Head of the Group

Dr.-Ing. Steffen Klamt
Dr.-Ing. Steffen Klamt
Phone: +49 391 6110 480
Fax: +49 391 6110 509


Anke Goettert
Phone: +49 391 6110 477
Fax: +49 391 6110 452

News / Latest Publications

07.12.2017: New Publication
Harder B-J, Bettenbrock K, Klamt S (2018) Temperature-dependent dynamic control of the TCA cycle increases volumetric productivity of itaconic acid production by Escherichia coli. Biotechnology and Bioengineering 115: 156-164.

05.10.2017: New Publication         
von Kamp A, Thiele S, Hädicke O, Klamt S (2017) Use of CellNetAnalyzer in biotechnology and metabolic engineering. Journal of Biotechnology 261: 221-228.

22.08.2017: News
Press release to a recently published paper of our group.

04.07.2017: New Publication       
von Kamp A, Klamt S. (2017) Growth-Coupled overproduction is feasible for almost all metabolites in five major production organisms. Nature Communications 8: 15956.

ERC Consolidator Grant

                ERC Consolidator Grant Project “StrainBooster”

Enforced ATP Wasting as a General Design Principle
to Rationally Engineer Microbial Cell Factories


Grant awarded to (project leader):
Steffen Klamt

Project duration:
1 May 2017 – 30 April 2022

1,998,750.00 €

Link to project entry in the CORDIS database of the EU.

Press release (in German):
See here.


One global challenge of humanity in the 21st century is the shift from a petrochemical to a bio-based production of chemicals and fuels. An enabling technology towards this goal is metabolic engineering which uses computational and experimental methods to construct microbial cell factories with desired properties. While it has been shown that genetically engineered microorganisms can, in principle, produce a broad range of chemicals, novel approaches to improve the performance of those strains are urgently needed to develop economically viable bioprocesses.

To this end, we propose a new metabolic design principle to rationally engineer cell factories with high performance. Supported by a recent pilot study, we postulate that suitable genetic interventions combined with mechanisms that burn (waste) an extra amount of ATP (e.g., by artificial futile cycles) will increase product yield and productivity of many microbial production strains. Key objectives of StrainBooster are therefore:

  1. to use computational techniques and constraint-based metabolic models to identify gene knockout strategies whose coupling with ATP wasting mechanisms can boost the performance of microbial strains and to prove in silico that those strategies exist for many combinations of substrates, products, and host organisms;
  2. to develop genetic modules that can robustly increase ATP dissipation in the cell;
  3. to experimentally demonstrate the power of the proposed strategy for selected production processes with Escherichia coli.

To reach these ambitious goals, an interdisciplinary approach will be pursued combining theoretical and experimental studies and making use of innovative methods from systems and synthetic biology.

If successful, StrainBooster will not only establish a new and ground-breaking strategy for metabolic engineering, it will also deliver novel computational tools and genetic parts facilitating direct application of the approach to design and optimize industrial fermentation processes.

Project Structure

StrainBooster will follow a highly interdisciplinary approach making use of innovative methods from systems and synthetic biology. The project consists of four (two theoretical and two experimental) interacting work packages (see Figure below). Whereas the modeling WP will analyze feasibility of the StrainBooster approach for several relevant production (micro-)organisms, experi­men­tal work will focus on E. coli, which represents one major host for biotechnological applications and is routinely studied in our lab.

Related publications of our group

  • von Kamp A, Klamt S(2017) Growth-coupled overproduction is feasible for almost all metabolites in five major production organisms. Nature Communications 8: 15956. Open Access
  • Klamt S, Regensburger G, Gerstl MP, Jungreuthmayer C, Schuster S, Mahadevan R, Zanghellini J, Müller S. (2017) From elementary flux modes to elementary flux vectors: Metabolic pathway analysis with arbitrary linear flux constraints. PLoS Comput Biol 13: e1005409. Open Access
  • Hädicke O, Klamt S (2017) EColiCore2: a reference model of the central metabolism of Escherichia coli and the relationships to its genome-scale parent model. Scientific Reports 7:39647. Open Access
  • Harder B-J, Bettenbrock K, Klamt S (2016) Model-Based metabolic engineering enables high yield itaconic acid production by Escherichia coli. Metabolic Engineering 38:29-37. PubMed
  • Hädicke O, Klamt S (2015) Manipulation of the ATP pool as a tool for metabolic engineering. Biochemical Society Transactions 43:1140-1145. PubMed
  • Hädicke O, Bettenbrock K, Klamt S (2015) Enforced ATP futile cycling increases specific productivity and yield of anaerobic lactate production in Escherichia coli. Biotechnology & Bioengineering 112:2195-2199. PubMed
  • Mahadevan R, von Kamp A, Klamt S (2015) Genome-scale strain designs based on regulatory minimal cut sets. Bioinformatics 31:2844-2851. PubMed
  • Erdrich P, Steuer R, Klamt S (2015) An algorithm for the reduction of genome-scale metabolic network models to meaningful core models.  BMC Systems Biology 9:48. Open Access
  • Klamt S, Mahadevan R (2015) On the feasibility of growth-coupled product synthesis in microbial strains. Metabolic Engineering 30:166-178. PubMed
  • Klamt S, Hädicke O, von Kamp A (2014) Stoichiometric and Constraint-Based Analysis of Biochemical Reaction Networks. Large-Scale Networks in Engineering and Life Sciences. Edited by Benner P, Findeisen R, Flockerzi D, Reichl U and Sundmacher K, Springer, pp.263-316. Springer
  • Erdrich P, Steuer R, Knoop H, Klamt S (2014) Cyanobacterial biofuels: new insights and strain design strategies revealed by computational modeling. Microbial Cell Factories 13:128. Open Access
  • von Kamp A, Klamt S (2014) Enumeration of smallest intervention strategies in genome-scale metabolic networks. PLoS Computational Biology 10(1):e1003378. Open Access
loading content