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‘Executable Disease Networks: Reconstruction, Topology, Dynamics’

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If you have a question about this talk, please contact Mala Jayasundera.

Host: Ben Hall

ABSTRACT: Biological processes rely on the concerted interactions and regulations of thousands of molecules that form complex molecular and signalling networks. The analysis of their structure and organization can reveal interesting topological properties that shed light onto the basic mechanisms that control normal cellular processes. Disruption and dysregulation of these networks can lead to disease. Therefore, the mapping and accurate representation of pathways implicated, is a primary but essential step for elucidating the mechanisms underlying disease pathogenesis. Disease maps have been an emerging concept as a useful and intuitive way of describing disease mechanisms in a systematic fashion. Based on information mining, human curation and experts’ advice, they summarize current biological pathway knowledge in a standard, comprehensive representation that is both human and machine readable. Disease maps can serve as templates for visualization and analysis of omic datasets, or they can be analysed in terms of their underlying network structure. However, their static nature provides relatively limited understanding concerning the emerging behaviour of the system under different conditions. Computational modelling can reveal dynamical properties of the network by in silico simulations and perturbations and can be further used for hypotheses testing and predictions.

In this talk I will present our efforts to establish an automated pipeline starting from a fully detailed Disease map and its analysis as a complex network, all the way to the automated inference of a dynamical (Boolean) model, based on network topology and semantics, creating thus “executable” disease networks. I will use Rheumatoid Arthritis as case study.

I will also talk briefly about our efforts to couple signalling networks based on prior knowledge with data-driven co-regulatory networks inferred from transcriptomic datasets in order to find synthetically lethal partners of integrin antagonists, in the case of Glioblastoma.

This talk is part of the Cambridge Oncology Seminar Series series.

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