Summary
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.
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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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/723106 |
| Start date: | 01-04-2017 |
| End date: | 30-09-2022 |
| Total budget - Public funding: | 1 993 858,00 Euro - 1 993 858,00 Euro |
Cordis data
Original description
Gene regulatory networks (GRNs) are an important cellular signal processing mechanism for translating input signals intoappropriate 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.
Status
CLOSEDCall topic
ERC-2016-COGUpdate Date
27-04-2024
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