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Lionel Blanchet

WP1: Iterative modeling, prediction and data integration of energy metabolism in skeletal muscle cell mitochondria

AL5: Omics type bioinformatics on the experimental data of WP2 and WP3 and of public domain data, to elucidate the key variables related to the different mitochondrial states in mouse and human and to predict biomarkers.

WP2: Measuring mitochondrial function at multiple levels of complexity

AL2: High-content microscopy analysis

This project focuses on the analysis of large data sets such as the one produced by omics and imaging methods. Unsupervised and supervised algorithms are developed in order to extract all information related to mitochondrial diseases. The results of such approaches should lead to the definition of novel biomarkers of mitochondrial dysfunctions.

Personal Profile:

Lionel received his engineering master degree (diplôme d’ingénieur) in Scientific Instrumentation from the Ecole Polytechnique Universitaire de Lille and his master degree in Advanced Analysis in 2005 from the Université des Sciences et Technologies de Lille. He obtained in 2008 his Ph.D. from the Universitat de Barcelona and the Université des Sciences et Technologies de Lille. Lionel participated as post doctoral researcher in the Radboud University Nijmegen to the project eTumour and to TI Pharma before joining the CSBB. His current research interests concerns 1.  the implementation of hyperspectral  analysis in Live Cell Imaging. The objective is to unmix spectrally overlapping signals of multiples fluorescent probes within the same cell. 2. The development of machine learning methods able to analyze in a high throughput fashion cellular and subcellular images. 3. The application of machine learning methods to define biomarkers based on omics data.

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