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MapMe SIGNED

Mapping metabolic regulators at a genome-scale to switch bacteria from growth to overproduction of chemicals

Total Cost €

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EC-Contrib. €

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Partnership

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Project "MapMe" data sheet

The following table provides information about the project.

Coordinator
MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV 

Organization address
address: HOFGARTENSTRASSE 8
city: Munich
postcode: 80539
website: www.mpg.de

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Germany [DE]
 Total cost 1˙461˙200 €
 EC max contribution 1˙461˙200 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2016-STG
 Funding Scheme ERC-STG
 Starting year 2017
 Duration (year-month-day) from 2017-01-01   to  2021-12-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV DE (Munich) coordinator 1˙461˙200.00

Map

 Project objective

Metabolic engineering creates improved microbes for industrial biotechnology. Rational design of industrial microbes centres on modifications of genes with known roles in the production pathway of interest. However, genes that are unrelated to the production pathway are also known to substantially impact productivity. To date there are no methods that allow the prediction of such distal genes on a rational basis. Effects of distal genes are indirect, and mediated through regulatory interactions between metabolites and proteins, most of which are currently unknown even in the well-studied microbe Escherichia coli. The lack of knowledge of metabolite-protein interactions thus effectively prohibits systematic exploration of distal regulatory relationships, with the consequence that models used to predict metabolic engineering targets are severely limited and rarely applied in industrial biotechnology. Previously, we used small-scale metabolic models and metabolomics to infer metabolite-protein interactions in specific pathways. In this project we propose a novel genome-wide endeavor to map metabolite-protein interactions across the entire metabolic network. For this purpose, we will use the recently developed CRISPR interference system to quantitatively perturb >900 single metabolic genes in E. coli. Combining the metabolomics data of these >900 gene perturbations with a genome-scale metabolic model will enable us to infer functionally relevant metabolite-protein interactions. Finally, we will apply this knowledge to the model-guided metabolic engineering of superior E. coli strains. Specifically, we want E. coli to cease growth upon induction and focus all its metabolic resources towards synthesis of succinic acid. This controlled uncoupling of growth from overproduction on a rational basis will break new grounds in metabolic engineering and opens up novel applications in industrial biotechnology.

 Publications

year authors and title journal last update
List of publications.
2019 Simon Boecker, Ahmed Zahoor, Thorben Schramm, Hannes Link, Steffen Klamt
Broadening the Scope of Enforced ATP Wasting as a Tool for Metabolic Engineering in Escherichia coli
published pages: 1800438, ISSN: 1860-6768, DOI: 10.1002/biot.201800438
Biotechnology Journal 2019-08-29
2019 Timur Sander, Niklas Farke, Christoph Diehl, Michelle Kuntz, Timo Glatter, Hannes Link
Allosteric Feedback Inhibition Enables Robust Amino Acid Biosynthesis in E. coli by Enforcing Enzyme Overabundance
published pages: 66-75.e8, ISSN: 2405-4712, DOI: 10.1016/j.cels.2018.12.005
Cell Systems 8/1 2019-08-29

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