Project #11221
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#11221 : State and parameter inference for stochastic models of gene expression
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Name of Applicant : Jakob Ruess
Date of application : 15-03-2018
Unit : Other
Location : Building 8B
Phone : 9583
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Project context and summary :

Quantitatively understanding the stochastic dynamics of gene expression requires measurements at the level of single cells. A common approach to follow the expression of genes in single cells and in real time is to make use of fluorescent reporter proteins and to record the cells’ fluorescence by microscopy. However, this provides only an indirect readout of the biological processes that are of interest such as the regulation mechanisms at the promoter. A possible way to uncover the unobservable biological processes is to infer the hidden dynamics from the available data through the use of mechanistic models of gene expression. The goal of this project is to develop methods for state estimation and parameter inference for such models and to test these methods on real data.

Related team publications :
Chait et al. (2017), Shaping bacterial population behavior through computer-interfaced control of individual cells, Nature Communications 8, 1535.
Lugagne et al. (2017), Balancing a genetic toggle switch by real-time feedback control and periodic forcing, Nature Communications 8, 1671.
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Project Type : Long
Status : Pending

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