Laureates of the QBI-Qlife joint call 2020

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.

Funded projects

• 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.