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[PhD public defense] Inference of gene networks from time series expression data and application to type 1 Diabetes

Public defense of the PhD thesis of Miguel Lopes entitled "Inference of gene networks from time series expression data and application to type 1 Diabetes". The defence will take place at the Université libre de Bruxelles (in the La Plaine, Forum G) on Friday, September 4, at 4PM. Abstract: The inference of gene regulatory networks (GRN) is of great importance to medical research, as causal mechanisms responsible for phenotypes are unravelled and potential therapeutical targets identified. In type 1 diabetes, insulin producing pancreatic beta-cells are the target of an auto-immune attack leading to apoptosis (cell suicide). Although key genes and regulations have been identified, a precise characterization of the process leading to beta-cell apoptosis has not been achieved yet. The inference of relevant molecular pathways in type 1 diabetes is then a crucial research topic. GRN inference from gene expression data may be tackled with well-established statistical and machine learning tools. In particular, the use of time series facilitates the identification of the causal direction in cause-effect gene pairs. GRN inference is a very challenging task due to the very high number of genes and typical low number of available samples. The first part of this presentation will present novel heuristics to GRN inference from time series, designed to deal with the high variable to sample ratio. State of the art approaches are described and assessed in real and simulated data. The second part of the presentation is on the context of type 1 diabetes, and consists of a study on beta cell gene expression after exposure to cytokines, emulating the mechanisms leading to apoptosis. Multiple datasets of beta cell gene expression were used to identify differentially expressed genes, and a regulatory network involving them was inferred. Top differentially expressed genes were found to modulate cytokine induced apoptosis and predicted regulations were experimentally confirmed.

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