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

14.12.2018: New Publication
Venayak N, von Kamp A, Klamt S, Mahadevan R (2018): MoVE identifies metabolic valves to switch between phenotypic states. Nature Communication 9:5332.

07.11.2018: Björn defended his PhD thesis.

28.09.2018: New Publication
Hädicke O, von Kamp A, Aydogan T, Klamt S (2018) OptMDFpathway: Identification of metabolic pathways with maximal thermodynamic driving force and its application for analyzing the endogenous CO2 fixation potential of Escherichia coli. PLoS Computational Biology 13:e1006492.

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.


Data-driven Inference of Cellular Networks

Genes and proteins of regulatory and signaling networks are often known whereas many of their mutual interactions remain still undiscovered or are unclear. Our group develops and applies novel algorithms for the computer-aided identification (inference) of cellular signaling and gene regulatory networks from experimental data. While we have used different formalisms for network inference (interaction graphs, logical networks, ODEs) recent work focuses on methods for interaction graphs:

  • Sign consistency in interaction graphs as paradigm for network inference.
  • Mixed-integer linear programming (MILP) and answer set programming (ASP) for solving combinatorial problems arising in sign-consistency based inference.
  • Experimental design for discriminating candidate models in network inference.
  • Transitive reduction in interaction graphs for identifying and removing edges from indirect effects.

We apply these methods for the inference of mammalian signaling networks, e.g. for the EGF and HGF signaling pathway in hepatocytes.

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