Major research topics of the group are the following:

Network Analysis

We develop qualitative 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. Particular topics are:

Metabolic networks

Signaling and regulatory networks

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 (see also Conradi Team below).

Targeted Modification and Redesign of Cellular Networks

Apart from descriptive studies, another focus of our research is model-based identification of intervention strategies for rational design and reconfiguration 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.

For identifying gentic targets in metabolic networks, e.g. for rational strain design, we proposed the approach of minimal cut sets (MCSs), the dual counterparts of elementary modes [3,4,13,21]. Another method developed by us for metabolic engineering is CASOP which aims at increasing the productivitiy of bacterial production strains [19].

For computing combinatorial intervention strategies and failure modes in signaling networks, we introduced the approach of minimal intervention sets which are combinations of knock-outs and over-expressions inducing a desired network behavior [8,18]. We devised an algorithm for computing minimal intervention sets in large networks [18].

Reverse Engineering and Network Inference

We are interested in methods for reverse engineering of celluar networks from experimental data. Some of these methods rely on modeling formalisms (interaction graphs, logical networks) mentioned above. We were involved in developing a method that creates logical models from experimental data sets and prior knowledge [17]. Another method we have been developing is based on transitive reduction and infers signaling and regulatory networks from perturbation experiments [20].

Tools: CellNetAnalyzer and ProMoT

Methods and algorithms described above have been implemented in our software package CellNetAnalyzer, a comprehensive graphical user interface for MATLAB facilitating structural and functional analysis of metabolic and signaling networks [9]. Another software developed at the MPI is the modeling tool ProMoT which provides a modular modeling concept for the systematic setup of dynamic and logical models [24]. ProMoT is tightly integrated with simulation and analysis tools including Diana and CellNetAnalyzer.
Data management has become a key technology for Systems Biology in order to store, find, share and exchange large sets experimental data as well as models and workflows. We established and maintain such a data management system for the Magdeburg Center for Systems Biology MaCS.

Qualitative Dynamics of Biochemical Reaction Networks
(Research Team Conradi)

The research of the Conradi Team aims at supporting the generation of biochemical knowledge by developing mathematical methods for of a systems theory of biochemical reaction networks. Realistic biochemical models comprise many states and parameters and measurement data are usually noisy and very few (in terms of data points and repetitions). Hence mathematical models are often very large and poorly parameterized. To overcome this problem one has to resort to methods that are independent of parameter values. For example, due to high uncertainty in measurement values one is often interested in whether or not a given reaction network can reproduce an observed behavior at all (i.e. for any conceivable parameter vector). Hence one is in fact interested in the qualitative dynamical properties of the mathematical model. Of particular interest is the existence of multiple steady states, e.g. in cell cycle regulation and signal transduction [27,28]. Our research is concerned with establishing necessary and sufficient conditions for the existence of multiple steady states, where necessary conditions are desired, as they allow to discard certain networks and hence exclude biochemical hypotheses.

Design Principles of Biological Networks
(Research Team Straube)

Despite recent advances in high-throughput methods for the quantitative analysis of large scale cellular reaction networks we often still lack an intuitive understanding for the design principles and the respective functionality of the underlying network structure. Therefore, the research team of Dr. Straube focuses on comparably small, yet biologically relevant, network structures (called network motifs) to reveal and analyze their advantages compared to alternative designs. We use methods from applied mathematics, nonlinear dynamics and non-equilibrium statistical physics to understand the relation between design and functionality of molecular reaction networks, especially in the presence of diffusive coupling and molecular noise. Particular topics of research include covalent modification systems, two-component systems, reaction-diffusion systems [25] and Mean-First-Passage-Time calculations [26] in singularly perturbed domains (domains with 'small holes').

Selected Publications

A complete list of publications can be found here.

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 Bioinformatics 7:56.
  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. Franke R, Mueller M, Wundrack N, Gilles ED, Klamt S, Kaehne T and Naumann M (2008) Host-pathogen systems biology: Logical modelling of hepatocyte growth factor and Helicobacter pylori induced c-Met signal transduction. BMC Systems Biology 2:4.
  12. 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.
  13. Haus UU, Klamt S and Stephen T (2008) Computing knock-out strategies in metabolic networks. Journal of Computational Biology 15:259-268.
  14. Klamt S and von Kamp A (2009) Computing paths and cycles in biological interaction graphs. BMC Bioinformatics 10:181.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. Hädicke O and Klamt S (2010) CASOP: a computational approach for strain optimization aiming at high productivity. Journal of Biotechnology 147:88-101.
  20. Klamt S, Flassig RJ and Sundmacher K (2010) TRANSWESD: inferring cellular networks with transitive reduction. Bioinformatics 26:2160-2168.
  21. Hädicke O and Klamt S (2011) Computing complex metabolic intervention strategies using constrained minimal cut sets. Metabolic Engineering 13:204-213.
  22. 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.
  23. 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.
  24. Mirschel, S, Steinmetz K, Rempel R, Ginkel M and Gilles ED (2009) PROMOT: modular modeling for systems biology. Bioinformatics 25:687-689.
  25. Straube R and Nicola EM (2010) Diffusive coupling can discriminate between similar reaction mechanisms in an allosteric enzyme system.BMC Systems Biology 4:165.
  26. Coombs D, Straube R and Ward MJ (2009) Diffusion on a Sphere with Localized Traps: Mean First Passage Time, Eigenvalue Asymptotics, and Fekete Points.SIAM J. Appl. Math 70:302-332.
  27. Conradi C, Flockerzi D, Raisch J and Stelling J. (2007) Subnetwork analysis reveals dynamic features of complex (bio)chemical networks. PNASi 104i:19175-19180.
  28. Conradi C and Flockerzi D (2011) Multistationarity in mass action networks with applications to ERK activation. Journal of Mathematical Biology in press.
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