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DYNACLOCK

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Cerrado

Dynamic protein DNA interactomes and circadian transcription regulatory networks...

Dynamic protein DNA interactomes and circadian transcription regulatory networks in mammals 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 fund... ver más

29/02/2016

EPFL

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2016-02-29 No tenemos la información de la convocatoria

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Información adicional privada

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1 Participantes

EPFL

0.00€ | Lider

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Líder del proyecto

ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 1M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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.

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