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Reverse Engineering Gene Regulatory Networks

Gene regulatory networks (GRNs) are an important cellular signal processing mechanism for translating input signals into appropriate phenotypes by modulating expression of the genome. The quantitative details of how cells process... ver más

30/09/2022

EPFL

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

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

TRL 4-5

Ver 2 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2022-09-30

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

ERC-2016-COG: ERC Consolidator Grant

I+D

Cerrada hace 8 años

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2 Participantes

EPFL

1.99M€ | Lider

PLASFUR

282.30K€ | Participante

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Últimas noticias

29-11-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

29-11-2024: ECE En las últimas 48 horas el Organismo ECE ha otorgado 2 concesiones

29-11-2024: CMVAMADRID En las últimas 48 horas el Organismo CMVAMADRID ha otorgado 37 concesiones

Duración del proyecto: 69 meses Fecha Inicio: 2016-12-15
Fecha Fin: 2022-09-30

Líder del proyecto

ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE

TRL 4-5

Presupuesto del proyecto 2M€

Descripción del proyecto Gene regulatory networks (GRNs) are an important cellular signal processing mechanism for translating input signals into appropriate phenotypes by modulating expression of the genome. The quantitative details of how cells process information through GRNs are still poorly understood, but of central importance in a large number of biological processes. Considerable progress has been made in mapping the topology of GRNs and more recently in deciphering the relationship between promoter sequence and function. Nonetheless, it is not yet possible to computationally predict the output of most native promoters, nor is it trivial to build promoters that integrate signals in a novel and predictive manner. Developing a quantitative understanding of transcriptional regulation, ultimately leading to the ability to predict entire GRNs will be a significant achievement and a prerequisite for our ability to engineer biological systems. I propose a multi-disciplinary approach incorporating biology, engineering, and computational modelling to improve our quantitative understanding by reverse engineering GRNs in S. cerevisiae. My research group has developed a powerful set of unique, high-throughput microfluidic technologies that enable the quantitative analysis of GRNs in vitro and in vivo. Specifically I propose to quantitatively investigate the yeast phosphate regulatory network and to develop a master model capable of predicting output of the network under various inorganic phosphate concentrations, to develop novel approaches for modulating GRNs using engineered Zn-finger transcription factors (TF) and CRISPR/Cas, to link GRN output to fitness in order to develop an understanding of how networks are optimized and evolve, and to reverse engineer an exact functional copy of the native phosphate regulatory network with orthogonal components.

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