Laureates of the QBI-Qlife joint call 2020
Congratulations to all of them!
Following our inaugural joint symposium with the Quantitative Biosciences Institute (QBI, UCSF) in September 2019, we decided to push our collaboration further by launching a joint call which would allow to trigger fruitful collaborations between teams from both institutes. Each chosen project received a total of 150K (75K per teams) for a duration of 2 years.
- 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.