Lauréats des projets joints entre le QBI et Qlife 2020

Suite à notre symposium inaugural joint avec l'Institut des Biosciences Quantitatives (QBI, UCSF) en septembre 2019, nous avons décidé de poursuivre notre collaboration en lançant un appel joint qui permettrait d’entraîner des collaborations fructueuses entre les équipes des deux instituts. Chaque projet choisi a reçu un total de 150K (75K par équipe) pour une durée de 2 ans.

Projets financés

• Judy Sakanari (UCSF, QBI), Makedonka Mitreva ( WUStl) and Gilles Gasser (Chimie ParisTech, Qlife) - Novel bioorganometallic drugs to treat neglected tropical diseases | This project aims to identify drugs with novel modes of actions to treat two major neglected tropical diseases: river blindness and elephantiasis. These parasitic infections result in permanent visual impairment and gross swelling of the legs and arms, respectively and affect over 145 million people worldwide.

• Steven Altschuler (UCSF, QBI), Lani Wu (UCSF, QBI) and Auguste Genovesio (ENS, Qlife) - Deciphering spatial patterning in the gut epithelium by computational tissue shuffling | In this project, we will model spatial patterning in gut epithelium to decipher coordination in cell-type composition. To this aim, we compare high-content imaging of enteroid monolayers with computational models that reconstruct alternative, realistic epithelial patterns from the same tissues.

•  Shaeri Mukherjee (UCSF, QBI) and Bruno Goud (Institut Curie, Qlife) - An investigation into how SARS-CoV-2 hijacks Rab GTPase function to promote virion assembly and exocytosis | The proposal aims at understanding how SARS-COV-2, the causative agent of COVID-19, co-opts the secretory pathway to assemble and egress out of infected cells. It focuses on Rab GTPases, master regulators of intracellular transport pathways shown to be manipulated by several pathogens.

•  Orion Weiner (UCSF, QBI) and Hervé Turlier (Collège de France, Qlife) - Decoding cell migration with closed-loop optogenetic control of cell mechanics | To disentangle the forces and signals that govern the regulation of cell shape during migration, we will build a unique feedback control system for real-time inference and control of cell mechanics combining spatiotemporal optogenetics and physics-informed deep-learning.