Suchergebnisse

Klamt, S.; Regensburger, G.; Gerstl, P.M.; Jungreuthmayer, C.; Schuster, S.; Mahadevan, R.; Zanghellini, J.; Müller, S.: From elementary flux modes to elementary flux vectors: Metabolic pathway analysis with arbitrary linear flux constraints. PLoS Computational Biology 13 (4), e1005409 (2017)

Gerstl, M. P.; Klamt, S.; Jungreuthmayer, C.; Zanghellini, J.: Exact quantification of cellular robustness in genome-scale metabolic networks. Bioinformatics 32 (5), S. 730 - 737 (2016)

Harder, B.-J.; Bettenbrock, K.; Klamt, S.: Model-Based metabolic engineering enables high yield itaconic acid production by Escherichia coli. Metabolic Engineering 38, S. 29 - 37 (2016)

Koch, S.; Benndorf, D.; Fronk, K.; Reichl, U.; Klamt, S.: Predicting compositions of microbial communities from stoichiometric models with applications for the biogas process. Biotechnology for Biofuels 9, 17 (2016)

Ud-Dean, S.; Heise, S.; Klamt, S.; Gunawan, R.: TRaCE+: Ensemble inference of gene regulatory networks from transcriptional expression profiles of gene knock-out experiments. BMC Bioinformatics 17 (17), 252 (2016)

Wu, H.; Kamp von, A.; Leoncikas, V.; Mori, W.; Sahin, M.; Gevorgyan, A.; Linley, C.; Grabowski, M.; Mannan, A. A.; Stoy, N. et al.; Steward, G. R.; Ward, L. T.; Lewis, D.J.M.; Sroca, J.; Matsuno, H.; Klamt, S.; Westerhoff, H.V.; McFadden, J.; Plant, N.J.; Kierzek, A.M.: MUFINS: multi-formalism interaction network simulator. npj Systems Biology and Applications 2, 16032 (2016)

Bastiaens, P. I.H.; Birtwistle, M.; Blüthgen, N.; Bruggeman, F.; Cho, K.-H.; de la Fuente, A.; Hoek, J.; Kiyatkin, A.; Klamt, S.; Kolch, W. et al.; Legewie, S.; Mendes, P.; Naka, T.; Santra, T.; Sontag, E.; Westerhoff, H.; Kholodenko, B.: Silence on the relevant literature and errors in implementation. Nature Biotechnology 33 (4), S. 336 - 339 (2015)

D`Allessandro, L. A.; Samaga, R.; Maiwald, T.; Rho, S.-H.; Bonefas, S.; Raue, A.; Iwamoto, N.; Kienast, A.; Waldow, K.; Meyer, R. et al.; Schilling, M.; Timmer, J.; Klamt, S.; Klingmüller, U.: Disentangling the Complexity of HGF Signaling by Combining Qualitative and Quantitative Modeling. PLoS Computational Biology 11 (4), e1004192 (2015)

Erdrich, P.; Steuer, R.; Klamt, S.: An algorithm for the reduction of genome-scale metabolic network models to meaningful core models. BMC Systems Biology 9, 48 (2015)

Hädicke, O.; Bettenbrock, K.; Klamt, S.: Enforced ATP futile cycling increases specific productivity and yield of anaerobic lactate production in Escherichia coli. Biotechnology and Bioengineering 112 (10), S. 2195 - 2199 (2015)

Hädicke, O.; Klamt, S.: Manipulation of the ATP pool as a tool for metabolic engineering. Biochemical Society Transactions (London) 43 (6), S. 1140 - 1145 (2015)

Klamt, S.; Mahadevan, R.: On the feasibility of growth-coupled product synthesis in microbial strains. Metabolic Engineering 30, S. 166 - 178 (2015)

Mahadevan, R.; Kamp von, A.; Klamt, S.: Genome-scale strain designs based on regulatory minimal cut sets. Bioinformatics 31 (17), S. 2844 - 2851 (2015)

Michailidou, M.; Melas, I.; Messinis, D.; Klamt, S.; Alexopoulos, L.; Kolisis, F.; Loutrari, H.: Network-Based Analysis of Nutraceuticals in Human Hepatocellular Carcinomas Reveals Mechanisms of Chemopreventive Action. CPT: Pharmacometrics & Systems Pharmacology 4 (6), S. 350 - 361 (2015)

Mueller, S.; Huard, J.; Waldow, K.; Huang, X.; D'Alessandro, L.; Bohl, S.; Börner, K.; Grimm, D.; Klamt, S.; Klingmüller, U. et al.; Schilling, M.: T160‐phosphorylated CDK2 defines threshold for HGF‐dependent proliferation in primary hepatocytes. Molecular Systems Biology 11, 795 (2015)

Thiele, S.; Cerone, L.; Saez-Rodriguez, J.; Siegel, A.; Guciolowski, C.; Klamt, S.: Extended notions of sign consistency to relate experimental data to signaling and regulatory network topologies. BMC Bioinformatics 16 (1), 345 (2015)

Wiechert, W.; Klamt, S.: Computational Systems Biology — neues Fach in den Lebenswissenschaften. Biospektrum 21 (1), S. 46 - 48 (2015)

Erdrich, P.; Knoop, H.; Steuer, R.; Klamt, S.: Cyanobacterial biofuels: new insights and strain design strategies revealed by computational modeling. Microbial Cell Factories 13, S. 128 - 128 (2014)

Kamp von, A.; Klamt, S.: Enumeration of Smallest Intervention Strategies in Genome-Scale Metabolic Networks. PLoS Computational Biology 10 (1), e1003378 (2014)

Lohr, V.; Hädicke, O.; Genzel, Y.; Jordan, I.; Buentemeyer, H.; Klamt, S.; Reichl, U.: The avian cell line AGE1.CR.pIX characterized by metabolic flux analysis. BMC Biotechnology 14, S. 72 (2014)