Network Signature Techniques for Drug Discovery in Pulmonary Fibrosis
Evangelos Karatzas, PhD Presentation
Wed. May 6th, 2020, 14:00-15:30, UoA e-presence & twitch.tv
Abstract: Fibrotic diseases cover a spectrum of systemic and organ specific diseases that affect a large portion of the population, currently without cure. Idiopathic Pulmonary Fibrosis (IPF) is an interstitial lung disease as well as one of the most common and studied fibrotic diseases which still remains an active research target. Understanding the underlying biological mechanisms and respective interactions of a disease remains an elusive, time consuming and very costly task. Computational methodologies that propose pathway communities and reveal respective relationships can be of great value as they can may help expedite the process of identifying how perturbations in a single pathway can affect other pathways. Drug Repurposing or Drug Repositioning (DR) is a methodology where already existing drugs are tested against diseases outside their initial spectrum to reduce the high cost and long periods of new drug development.
Our objectives in this thesis are: (i) identify key differentially expressed genes of fibrotic diseases, (ii) explore the respective perturbed biological pathways and (iii) suggest repurposed drugs as potential anti-fibrotic candidates for further testing and (iv) identify which fibrotic diseases resemble IPF based on common terms, to potentially pursue common regimens.
We analyze transcriptomics datasets containing fibrotic and normal samples to identify key genes that are implicated in fibrotic diseases. We use these genes as input in DR tools and then propose a novel drug re-ranking methodology via a scoring formula that consolidates standard repurposing scores with structural, functional and side effect scores. Following, we present a pathway analysis and community detection methodology, based on random walks, where a walker crosses a pathway-to-pathway network under the guidance of a disease-related map. The latter is a gene-network that we construct by integrating multi-source information regarding a specific disease. The most frequent trajectories highlight communities of pathways that are expected to be strongly related to the disease under study. By applying our pathway analysis methodology on 9 different fibrotic maladies, we identify various common highlighted pathways as well as unique entries for some of the diseases.
---- Postscript ----
Due to Covid-19 restrictions, this PhD defense will be held remotely through e-presence for the doctoral panel members and will be live-streamed at https://www.twitch.tv/vag_karatzas
According to University of Athens bylaws and practice, this doctoral presentation is open to the public. In the above twitch URL, the event can be viewed in real-time and members of the audience can ask questions through chat. Please, feel free to share this invitation with colleagues/parties who may be interested.
26/04/2020
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