Biological systems are constantly changing, and their networks of interactions evolve over time. One example is the cell cycle, where proteins interact in specific ways at different stages.
My work looks at how these temporal interaction networks can be used to infer the phases of the cell cycle. By studying the timing and structure of protein–protein interactions, I aim to uncover how dynamic networks reflect underlying biological processes.
References
Phasik: a Python package to identify system states in partially temporal networks
M. Lucas, A. Townsend-Teague, M. Neri, S. Poetto, A. Morris, B. H. Habermann, and L. Tichit
Phasik is a Python library for analyzing the temporal structure of temporal and partially temporal networks. Temporal networks are used to model complex systems that consist of entities with time-varying interactions. This library provides methods for building temporal networks (including from data), visualizing them, and analyzing their structure. In particular, Phasik focuses on the identification of temporal phases, that is, periods of time during which the system is in a given state. The library supports partially temporal networks for which information about only a subset of the edges’ temporal evolution is available. Phasik is implemented in pure Python and integrates with the rest of the Python scientific stack.
@article{lucas2023phasik,doi={10.21105/joss.05872},year={2023},publisher={The Open Journal},volume={8},number={91},pages={5872},author={Lucas, M. and Townsend-Teague, A. and Neri, M. and Poetto, S. and Morris, A. and Habermann, B. H. and Tichit, L.},title={Phasik: a Python package to identify system states in partially temporal networks},journal={Journal of Open Source Software},}
Inferring cell cycle phases from a partially temporal network of protein interactions
M. Lucas, A. Morris, A. Townsend-Teague, L. Tichit, B. H. Habermann, and A. Barrat
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.
@article{lucas2023inferring,title={Inferring cell cycle phases from a partially temporal network of protein interactions},author={Lucas, M. and Morris, A. and Townsend-Teague, A. and Tichit, L. and Habermann, B. H. and Barrat, A.},year={2023},journal={Cell Reports Methods},pages={100397},biorxiv={10.1101/2021.03.26.437187v1},doi={https://doi.org/10.1016/j.crmeth.2023.100397},}