Dr.-Ing. Steffen Klamt
Dr.-Ing. Steffen Klamt
Telefon: +49 391 6110 480
Fax: +49 391 6110 509
Raum: S2.10


Susanne Hintsch
Telefon:+49 391 6110 477Fax:+49 391 6110 452

Neuigkeiten / Letzte Publikationen

15.03.2018: de.NBI-Workshop 24.-26. April 2018 am MPI
Wir sind Co-Organisator eines Workshops über Modellierung und Datenaustausch in der Systembiologie (vorgestellte Werkzeuge: u.a. COPASI, CellNetAnalyzer, SEEK, SABIO-RK). Mehr Informationen und Anmeldung auf dieser Webseite.

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

07.12.2017: Neue Publikation
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.

05.10.2017: Neue Publikation      
von Kamp A, Thiele S, Hädicke O, Klamt S (2017) Use of CellNetAnalyzer in biotechnology and metabolic engineering. Journal of Biotechnology 261: 221-228.

Analyse und Redesign biologischer Netzwerke

Steffen Klamt

Zeitschriftenartikel (76)

  1. 1.
    Bosch, J.; Klamt, S.; Stoll, M.: Generalizing diffuse interface methods on graphs: non-smooth potentials and hypergraphs. SIAM Journal of Applied Mathematics (angenommen)
  2. 2.
    Harder, B.-J.; Bettenbrock, K.; Klamt, S.: Temperature-dependent dynamic control of the TCA cycle increases volumetric productivity of itaconic acid production by Escherichia coli. Biotechnology and Bioengineering 115 (1), S. 156 - 164 (2018)
  3. 3.
    Klamt, S.; Mahadevan, R.; Hädicke, O.: When do two-stage processes outperform one-stage processes? Biotechnology Journal 13 (2), 1700539 (2018)
  4. 4.
    Klamt, S.; Müller, S.; Regensburger, G.; Zanghellini, J.: A mathematical framework for yield (versus rate) optimization in constraint-based modeling and applications in metabolic engineering. Metabolic Engineering (angenommen)
  5. 5.
    Hädicke, O.; Klamt, S.: EColiCore2: a reference model of the central metabolism of Escherichia coli and the relationships to its genome-scale parent model. Scientific Reports 7, 39647 (2017)
  6. 6.
    Kamp von, A.; Thiele, S.; Hädicke, O.; Klamt, S.: Use of CellNetAnalyzer in biotechnology and metabolic engineering. Journal of Biotechnology 261, S. 221 - 228 (2017)
  7. 7.
    Kamp von, A.; Klamt, S.: Growth-coupled overproduction is feasible for almost all metabolites in five major production organisms. Nature Communications 8, 15926 (2017)
  8. 8.
    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)
  9. 9.
    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)
  10. 10.
    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)
  11. 11.
    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)
  12. 12.
    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)
  13. 13.
    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)
  14. 14.
    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)
  15. 15.
    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)
  16. 16.
    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)
  17. 17.
    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)
  18. 18.
    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)
  19. 19.
    Klamt, S.; Mahadevan, R.: On the feasibility of growth-coupled product synthesis in microbial strains. Metabolic Engineering 30, S. 166 - 178 (2015)
  20. 20.
    Mahadevan, R.; Kamp von, A.; Klamt, S.: Genome-scale strain designs based on regulatory minimal cut sets. Bioinformatics 31 (17), S. 2844 - 2851 (2015)
  21. 21.
    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)
  22. 22.
    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)
  23. 23.
    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)
  24. 24.
    Wiechert, W.; Klamt, S.: Computational Systems Biology — neues Fach in den Lebenswissenschaften. Biospektrum 21 (1), S. 46 - 48 (2015)
  25. 25.
    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)
  26. 26.
    Kamp von, A.; Klamt, S.: Enumeration of Smallest Intervention Strategies in Genome-Scale Metabolic Networks. PLoS Computational Biology 10 (1), e1003378 (2014)
  27. 27.
    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)
  28. 28.
    Ryll, A.; Bucher, J.; Bonin, A.; Bongard, S.; Gonçalves , E.; Saez- Roidriguez, J.; Niklas, J.; Klamt, S.: A model integration approach linking signalling and gene-regulatory logic with kinetic metabolic models. Biosystems 124, S. 26 - 38 (2014)
  29. 29.
    Chaoiya, C.; Berenguier, D.; Keating, S. M.; Naldi, A.; van Iersel, M. P.; Rodriguez, N.; Dräger, A.; Büchel, F.; Cokelaer, T.; Kowal, B. et al.; Wicks, B.; Gonçalves, E.; Dorier, J.; Page, M.; Monteiro, P. T.; Kamp von, A.; Xenarius , I.; de Jong, H.; Hucka, M.; Klamt, S.; Thieffrey, D.; Le Novère, N.; Saez-Rodriguez, J.; Helikar, T.: SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formlisms and tools. BMC Systems Biology 7, S. 135 (2013)
  30. 30.
    Flassig, R.; Heise, S.; Sundmacher, K.; Klamt, S.: An effective framework for reconstructing gene regulatory networks from genetical genomics data. Bioinformatics 29 (2), S. 246 - 254 (2013)
  31. 31.
    Gonçalves, E.; Bucher, J.; Ryll, A.; Niklas, J.; Mauch, K.; Klamt, S.; Rocha, M.; Saez-Rodriguez, J.: Bridging the layers: towards integration of signal transduction, regulation and metabolism into mathematical models. Molecular BioSystems 9 (7), S. 1576 - 1583 (2013)
  32. 32.
    Gruchattka, E.; Hädicke, O.; Klamt, S.; Schuetz, V.; Kayser , O.: In silico profiling of Escherichia coli and Saccharomyces cerevisiae as terpenoid factories. Microbial Cell Factories 12, 84 (2013)
  33. 33.
    Hädicke, O.; Lohr, V.; Genzel, Y.; Reichl, U.; Klamt, S.: Evaluating differences of metabolic performances: Statistical methods and their application to animal cell cultivations. Biotechnology and Bioengineering 110 (10), S. 2633 - 2642 (2013)
  34. 34.
    Jungreuthmeyer, C.; Nair, G.; Klamt, S.; Zanghellini, J.: Comparison and improvement of algorithms for computing minimal cut sets. BMC Bioinformatics 14 (1), S. 318 (2013)
  35. 35.
    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), S. e1003204 (2013)
  36. 36.
    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, S. 73 (2013)
  37. 37.
    Samaga, R.; Klamt, S.: Modeling approaches for qualitative and semi-quantitative analysis of cellular signaling networks. Cell Communication and Signaling 11 (1), S. 43 (2013)
  38. 38.
    Ballerstein, K.; von Kamp, A.; Klamt, S.; Haus, U.-U.: Minimal cut sets in metabolic networks are elementary modes in a dual network. Bioinformatics 28 (3), S. 381 - 387 (2012)
  39. 39.
    Huard, J.; Mueller, S.; Gilles, E. D.; Klingmüller, U.; Klamt, S.: An integrative model links multiple inputs and signaling pathways to the onset of DNA synthesis in hepatocytes. FEBS Journal 279 (18), S. 3290 - 3313 (2012)
  40. 40.
    Hädicke, O.; Klamt, S.: Computing complex metabolic intervention strategies using constrained minimal cut sets. Metabolic Engineering 13 (2), S. 204 - 213 (2011)
  41. 41.
    Hädicke, O.; Grammel, H.; Klamt, S.: Metabolic network modeling of redox balancing and biohydrogen production in purple nonsulfur bacteria. BMC Systems Biology 5, S. 150 (2011)
  42. 42.
    Klamt, S.; Kamp von, A.: An application programming interface for CellNetAnalyzer. Biosystems 105 (2), S. 162 - 168 (2011)
  43. 43.
    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), S. 3253 - 3270 (2011)
  44. 44.
    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), S. 151 (2010)
  45. 45.
    Hädicke, O.; Klamt, S.: CASOP: a computational approach for strain optimization aiming at high productivity. Journal of Biotechnology 147 (2), S. 88 - 101 (2010)
  46. 46.
    Klamt, S.; Flassig, R.; Sundmacher, K.: TRANSWESD: inferring cellular networks with transitive reduction. Bioinformatics 26 (17), S. 2160 - 2168 (2010)
  47. 47.
    Samaga, R.; von Kamp, A.; Klamt, S.: Computing Combinatorial Intervention Strategies and Failure Modes in Signaling Networks. Journal of Computational Biology 17 (1), S. 39 - 53 (2010)
  48. 48.
    Klamt, S.; von Kamp, A.: Computing Paths and Cycles in Biological Interaction Graphs. BMC Bioinformatics 10, S. 181 (2009)
  49. 49.
    Klamt, S.; Haus, U.-U.; Theis, F.: Hypergraphs and cellular networks. PLoS Computational Biology 5 (5), S. e1000385 (2009)
  50. 50.
    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, S. 125 - 138 (2009)
  51. 51.
    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, S. 331 (2009)
  52. 52.
    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)
  53. 53.
    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)
  54. 54.
    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)
  55. 55.
    Haus, U.-U.; Klamt, S.; Stephen, T.: Computing knock-out strategies in metabolic networks. Journal of Computational Biology 15 (3), S. 259 - 268 (2008)
  56. 56.
    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)
  57. 57.
    Saez-Rodriguez, J.; Hammerle-Fickinger, A.; Dalal, O.; Klamt, S.; Gilles, E. D.; Conradi, C.: Multistability of signal transduction motifs. IET Systems Biology 2 (2), S. 80 - 93 (2008)
  58. 58.
    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)
  59. 59.
    Klamt, S.; Saez-Rodriguez, J.; Gilles, E. D.: Structural and functional analysis of cellular networks with CellNetAnalyzer. BMC Systems Biology 1, 2 (2007)
  60. 60.
    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)
  61. 61.
    Klamt, S.: Generalized concept of minimal cut sets in biochmical networks. Biosystems 83 (2-3 ), S. 233 - 247 (2006)
  62. 62.
    Klamt, S.; Saez-Rodriguez, J.; Lindquist, J.; Simeoni, L.; Gilles, E. D.: A methodology for the structural and functional analysis of signaling and regulatory networks. BMC Bioinformatics 7, S. 56 (2006)
  63. 63.
    Saez-Rodriguez, J.; Mirschel, S.; Hemenway, R.; Klamt, S.; Gilles, E. D.; Ginkel, M.: Visual set-up of logical models of signaling and regulatory networks with ProMoT. BMC Bioinformatics 7, 506 (2006)
  64. 64.
    Klamt, S.; Gagneur, J.; von Kamp, A.: Algorithmic approaches for computing elementary modes in large biochemical reaction networks. IEE Proceedings - Systems Biology 152 (4), S. 249 - 255 (2005)
  65. 65.
    Gagneur, J.; Klamt, S.: Computation of elementary modes: a unifying framework and the new binary approach. BMC Bioinformatics 5, 175 (2004)
  66. 66.
    Klamt, S.; Gilles, E. D.: Minimal cut sets in biochemical reaction networks. Bioinformatics 20, S. 226 - 234 (2004)
  67. 67.
    Kremling, A.; Ginkel, M.; Klamt, S.; Gilles, E. D.: Workbench zur Modellbildung, Simulation und Analyse zellulärer Systeme. it - Information Technology 46, S. 12 - 19 (2004)
  68. 68.
    Papin, J. A.; Stelling, J.; Price, N. D.; Klamt, S.; Schuster, S.; Palsson, B. O.: Comparison of network-based pathway analysis methods. Trends in Biotechnology 22 (8), S. 400 - 405 (2004)
  69. 69.
    Klamt, S.; Stelling, J.: Two approaches for metabolic pathway analysis? Trends in Biotechnology 21 (2), S. 64 - 69 (2003)
  70. 70.
    Klamt, S.; Stelling, J.; Ginkel, M.; Gilles, E. D.: FluxAnalyzer: exploring structure, pathways, and flux distributions in metabolic networks on interactive flux maps. Bioinformatics 19 (2), S. 261 - 269 (2003)
  71. 71.
    Klamt, S.; Schuster, S.; Gilles, E. D.: Calculability analysis in underdetermined metabolic networks illustrated by a model of the central metabolism in purple nonsulfur bacteria. Biotechnology and Bioengineering 77 (7), S. 734 - 751 (2002)
  72. 72.
    Klamt, S.; Stelling, J.: Combinatorial complexity of pathway analysis in metabolic networks. Molecular Biology Reports 29 (1-2), S. 233 - 236 (2002)
  73. 73.
    Klamt, S.; Schuster, S.: Calculating as many fluxes as possible in underdetermined metabolic networks. Molecular Biology Reports 29 (1-2), S. 243 - 248 (2002)
  74. 74.
    Schuster, S.; Klamt, S.; Weckwerth, W.; Moldenhauer, F.; Pfeiffer, T.: Use of network analysis of metabolic systems in bioengineering. Bioprocess and Biosystems Engineering 24 (6), S. 363 - 372 (2002)
  75. 75.
    Schuster, S.; Klamt, S.: Applying metabolic pathway analysis to make good use of methanol. Trends in Biotechnology 20 (8), S. 322 (2002)
  76. 76.
    Stelling, J.; Klamt, S.; Bettenbrock, K.; Schuster, S.; Gilles, E. D.: Metabolic network structure determines key aspects of functionality and regulation. Nature 420 (6912), S. 190 - 193 (2002)

Buchkapitel (3)

  1. 77.
    Klamt, S.; Haedicke, O.; von Kamp, A.: Stoichiometric and Constraint-Based Analysis of Biochemical Reaction Networks. In: Large-Scale Networks in Engineering and Life Sciences, S. 263 - 316. Springer International Publishing, Cham (2014)
  2. 78.
    Heise, S.; Flassig, R.; Klamt, S.: Benchmarking a Simple Yet Effective Approach for Inferring Gene Regulatory Networks from Systems Genetic Data. In: Gene Network Inference: Verification of Methods for Systems Genetic Data., S. 33 - 47. Springer, Heidelberg (2013)
  3. 79.
    Waddell, S. J.; Kamp von, A.; Klamt, S.; Neyrolles, O.: Host-pathogen interactions. In: Systems Biology of Tuberculosis, S. 107 - 126. Springer, New York (2013)

Konferenzbeitrag (3)

  1. 80.
    Ryll, A.; Bucher, J.; Niklas , J.; Klamt, S.: A fusion approach linking signaling logic and metabolic mass-flow kinetics in hepatocytes. In: Computational Methods in Systems Biology: 11th International Conference, CMSB 2013, S. 255 - 256. 11th International Conference on Computational Methods in Systems Biology CMSB 13, Klosterneuburg, Austria, 22. September 2013 - 24. September 2013. (2013)
  2. 81.
    Bohl, K.; Figueiredo, L.F.; Hädicke, O.; Klamt, S.; Kost, C.; Schuster, S.; Kaleta, C.: CASOP GS: Computing intervention strategies targeted at production improvement in genome-scale metabolic networks. In: German Conference on Bioinformatics 2010, S. 71 - 80 (Hg. Schomburg, D.; Grote, A.). German Conference on Bioinformatics 2010, Braunschweig, Germany, 20. September 2010 - 22. September 2010. (2010)
  3. 82.
    Klamt, S.; Kremling, A.; Gilles, E. D.: FluxAnalyzer: a graphical user interface for stoichiometric and quantitative analysis of metabolic networks. In: Computer applications in biotechnology 2001, S. 119 - 124 (Hg. Dochain, D.; Perrier, M.). 8th IFAC Internationall Conference on Computer Applications in Biotechnology, Québec, 24. Juni 2001 - 27. Juni 2001. (2002)

Vortrag (4)

  1. 83.
    Harder, B.-J.; Bettenbrock, K.; Klamt, S.: Model-based Metabolic Engineering of Escherichia coli for high yield itaconic acid production. Himmelfahrtstagung "Models for Developing and Optimising Biotech Production", Neu-Ulm, Germany (2017)
  2. 84.
    Harder, B.-J.; Bettenbrock, K.; Klamt, S.: Model-based Metabolic Engineering of Escherichia coli for high yield itaconic acid production. Systems Biology meets Synthetic Biology, Frankfurt am Main, Germany (2017)
  3. 85.
    Harder, B.-J.; Bettenbrock, K.; Klamt, S.: Model-based Metabolic Engineering of Escherichia coli for high yield itaconic acid production. Annual Conference 2016 of the Association for General and Applied Microbiology (VAAM), Jena, Germany (2016)
  4. 86.
    Koch, S.; Benndorf, D.; Reichl, U.; Klamt, S.: Predicting compositions of microbial communities from stoichiometric models with applications for the biogas process. Annual Conference 2016 of the Association for General and Applied Microbiology (VAAM), Jena, Germany (2016)

Poster (6)

  1. 87.
    Koch, S.; Benndorf, D.; Kohrs, F.; Lahmann, P.; Reichl, U.; Klamt, S.: Strategies for Modeling of Large-Scale Metabolic Models of Microbial Communities. Metabolic Pathway Analysis 2017, Bozeman, Montana, USA (2017)
  2. 88.
    Harder, B.-J.; Bettenbrock, K.; Klamt, S.: Model-Based Metabolic Engineering of Escherichia coli for High Yield Itaconic Acid Production. Metabolic Engineering 11, Kobe, Japan (2016)
  3. 89.
    Harder, B.-J.; Bettenbrock, K.; Klamt, S.: Model-Based Metabolic Engineering of Escherichia Coli for High Yield Itaconic Acid Production. 6th International Conference on Foundations of Systems Biology in Engineering, Magdeburg, Germany (2016)
  4. 90.
    Koch, S.; Benndorf, D.; Reichl, U.; Klamt, S.: New Methods for Modeling of Microbial Communities with Stoichiometric Metabolic Models. 6th International Conference on Foundations of Systems Biology in Engineering, Magdeburg, Germany (2016)
  5. 91.
    Koch, S.; Benndorf, D.; Reichl, U.; Klamt, S.: Prediction of Community Compositions in Stoichiometric Community Models. 4th Conference on Constraint-Based Reconstruction and Analysis, Heidelberg, Germany (2015)
  6. 92.
    Flassig, R.; Heise, S.; Sundmacher, K.; Klamt, S.: An effective Framework for Gene RegulatoryNetwork Reconstruction from Genetical Genomics Data. GCB 2012, Jena, Germany (2012)

Hochschulschrift - Dissertation (1)

  1. 93.
    Klamt, S.: Strukturelle Analyse von Stoffwechselnetzen illustriert am bakteriellen Redox- und Zentralstoffwechsel. Dissertation, 194 S., Shaker, Aachen (2005)

Sonderheft (1)

  1. 94.
    Schuster, S.; Kaleta, C.; Klamt, S.; Figueiredo de, L.F. (Hg.): Proceedings of the workshop "Integration of OMICs datasets into metabolic pathway analysis" Edingburgh, U.K., 15 October 2010 IOMPA 2010 (Sonderheft). Biosystems 105 (2 ) (2011)

Editorial (1)

  1. 95.
    Kaleta, C.; de Figueiredo, L. F.; Heiland, I.; Klamt, S.; Schuster, S.: Special Issue: Integration of OMICs datasets into Metabolic Pathway Analysis. Biosystems 105 (2), S. 107 - 108 (2011)
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