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

View ORCID ProfileMaxime Lucas, Arthur Morris, Alex Townsend-Teague, View ORCID ProfileLaurent Tichit, View ORCID ProfileBianca H. Habermann, View ORCID ProfileAlain Barrat
doi: https://doi.org/10.1101/2021.03.26.437187
Maxime Lucas
1Aix Marseille Univ, CNRS, I2M, Turing Center for Living Systems, Marseille France
3Aix Marseille Univ, CNRS, IBDM UMR 7288, Turing Center for Living Systems, Marseille France
4Aix Marseille Univ, CNRS, CPT, Turing Center for Living Systems, Marseille France
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  • For correspondence: bianca.habermann@univ-amu.fr
Arthur Morris
2Oxford University, Oxford, UK
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Alex Townsend-Teague
2Oxford University, Oxford, UK
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Laurent Tichit
1Aix Marseille Univ, CNRS, I2M, Turing Center for Living Systems, Marseille France
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  • For correspondence: bianca.habermann@univ-amu.fr
Bianca H. Habermann
3Aix Marseille Univ, CNRS, IBDM UMR 7288, Turing Center for Living Systems, Marseille France
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  • For correspondence: bianca.habermann@univ-amu.fr
Alain Barrat
4Aix Marseille Univ, CNRS, CPT, Turing Center for Living Systems, Marseille France
5Tokyo Tech World Research Hub Initiative (WRHI), Tokyo Institute of Technology, Tokyo, Japan
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  • For correspondence: bianca.habermann@univ-amu.fr
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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. Our results show for the first time that a temporal network with only partial temporal information, i.e. for some of the PPIs, is sufficient to infer the temporal organization of a system. 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.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • We have revised the abstract of our article

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted April 01, 2021.
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Inferring cell cycle phases from a partially temporal network of protein interactions
Maxime Lucas, Arthur Morris, Alex Townsend-Teague, Laurent Tichit, Bianca H. Habermann, Alain Barrat
bioRxiv 2021.03.26.437187; doi: https://doi.org/10.1101/2021.03.26.437187
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Inferring cell cycle phases from a partially temporal network of protein interactions
Maxime Lucas, Arthur Morris, Alex Townsend-Teague, Laurent Tichit, Bianca H. Habermann, Alain Barrat
bioRxiv 2021.03.26.437187; doi: https://doi.org/10.1101/2021.03.26.437187

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