DYNACLOCK
Dynamic protein-DNA interactomes and circadian transcription regulatory networks in mammals
| Coordinatore |
Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie. |
| Nazionalità Coordinatore | Non specificata |
| Totale costo | 1˙499˙999 € |
| EC contributo | 14 € |
| Programma | FP7-IDEAS-ERC
Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) |
| Anno di inizio | 2011 |
| Periodo (anno-mese-giorno) | 2011-03-01 - 2016-02-29 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Organization address
address: BATIMENT CE 3316 STATION 1 contact info |
CH (LAUSANNE) | hostInstitution | 1˙499˙999.90 |
| 2 |
ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Organization address
address: BATIMENT CE 3316 STATION 1 contact info |
CH (LAUSANNE) | hostInstitution | 1˙499˙999.90 |
Mappa
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Obiettivo del progetto (Objective)
'The aim of this project is to understand the dynamics of protein-DNA interactomes underlying circadian oscillators in mammals, and how these shape circadian transcriptional output programs. Specifically our goal is to solve a fundamental issue in circadian biology: the phase specificity problem underlying circadian gene expression. We have taken a challenging and original multi-disciplinary approach in which molecular biology experiments will be tightly interlinked with computational analyses and biophysical modeling. The approach will generate time resolved protein-DNA interactomes in mouse liver for several key circadian repressors at unprecedented resolution. These experiments will be complemented with chromosome conformation capture (3C) experiments to monitor how looping interactions and 3D genome structure rearrange during the circadian cycle, which will inform on how circadian transcription networks use long-range gene regulatory mechanisms. Novel computational algorithms based on biophysical principles will be developed and implemented to optimally analyze interactome and 3C datasets. For the latter, statistical models from polymer physics will be used to reconstruct the chromatin networks and interaction maps from the 3C data. At the detailed level of individual cells, we will investigate transcription bursts, and how those are involved in the control of circadian gene expression. In particular we will exploit high temporal resolution bioluminescence reporters using a biophysical model of transcription coupled with a Hidden Markov Model (HMM). Through our innovative approach, we expect that the data generated and state-of-the-art analyses performed will lead novel insight into the role and mechanics of circadian transcription in controlling circadian outputs in mammals.'