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.


Modeling and Analysis of Biological Networks

We develop and employ various mathematical modeling techniques to under­stand the behavior of cellular systems and to study the relation­ships between structure and function of biomolecular networks. One particular focus are qualitative and semi-quantitative modeling techniques for analyzing large-scale cellular networks where the set-up of predictive quantitative models (based on differential equations) is usually infeasible due to lack of precise kinetic information. We are particularly interested in methods that deliver verifiable predictions and hypotheses on network function driving the iterative cycle between experiment and modeling. Selected research topics are:

Metabolic networks

  • Stoichiometic Network Analysis / Flux Balance Analysis / Metabolic Network Analysis / Constraint-based Modeling
  • Theory and applications of elementary modes (EMs)  [1,2] and minimal cut sets [3,4,19] in metabolic network analysis [1,2]
  • Duality of EMs and MCSs [4,12] and algorithms for their computation in large networks [5,6,12]
  • Applications: metabolic network analysis of Escherichia coli [1], purple nonsulfur bacteria (in particular Rhodospirillum rubrum [7,11,20], and others

Signaling and regulatory networks

  • Interaction graphs and logical networks as modeling frameworks for signal transduction networks: concepts and algorithms [8,9,12,16,24]
  • Transforming logical models into (qualitative) ODE models [13]
  • Evaluation of high-throughput form signaling networks with logical models [14,15]
  • Combining qualitative and quantitative modeling approaches [23]
  • Applications: Logical modeling of T-Cell receptor signaling [10,13], EGFR/ErbB signaling [14], IL-1/IL-6 signaling [19] and others [20]

We are also interested in methods capable to predict aspects of the qualitative dynamic behavior of mechanistic (ODE) models of biological systems from network structure alone. Moreover, we develop approaches for integrating different types of biological networks and models [21,23].

Furthermore, we combine modeling and experimental studies (in our own lab) to gain an in-depth understanding of (i) the genetic control of carbohydrate uptake in E.coli as well as of (ii) the adaption of in E.coli to changing oxygen supply (see Experimental Systems Biology).

Selected references cited above:

  1. Stelling J, Klamt S, Bettenbrock K, Schuster S and Gilles ED (2002) Metabolic network structure determines key aspects of functionality and regulation. Nature 420:190-193.
  2. Klamt S and Stelling J (2006) Stoichiometric and constraint-based modelling. In: System Modeling in Cellular Biology: From Concepts to Nuts and Bolts. Edited by Szallasi Z, Stelling S and Periwal V. Cambridge, MIT Press, pp. 73-96.
  3. Klamt S and Gilles ED (2004) Minimal cut sets in biochemical reaction networks. Bioinformatics 20(2):226-234.
  4. Klamt S (2006) Generalized concept of minimal cut sets in biochemical networks. Biosystems 83:233-247.
  5. Gagneur J and Klamt S (2004) Computation of elementary modes: a unifying framework and the new binary approach. BMC Bioinformatics 5:175.
  6. Klamt S, Gagneur J and von Kamp A (2005) Algorithmic approaches for computing elementary modes in large biochemical reaction networks. IEE Systems Biology 152:249-255.
  7. Klamt S, Schuster S and Gilles ED (2002) Calculability analysis in underdetermined metabolic networks illustrated by a model of the central metabolism in purple nonsulfur bacteria. Biotechnology & Bioengineering 77:734-751.
  8. 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 Bioinformatics7:56.
  9. Klamt S, Saez-Rodriguez J and Gilles ED (2007) Structural and functional analysis of cellular networks with CellNetAnalyzer. BMC Systems Biology 2:4.
  10. Saez-Rodriguez J, Simeoni L, Lindquist JA, Hemenway R, Bommhardt U, Arndt B, Haus UU, Weismantel R, Gilles ED, Klamt S and Schraven B (2007) A logical model provides insights into T cell receptor signaling. PLoS Computational Biology 3:e163.
  11. Klamt S, Grammel H, Straube R, Ghosh R and Gilles ED (2008) Modeling the electron transport chain of purple non-sulfur bacteria. Molecular Systems Biology 4:156.
  12. Haus UU, Klamt S and Stephen T (2008) Computing knock-out strategies in metabolic networks. Journal of Computational Biology 15:259-268.
  13. Klamt S and von Kamp A (2009) Computing paths and cycles in biological interaction graphs. BMC Bioinformatics 10:181.
  14. Wittmann DM, Krumsiek J, Saez-Rodriguez J, Lauffenburger DA, Klamt S and Theis FJ (2009) Transforming Boolean models to continuous models: methodology and application to T-cell receptor signaling. BMC Systems Biology 3:98.
  15. Samaga R, Saez-Rodriguez J, Alexopoulos LG, Sorger PK and Klamt S (2009) The logic of EGFR/ErbB signaling: Theoretical properties and analysis of high-throughput data. PLoS Computational Biology 5(8):e1000438.
  16. 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.
  17. Hädicke O and Klamt S (2011) Computing complex metabolic intervention strategies using constrained minimal cut sets. Metabolic Engineering 13:204-213.
  18. Hädicke O, Grammel H and Klamt S (2011) Metabolic network modeling of redox balancing and biohydrogen production in purple nonsulfur bacteria. BMC Systems Biology 5:150.
  19. Ryll A, Samaga R, Schaper F, Alexopoulos LG and Klamt S (2011) Large-scale network models of IL-1 and IL-6 signaling and their hepatocellular specification. Molecular Biosystems 7:3253-3270.
  20. Huard J, Mueller S, Gilles ED, Klingmueller U and Klamt S (2012) An integratice model links multiple inputs and signaling pathways to the onset of DNA synthesis in hepatocytes. FEBS Journal 279: 3290-3313.
  21. Goncalves E, Bucher J, Ryll A, Niklas J, Mauch K, Klamt S, Rocha m, Saez-Rodriguez J (2013) Bridging the layers: towards integration of signal transduction, regulation and metabolism models. Molecular BioSystems 9: 1576-1583.
  22. Ryll A, Bucher J, Bonin A, Bongard S, Goncalves E, Saez-Rodriguez J, Niklas J, Klamt S (2014) A model integration approach linking signalling and generegulatory logic with kinetic metabolic models. BioSystems 124: 26-38.
  23. D`Allessandro LA, Samaga R, Maiwal T, Rho SH, Bonefas S, Raue A, Iwamoto N, Kienast A, Waldow K, Meyer R, SChilling M, Timmer J, Klamt S, Klingmueller U (2015) Disentangling the Complexity of HGF Combining Qualitative and Quantitative Modeling. PLOS Computational Biology 11 (4): e1004192.
  24. Samaga R, Klamt S (2013) Modeling approaches for qualitative and semi-quantitative analysis of cellular signaling networks. Cell Communicationand Signaling 11:43.

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