Paul Villoutreix (LIS)

Learning Meaningful Representations of Life (QARMA Team)

Background

February 2019 - present | CENTURI group leader

2017 - 2018 | Postdoctoral fellow, Weizmann Institute of Science (Israel)

2015 - 2017 | Postdoctoral fellow, Princeton University (USA)

2011 - 2015 | PhD, Mathematical Biology, Paris Descartes University

2010 - 2011 | MSc degree in Interdisciplinary Approaches to Life Sciences (AIV), Paris Diderot University (Paris VII - CRI)

2008 - 2011 | Supélec Engineering School Degree (MSc)

Awards

2017 |  Kantar Information is Beautiful Awards – Showcased

2017 |  Mozilla Science MiniGrant

2017 |  Princeton Project X Fund

2015 |  International Systems Biology Scholars Program – 2 years postdoc fellowship between Princeton University and the Weizmann Institute of Science

2015 |  Gordon Research Conference Stochastic physics in biology – Best poster award

2013 |  France Stanford Center for Interdisciplinary Studies – Visiting student researcher fellowship recipient – One semester

2011 |  Complex Systems Institute 3 years PhD fellowship – Grant recipient

2011 |  Eleuthère Mascart medal – Achievement award for Supélec Diploma

Location

Luminy campus, Marseille (France)

About his research

Multicellular organisms develop from a single fertilized egg. The sequence of events leading to the precise positioning of individual cells with the required fate is orchestrated by gene regulation within and between cells, cell proliferation and rearrangements as well as global morphological changes. It is a multi-scale process involving dynamics from the single molecule to the cell, to the tissue, to the organ, to the entire organism and a temporal process in which the entire sequence of events is linked hierarchically through the cell lineage. In recent years, many measurement methods have been developed, each of which bringing out partial features of the process. They mainly fall into two categories, on one hand, microscopy techniques, which generate large microscopy imaging datasets and give access to spatio-temporal information of entire developing embryos with a limited number of variables that can be measured at the same time. On the other hand, single cell multi-omics methods, such as single cell RNAseq, generate large datasets giving access to exhaustive molecular information at a given time step within each of the cell of a tissue or an entire embryo without spatial information. To understand embryonic development in all its complexity and establish predictive multi-scale models, we need to integrate those multiple sources of data. Given the size of these datasets that far exceeds what a human expert can process, we need to use the tools of artificial intelligence and machine learning.

The main question underlying our research project concerns the role of the shape of an embryo in regulating the differentiation trajectories of cells. The potential number of molecular interactions, spatial patterns of gene expression and cell and tissue organization far outnumbers the number of samples that we can possibly measure; therefore we need to look for structures that reduce this combinatorial complexity. The spatial ordering of cells within an embryo, as well as the temporal ordering of cells within a cell lineage tree can be harnessed as core structures upon which we can organize other measurements and models.

Team members

Open position

PhD position 3 open positions

Postdoctoral fellow position To be announced

 
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