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

04.04.2018: New Publication
Klamt S, Müller S, Regensburger J, Zanghellini J (2018) A mathematical framework for yield (vs. rate) optimization in constraint-based modeling and applications in metabolic engineering. Metabolic Engineering 47: 153-169.

15.03.2018: de.NBI-Workshop April 24-26, 2018 at the MPI
We are co-organizing a workshop about modeling and data exchange in systems biology (presented software tools: COPASI, CellNetAnalyzer, SEEK, SABIO-RK). More information and registration via the following website.

23.02.2018: New Publication
Klamt S, Mahadevan R, Hädicke O (2018) When Do Two-Stage Processes Outperform One-Stage Processes? Biotechnology Journal 3: 1700539.   

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.


Reverse Engineering and Network Inference

We develop methods for reverse engineering of cellular networks from experimental data based on different modeling formalisms (interaction graphs, logical networks, ODEs). We were involved in elaborating a method that creates logical models from experimental data sets and prior knowledge [1]. Another method we have been developing is based on transitive reduction and infers signaling and regulatory networks from pertubation experiments [2,3]. We also showed that transitive reduction is a useful method in the context of genetical genomics data [4,5]. More recently, in collaboration with the Alexopoulos group from the NTU Athens, we used sign-consistency-related approaches for detecting and removing inconsistencies between signaling network topologies and experimental data [6]. Finally together with the groups of Ursula Klingmüller (DKFZ Heidelberg) and Jens Timmer (Univ. Freiburg), we devised a hybrid framework combining qualitative and quantitative modeling to identify the HGF signaling network topology in primary hepatocytes based on experimental data [7].

Selected references cited above:

  1. Saez-Rodriguez J, Alexopoulos LG, Epperlein J, Samaga R, Lauffenburger DA, Klamt S and Sorger PK (2009) Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction. Molecular Systems Biology 5:331.
  2. Klamt S, Flassig RJ and Sundmacher K (2010) TRANSWESD: inferring cellular networks with transitive reduction. Bioinformatics 26:2160-2168.
  3. Pinna A, Heise S, Flassig RJ, de la Fuente A, Klamt S (2013) Reconstruction of large-scale regulatory networks based on pertubation graphs and transitive reduction: improved methods and their evaluation. BMC Systems Biology 7: 73.
  4. Flassig RJ, Heise S, Sundmacher K, Klamt S (2013) An effective framework of reconstuction gene regulatory networks from genetical genomics data. Bioinformatics 29: 246-254.
  5. Heise S, Flassig RJ, Klamt S (2014) Benchmarking a Simple Yet Effective Approach for Inferring Gene Regulatory Networks from Systems Genetics Data In: Gene Network Inference: Verification of Methods for Systems Genetic Data. Edited by de la Fuenta A, Springer, pp. 33-47.
  6. Melas IN, Samaga R, Alexopoulos LG, Klamt S (2013) Detecting and Removing Inconsistencies between Experimental Data and Signaling Network Topologies Using Integer Linear Programming on Interaction Graphs. PLoS Computational Biology 9:e1003204.
  7. D`Alessandro LA, Samaga R, Maiwald T, Tho SH, Bonefas S, Raue A, Iwamoto N, Kienast A, Wladow K, Meyer R, Schilling M, Timmer J, Klamt S, Klingmüller U (2015) Disentangling the Complexity of HGF Combining Qualitative and Quantitative Modeling. PLoS Computational Biology 11(4):e1004192.
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