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Transcription Factor Dynamics in Living Cells at the Single Molecule Level (TF dynamics in vivo)
Start date: 01 Jul 2008, End date: 30 Jun 2013 PROJECT  FINISHED 

Progress in bioengineering and biomedicine is limited by our inadequate understanding of genetic control systems in living cells. The lack of methods for studying kinetics and gene regulation in single cells seriously impairs our prospects to gain deeper insight to develop better quantitative models of such control systems. This project is focused on transcription factors (TFs), proteins that mediate gene regulation in all kingdoms of life. It aims at understanding how bacterial TFs coordinate the expression of genes at the level of single cells. The experimental challenge of studying TF mediated gene regulation directly is that it is a single molecule process where one or a few TF molecules bind one or a few binding sites on the bacterial chromosome. In addition studying TF kinetics poses two major theoretical challenges: its non-negligible spatial aspects and the stochastic nature of kinetics at the single molecule level. This proposal describes new state-of-the-art single molecule microscopy methods for studying kinetics and diffusion of TFs in living cells. The proposed experimental techniques will be accompanied by pioneering computational methods for stochastic reaction-diffusion modeling of intracellular kinetics. Only by the concomitant advancement of both methodologies will we gain understanding of how transcription factors operate in living cells, how their copy number is maintained, how different classes of TFs optimize their search for chromosomal targets, and how the location of TF genes and binding sites constrain genome evolution. Direct observation of TF dynamics will allow probing gene regulation with unprecedented time resolution. This makes it possible to test hypotheses about coordinated gene regulation which have so far been experimentally inaccessible. The unique combination of single molecule in vivo microscopy and spatially resolved stochastic modeling will advance Europe’s position at the frontier of systems biology.
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