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Quantitative network-level analysis of stochastic cell fate decisions (STOCHCELLFATE)
Date du début: 1 oct. 2013, Date de fin: 30 sept. 2018 PROJET  TERMINÉ 

"In a developing embryo, each cell has to assume the correct cell fate to yield a viable adult organism. It has become clear that the signaling networks that control development are heavily influenced by molecular noise, i.e. stochastic fluctuations on the molecular level. Intriguingly, in many organisms cell fates are assigned in a stochastic manner. Such stochastic cell fate decisions are thought to exploit molecular noise, using positive feedback loops to amplify noise into an all-or-nothing cell fate decision. Yet, how a stochastic process results in robust cell fate assignment without errors is poorly understood.To address this question, we propose to study stochastic cell fate decisions in the nematode C. elegans using a new quantitative, physics-inspired approach. To this end we will use two innovative techniques to quantify the stochastic dynamics of the signaling networks controlling the cell fate decisions. First, we will use single molecule Fluorescence In Situ Hybridization to measure gene expression with unparalleled single-mRNA resolution. Secondly, we will construct a novel microfluidic setup that for the first time will allow high-throughput timelapse microscopy of developmental processes in live C. elegans animals at the single cell level.Using these techniques in combination with mathematical modeling, we will elucidate the core features of the signaling network essential for robust stochastic cell fate assignment. We will apply this approach to study two stochastic cell fate decisions occurring in gonad development: 1) the so-called AC/VU decision, a classical stochastic cell fate decision that is well-characterized on the molecular level, allowing us to fully focus on how feedback loops reliably amplify noise into all-or-none cell fate decisions. 2) Stochastic vulva precursor cell specification. In this relatively underexplored system, we will apply our insight gained in studying the AC/VU decision to identify the essential feedback loops."