-protein interactions (PPIs), necessary to the cycle progression. Any deviation from this temporal organisation can indicate a change in cell fate, behaviour, or a malfunction which can lead to diseases. Identifying, predicting, and understanding this temporal organisation can thus yield important insight into their behaviour.
Here, we infer phases of the cell cycle by modelling it as a temporal network of protein-protein interactions and clustering the network’s snapshots. First, we build our temporal network by integrating time series of protein concentrations to a static PPI network. Second, we group snapshots at different times that have a similar structure by clustering them. We show that the obtained clusters recover the 4 known phases of the cell cycle. Moreover, we identify the temporal organisation across a range of timescales by clustering the snapshots for different number of clusters. Results are in agreement with biological knowledge, and we show how robust they are against method changes. Finally, we explore how the input time series affect the results. For example, we investigate the effect of having only partial temporal information in the networks, i.e. not for all edges, which is a common situation in biology. Finally, we show that gene expression data from RNA-seq, widely available for many biological systems, can be used too. This method can be used to further our understanding of the multiscale temporal organisation of many biological networks.
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