At this week’s group meeting I presented on my second SABS short project, which is supervised by Charlotte Deane, Mason Porter, and Jonny Wray from e-Therapeutics. It has the title “Multilayer-Network Analysis of Protein Interaction Networks”.
Protein interactions can be represented using networks. Accordingly, approaches that have been developed in network science are appropriate for the analysis of protein interactions, and they can lead to the detection of new drug targets. Thus far, only ordinary (“monolayer”) protein interaction networks have been exploited for drug discovery. However, because “multilayer networks” allow the representation of multiple types of interactions and of time-dependent interactions, they have the potential to improve insight from network-based approaches [1].
Aim of my project was to employ known multilayer methods on well-established data to investigate potential use cases of multilayer protein interaction networks. We focussed on various community detection methods [3,4] to find groups of proteins as candidates of functional, biological modules. Additionally, temporal centrality [5] measures were used to identify important proteins across time.

[1] Kivelä, Mikko, et al. “Multilayer networks.” Journal of Complex Networks (2014) [2] Calvano, Steve E., et al. “A network-based analysis of systemic inflammation in humans.” Nature (2005) [3] Peixoto, Tiago P. “Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models.” PRE (2014) [4] Mucha, Peter J., et al. “Community structure in time-dependent, multiscale, and multiplex networks.” Science (2010) [5] Taylor, Dane, et al. “Eigenvector-Based Centrality Measures for Temporal Networks.” arXiv preprint (2015).

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