Networks and Perturbations

Interactions among molecules, cells, and individuals determine the properties and the behavior of a biological system. These interactions give rise to various biological networks and their mathematical representations as graphs. We use biological networks for integrating different data types. For example, cancer cells can be profiled for their DNA, RNA, and protein composition, and we use gene regulatory networks and protein-​protein interaction networks to combine the evidence from all molecular measurements.

Enlarged view: Integration of multiple biological networks.
Integration of multiple biological networks for gene prioritisation [from doi:10.1515/sagmb-2018-0033]

Molecular changes, such as mutations, can be regarded as perturbations of a biological network. The connectivity of the network is used to predict the effect of the perturbation on the system. We employ graph diffusion techniques to estimate the system-​level effect of individual patterns of perturbations, such as mutational profiles or differentially expressed genes of tumor cells.

Enlarged view: Nested effects models can be used to learn signalling pathways
A mixture of nested effects models to learn signalling pathways [from doi:10.1093/bioinformatics/bty602]

Network perturbations also reveal information on the structure of the underlying network, and we build computational methods for learning networks from perturbation data. For example, we have developed several nested effects models for reconstructing intracellular signaling networks from RNA interference and CRISPR-Cas9 screening data.

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