Head of the Group

Dr.-Ing. Steffen Klamt
Dr.-Ing. Steffen Klamt
Phone: +49 391 6110 480
Fax: +49 391 6110 509
Room: S2.10


Susanne Hintsch
Phone:+49 391 6110-477Fax:+49 391 6110-452

News / Latest Publications

28.08.2018: New Publication
Kyselova L, Kreitmayer D, Kremling A & Bettenbrock K (2018) Type and capacity of glucose transport influences succinate yield in two-stage cultivations. Microbial Cell Factories, 17: 132.

10.08.2018: New Publication
Mahour R, Klapproth J, Rexer TFT, Schildbach A, Klamt S, Pietzsch M, Rapp E, Reichl U (2018) Establishment of a five-enzyme cell-free cascade for the synthesis of uridine diphosphate N-acetylglucosamine. J Biotechnol. 283:120-129.

04.07.2018: Regina defended her PhD thesis.

01.06.2018: New Publication
Bosch J, Klamt S, Stoll M (2018) Generalizing Diffuse Interface Methods on Graphs: Nonsmooth Potentials and Hypergraphs. SIAM J. Appl. Math. 78(3): 1350–1377.

28.04.2018: New Publication
Nitzschke A, Bettenbrock K (2018) All three quinone species play distinct roles in ensuring optimal growth under aerobic and fermentative conditions in E. coli K12. PLoS ONE 13(4): e0194699.


Metabolic Engineering and Targeted Modification of Cellular Networks

We use mathematical models and computational methods to identify intervention strategies for rational (re)design of biological networks. Targeted modification of cellular networks has far-reaching applications in biotechnology and medicine and we intend to develop, test, and apply new algorithms and techniques.

One particular focus of our research is computational methods for metabolic engineering and design of microbial cell factories (computational strain design). The methods we employ are based on stoichiometric and constraint-based modeling techniques. In particular, we have been developing the approach of minimal cut sets (MCSs) which represent intervention strategies in metabolic metworks blocking undesired while keeping desired metabolic phenotypes [1,2,4,7]. Recent research and developments include the characterization of duality principles between elementary modes and MCSs [8] (collaboration with Utz Uwe Haus, ETH Zürich), an algorithm for enumeration of MCSs in genome-sclae metabolic networks [10], the generalization of MCSs to regulatory MCSs [12] and findings on structural requirements for coupling growth and product synthesis in microbial strains [13] (collaborations with Krishna Mahadevan, Univ. Toronto). Another heuristic method developed by us for metabolic engineering is CASOP which aims at increasing the productivity of bacterial production strains [6].

We apply the above methods in realistic applications, e.g., for improving biofuel production in cyanobacteria [11] or terpenoid synthesis with E. coli or yeast [9]. Based on model predictions, we recently also implemented an ATP futile cycle in E.coli demonstrating that elevated ATP wasting can increase the yield of lactate produced by E.coli [14].

For computing combinatorial intervention strategies and failure modes in signaling networks, we introduced the approach of minimal intervention sets which are combinations of gene knock-outs and over-expressions inducing a desired network behavior [3,5]. We devised an algorithm for computing those minimal intervention sets in large networks [5].

Selected references cited above:

  1. Klamt S and Gilles ED (2004) Minimal cut sets in biochemical reaction networks. Bioinformatics 20(2):226-234.
  2. Klamt S (2006) Generalized concept of minimal cut sets in biochemical networks. Biosystems 83:233-247.
  3. Klamt S, Saez-Rodriguez J, Lindquist JA, Simeoni L and Gilles ED (2006) A methodology for the structural amd functional analysis of signaling and regulatory networks. BMC Bioinformatics 7:56.
  4. Haus UU, Klamt S and Stephen T (2008) Computing knock-out strategies in metabolic networks. Journal of Computational Biology 15:259-268.
  5. Samaga R, von Kamp A and Klamt S (2010) Computing combinatorial intervention strategies and failure modes in signaling networks. Journal of Computational Biology 17:39-53.
  6. Hädicke O and Klamt S (2010) CASOP: a computational approach for strain optimization aiming at high productivity. Journal of Biotechnology 147:88-101.
  7. Hädicke O and Klamt S (2011) Computing complex metabolic intervention strategies using constrained minimal cut sets. Metabolic Engineering 13:204-213.
  8. Ballerstein K, von Kamp A, Klamt S and Haus UU (2012) Minimal cut sets in a metabolic network are elementary modes in a dual network. Bioinformatics 28:381-387.
  9. Gruschattka E, Hädicke O, Klamt S, Schuetz V, Kayser O (2013) In silico profiling of Escherichia coli and Saccharomyces cerevisiae as terpeniod factories, Microbial Cell Factories 12:84.
  10. von Kamp A, Klamt S (2014) Enumeration of smallest intervention strategies in genome-sclae metabolic networks. PLoS Computational Biology 10(1):e1003378.
  11. Erdrich P, Steuer R, Knoop H, Klamt S (2014) Cyanobacterial biofuels: new insights and strain desing strategies revealed by computational modeling. Microbial Cell Factories 13:128.
  12. Mahadevan R, von Kamp A, Klamt S (2015) Genome-scale strain design based on regulatory minimal cut sets. Bioinformatics, in press.
  13. Klamt S, Mahadevan R, (2015) On the feasibility of growth-coupled product synthesis in microbial strains, Metabolic Engineering, in press.
  14. Hädicke O, Bettenbrock K, Klamt S (2015) Enforced ATP futile cycling increases sepcific productivity and yield of anaerobic lactate production in Escherichia coli. Biotechnology and Bioengineering, in press.
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