Publications of Steffen Klamt

Jungreuthmeyer, C.; Nair, G.; Klamt, S.; Zanghellini, J.: Comparison and improvement of algorithms for computing minimal cut sets. BMC Bioinformatics 14 (1), p. 318 (2013)

Melas, I. N.; Samaga, R.; Alexopoulos, L. G.; Klamt, S.: Detecting and Removing Inconsistencies between Experimental Data and Signaling Network Topologies Using Integer Linear Programming on Interaction Graphs. PLoS Computational Biology 9 (9), p. e1003204 (2013)

Pinna, A.; Heise, S.; Flassig, R.; de la Fuente, A.; Klamt, S.: Reconstruction of large-scale regulatory networks based on perturbation graphs and transitive reduction: improved methods and their evaluation. BMC Systems Biology 7, p. 73 (2013)

Samaga, R.; Klamt, S.: Modeling approaches for qualitative and semi-quantitative analysis of cellular signaling networks. Cell Communication and Signaling 11 (1), p. 43 (2013)

Hädicke, O.; Grammel, H.; Klamt, S.: Metabolic network modeling of redox balancing and biohydrogen production in purple nonsulfur bacteria. BMC Systems Biology 5, p. 150 (2011)

Ryll, A.; Samaga, R.; Schaper, F.; Alexopoulos, L.G.; Klamt, S.: Large-scale models of IL-1 and IL-6 signaling and their hepatocellular specification. Molecular BioSystems 7 (12), pp. 3253 - 3270 (2011)

Franke, R.; Theis, F.J.; Klamt, S.: From Binary to Multivalued to Continuous Models: The Iac Operon as a Case Study. Journal of Integrative Bioinformatics 7 (1), p. 151 (2010)

Klamt, S.; Flassig, R.; Sundmacher, K.: TRANSWESD: inferring cellular networks with transitive reduction. Bioinformatics 26 (17), pp. 2160 - 2168 (2010)

Samaga, R.; von Kamp, A.; Klamt, S.: Computing Combinatorial Intervention Strategies and Failure Modes in Signaling Networks. Journal of Computational Biology 17 (1), pp. 39 - 53 (2010)

Klamt, S.; Haus, U.-U.; Theis, F.: Hypergraphs and cellular networks. PLoS Computational Biology 5 (5), p. e1000385 (2009)

Klamt, S.; von Kamp, A.: Computing Paths and Cycles in Biological Interaction Graphs. BMC Bioinformatics 10, p. 181 (2009)

Poltz, R.; Franke, R.; Schweitzer, K.; Klamt, S.; Gilles, E. D.; Naumann, M.: Logical network of genotoxic stress-induced NF-kB signal transduction predicts putative target structures for therapeutic intervention strategies. Advances and Applications in Bioinformatics and Chemistry 2, pp. 125 - 138 (2009)

Saez-Rodriguez, J.; Alexopoulos, L. G.; Epperlein, J.; Samaga, R.; Lauffenburger, D. A.; Klamt, S.; Sorger, P. K.: Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction. Molecular Systems Biology 5, p. 331 (2009)

Samaga, R.; Saez-Rodriguez, J.; Alexopoulos, L. G.; Sorger, P. K.; Klamt, S.: The logic of EGFR/ErbB signaling: theoretical properties and analysis of high-throughput data. PLoS Computational Biology 5 (8), e1000438 (2009)

Wittmann, D. M.; Krumsiek, J.; Saez, J.; Lauffenburger, D. A.; Klamt, S.; Theis, F.: Transforming Boolean models to continuous models: methodology and application to T-cell receptor signaling. BMC Systems Biology 3, 98 (2009)

Franke, R.; Mueller, M.; Wundrack, N.; Gilles, E. D.; Klamt, S.; Kaehne, T.; Naumann, M.: Host-pathogen systems biology: Logical modelling of hepatocyte growth factor and Helicobacter pylori induced c-Met signal transduction. BMC Systems Biology 2, 4 (2008)

Klamt, S.; Grammel, H.; Straube, R.; Ghosh, R.; Gilles, E. D.: Modeling the electron transport chain of purple non-sulfur bacteria. Molecular Systems Biology 4, 156 (2008)

Beste, D. J.; Hooper, T.; Stewart, G.; Bonde, B.; Avignone-Rossa, C.; Bushell, M. E.; Wheeler, P.; Klamt, S.; Kierzek, A. M.; McFadden, J.: GSMN-TB: a web-based genome-scale network model of Mycobacterium tuberculosis metabolism. Genome Biology 8, R89 (2007)

Klamt, S.; Saez-Rodriguez, J.; Gilles, E. D.: Structural and functional analysis of cellular networks with CellNetAnalyzer. BMC Systems Biology 1, 2 (2007)

Saez-Rodriguez, J.; Simeoni, L.; Lindquist, J.; Hemenway, R.; Bommhardt, U.; Arndt, B.; Haus, U. U.; Weismantel, R.; Gilles, E. D.; Klamt, S. et al.; Schraven, B.: A logical model provides insights into T cell receptor signaling. PLoS Computational Biology 3, e163 (2007)