Edda Klipp

Summary

Affiliation: Max Planck Institute for Molecular Genetics
Country: Germany

Publications

  1. ncbi request reprint Insights into the network controlling the G1/S transition in budding yeast
    Matteo Barberis
    Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany
    Genome Inform 18:85-99. 2007
  2. pmc Towards a systems biology approach to mammalian cell cycle: modeling the entrance into S phase of quiescent fibroblasts after serum stimulation
    Roberta Alfieri
    Institute for Biomedical Technology Consiglio Nazionale delle Ricerche, Via Fratelli Cervi 93, Segrate, Milan, Italy
    BMC Bioinformatics 10:S16. 2009
  3. pmc Mathematical modeling of intracellular signaling pathways
    Edda Klipp
    Max Planck Institute for Molecular Genetics, Ihnestr 73, 14195 Berlin, Germany
    BMC Neurosci 7:S10. 2006
  4. ncbi request reprint Integrative model of the response of yeast to osmotic shock
    Edda Klipp
    Berlin Center for Genome Based Bioinformatics, Max Planck Institute for Molecular Genetics, Dept Vertebrate Genomics, Ihnestr 73, 14195 Berlin, Germany
    Nat Biotechnol 23:975-82. 2005
  5. ncbi request reprint Inferring dynamic properties of biochemical reaction networks from structural knowledge
    Edda Klipp
    Berlin Center for Genome Based Bioinformatics BCB, Max Planck Institute for Molecular Genetics, Dept Vertebrate Genomics, Ihnestr 73, Berlin 14195, Germany
    Genome Inform 15:125-37. 2004
  6. ncbi request reprint Modelling dynamic processes in yeast
    Edda Klipp
    Max Planck Institute for Molecular Genetics, Computational Systems Biology, Ihnestrasse 63 73, 14195 Berlin, Germany
    Yeast 24:943-59. 2007
  7. ncbi request reprint Exploring the impact of osmoadaptation on glycolysis using time-varying response-coefficients
    Clemens Kühn
    Computational Systems Biology Group, Max Planck Institute for Molecular Genetics, Ihnestrasse 63 73, 14195 Berlin, Germany
    Genome Inform 20:77-90. 2008
  8. ncbi request reprint Automatically generated model of a metabolic network
    Simon Borger
    Max Planck Institute for Molecular Genetics, Berlin, Germany
    Genome Inform 18:215-24. 2007
  9. ncbi request reprint ModelMage: a tool for automatic model generation, selection and management
    Max Flöttmann
    Max Planck Institute for Molecular Genetics, Ihnestr 63 73, 14195 Berlin, Germany
    Genome Inform 20:52-63. 2008
  10. pmc Dynamic rerouting of the carbohydrate flux is key to counteracting oxidative stress
    Markus Ralser
    Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany
    J Biol 6:10. 2007

Detail Information

Publications39

  1. ncbi request reprint Insights into the network controlling the G1/S transition in budding yeast
    Matteo Barberis
    Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany
    Genome Inform 18:85-99. 2007
    ..The sensitivity analysis of the influence that the kinetic parameters of the G1/S transition model have on the setting of the P(s) value is also reported...
  2. pmc Towards a systems biology approach to mammalian cell cycle: modeling the entrance into S phase of quiescent fibroblasts after serum stimulation
    Roberta Alfieri
    Institute for Biomedical Technology Consiglio Nazionale delle Ricerche, Via Fratelli Cervi 93, Segrate, Milan, Italy
    BMC Bioinformatics 10:S16. 2009
    ....
  3. pmc Mathematical modeling of intracellular signaling pathways
    Edda Klipp
    Max Planck Institute for Molecular Genetics, Ihnestr 73, 14195 Berlin, Germany
    BMC Neurosci 7:S10. 2006
    ..Focusing on the close interplay between experimental investigation of pathways and the mathematical representations of cellular dynamics, we discuss challenges and perspectives that emerge in studies of signaling systems...
  4. ncbi request reprint Integrative model of the response of yeast to osmotic shock
    Edda Klipp
    Berlin Center for Genome Based Bioinformatics, Max Planck Institute for Molecular Genetics, Dept Vertebrate Genomics, Ihnestr 73, 14195 Berlin, Germany
    Nat Biotechnol 23:975-82. 2005
    ..It also serves as a starting point for a comprehensive description of cellular signaling...
  5. ncbi request reprint Inferring dynamic properties of biochemical reaction networks from structural knowledge
    Edda Klipp
    Berlin Center for Genome Based Bioinformatics BCB, Max Planck Institute for Molecular Genetics, Dept Vertebrate Genomics, Ihnestr 73, Berlin 14195, Germany
    Genome Inform 15:125-37. 2004
    ..This analysis reveals how much information about dynamic behavior can be drawn from structural knowledge...
  6. ncbi request reprint Modelling dynamic processes in yeast
    Edda Klipp
    Max Planck Institute for Molecular Genetics, Computational Systems Biology, Ihnestrasse 63 73, 14195 Berlin, Germany
    Yeast 24:943-59. 2007
    ..Therefore, yeast-related data are often used to develop and examine computational approaches and modelling methods...
  7. ncbi request reprint Exploring the impact of osmoadaptation on glycolysis using time-varying response-coefficients
    Clemens Kühn
    Computational Systems Biology Group, Max Planck Institute for Molecular Genetics, Ihnestrasse 63 73, 14195 Berlin, Germany
    Genome Inform 20:77-90. 2008
    ..Existing experimental data and a detailed analysis of the model lead to the suggestion of an interaction between activated Hog1 and activators of glycolysis such as Pfk26...
  8. ncbi request reprint Automatically generated model of a metabolic network
    Simon Borger
    Max Planck Institute for Molecular Genetics, Berlin, Germany
    Genome Inform 18:215-24. 2007
    ..Last, the kinetics of the reactions are assigned parameters. The resulting model in SBML format can be fed into standard simulation tools. The approach is applied to the sulphur-glutathione-pathway in Saccharomyces cerevisiae...
  9. ncbi request reprint ModelMage: a tool for automatic model generation, selection and management
    Max Flöttmann
    Max Planck Institute for Molecular Genetics, Ihnestr 63 73, 14195 Berlin, Germany
    Genome Inform 20:52-63. 2008
    ..Thus, all simulation and optimization features of COPASI are readily incorporated. ModelMage can be downloaded from http://sysbio.molgen.mpg.de/modelmage and is distributed as free software...
  10. pmc Dynamic rerouting of the carbohydrate flux is key to counteracting oxidative stress
    Markus Ralser
    Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany
    J Biol 6:10. 2007
    ..Eukaryotic cells have evolved various response mechanisms to counteract the deleterious consequences of oxidative stress. Among these processes, metabolic alterations seem to play an important role...
  11. pmc SBML-SAT: a systems biology markup language (SBML) based sensitivity analysis tool
    Zhike Zi
    Computational Systems Biology, Max Planck Institute for Molecular Genetics, Ihnestr, 73, 14195 Berlin, Germany
    BMC Bioinformatics 9:342. 2008
    ..However, current SBML compatible software tools are limited in their ability to perform global sensitivity analyses of these models...
  12. pmc Bringing metabolic networks to life: integration of kinetic, metabolic, and proteomic data
    Wolfram Liebermeister
    Computational Systems Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany
    Theor Biol Med Model 3:42. 2006
    ..In Bayesian parameter estimation, model parameters are described by a posterior probability distribution, which scores the potential parameter sets, showing how well each of them agrees with the data and with the prior assumptions made...
  13. ncbi request reprint Exploratory simulation of cell ageing using hierarchical models
    Marija Cvijovic
    Max Planck Institute for Molecular Genetics, Berlin, Germany
    Genome Inform 21:114-25. 2008
    ..The combination of a single-cell model and a simulation platform permitting parallel composition and dynamic node creation has proved to be an efficient tool for in silico exploration of cell behavior...
  14. ncbi request reprint Exploring the effect of variable enzyme concentrations in a kinetic model of yeast glycolysis
    József Bruck
    Max Planck Institute for Molecular Genetics, Ihnestr 63 73, 14195 Berlin, Germany
    Genome Inform 20:1-14. 2008
    ..We were partly able to reproduce the experimental data and present a number of changes that were necessary to improve the modeling result...
  15. ncbi request reprint Prediction of enzyme kinetic parameters based on statistical learning
    Simon Borger
    Max Planck Institute for Molecular Genetics, Ihnestrasse 63 73, 14195 Berlin, Germany
    Genome Inform 17:80-7. 2006
    ..16. The method is applicable to other types of kinetic parameters for which many experimental data are available...
  16. pmc Monte Carlo analysis of an ODE Model of the Sea Urchin Endomesoderm Network
    Clemens Kühn
    Max Planck Institute for Molecular Genetics, Ihnestr 63 73, 14195 Berlin, Germany
    BMC Syst Biol 3:83. 2009
    ..Parameter estimation for each node is often infeasible for very large GRNs. We propose a method, based on random parameter estimations through Monte-Carlo simulations to measure completeness grades of GRNs...
  17. ncbi request reprint SBMLmerge, a system for combining biochemical network models
    Marvin Schulz
    Max Planck Institute for Molecular Genetics, Ihnestrasse 63 73, 14195 Berlin, Germany
    Genome Inform 17:62-71. 2006
    ..If the input models make contradicting statements about a biochemical quantity, the user is asked to choose between them. In the end the merging process results in a new, valid SBML model...
  18. ncbi request reprint Graphical analysis and experimental evaluation of Saccharomyces cerevisiae PTRK1|2 and PBMH1|2 promoter region
    Susanne Gerber
    Group of Theoretical Biophysics, MNF I, Humboldt University of Berlin, Invalidenstrasse 42, 10115 Berlin, Germany
    Genome Inform 22:11-20. 2010
    ..Upon phenotypic testing one TF mutant exhibited severely impaired growth under non-permissive conditions. This TF, Mot3p was identified as of most abundant potential binding sites and distinctive patterns among the TRK promoter regions...
  19. ncbi request reprint Alternative pathways as mechanism for the negative effects associated with overexpression of superoxide dismutase
    Axel Kowald
    Kinetic Modelling Group, Max Planck Institute for Molecular Genetics, Ihnestr 73, 14195 Berlin, Germany
    J Theor Biol 238:828-40. 2006
    ..But furthermore we identified an additional mechanism that is of more general nature and might be a common basis for the experimental findings. We call it the alternative pathway mechanism...
  20. ncbi request reprint Alternative pathways might mediate toxicity of high concentrations of superoxide dismutase
    Axel Kowald
    Kinetic Modelling Group, Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany
    Ann N Y Acad Sci 1019:370-4. 2004
    ..We therefore think that it might be the common mechanism for the detrimental effects seen in cells and organisms with increased levels of the different forms of superoxide dismutase...
  21. ncbi request reprint Modeling development: spikes of the sea urchin
    Clemens Kühn
    Max Planck Institute for Molecular Genetics, Ihnestr 63 73, 14195 Berlin, Germany
    Genome Inform 18:75-84. 2007
    ..Although the resulting model is capable of reproducing fractions of the experimental data, it falls short of reproducing specification of cell types. These findings can facilitate the refinement of the Endomesoderm Network...
  22. ncbi request reprint A systems biology approach: modelling of Aquaporin-2 trafficking
    Martina Fröhlich
    Max Planck Institute for Molecular Genetics, Berlin, Germany
    Genome Inform 24:42-55. 2010
    ..We predict the time courses for membrane located AQP2 at different vasopressin concentrations, compare them with newly generated data and discuss the competencies of the model...
  23. ncbi request reprint Biochemical network models simplified by balanced truncation
    Wolfram Liebermeister
    Max Planck Institute for Molecular Genetics, Berlin, Germany
    FEBS J 272:4034-43. 2005
    ..The speed-up in computation gained by model reduction may become vital for parameter estimation in large cell models...
  24. ncbi request reprint Steady state analysis of signal response in receptor trafficking networks
    Zhike Zi
    Max Planck Institute for Molecular Genetics, Ihnestr 73, 14195 Berlin, Germany
    Genome Inform 18:100-8. 2007
    ..Furthermore, the steady state analysis demonstrates that classic Scatchard plot analysis is still valid for the steady state of the complicated receptor trafficking network...
  25. pmc Constraint-based modeling and kinetic analysis of the Smad dependent TGF-beta signaling pathway
    Zhike Zi
    Computational Systems Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany
    PLoS ONE 2:e936. 2007
    ..In this study, we aim at using systems biology approach to provide dynamic analysis on this pathway...
  26. ncbi request reprint Drug-efficacy depends on the inhibitor type and the target position in a metabolic network--a systematic study
    Susanne Gerber
    Max Planck Institute for Molecular Genetics, Computational Systems Biology, Berlin, Germany
    J Theor Biol 252:442-55. 2008
    ..This work is devoted to the memorial of our teacher Reinhart Heinrich, who made important contributions to the investigation of the regulation of metabolic networks, namely by introducing and applying the concept of metabolic control...
  27. pmc Onset of immune senescence defined by unbiased pyrosequencing of human immunoglobulin mRNA repertoires
    Florian Rubelt
    Max Planck Institute for Molecular Genetics, Berlin, Germany
    PLoS ONE 7:e49774. 2012
    ..The observed age-dependent reduction of CSR ability proposes a feasible explanation why reduced efficacy of vaccination is seen in the elderly and implies that novel vaccine strategies for the elderly should include the "Golden Agers"...
  28. pmc Bringing metabolic networks to life: convenience rate law and thermodynamic constraints
    Wolfram Liebermeister
    Computational Systems Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany
    Theor Biol Med Model 3:41. 2006
    ..The mathematical expressions depend on the underlying enzymatic mechanism; they can become quite involved and may contain a large number of parameters. Rate laws and enzyme parameters are still unknown for most enzymes...
  29. doi request reprint Short-term volume and turgor regulation in yeast
    Jörg Schaber
    Max Planck Institute for Molecular Genetics, Computational Systems Biology, Ihnestr 63 73, 14195 Berlin, Germany
    Essays Biochem 45:147-59. 2008
    ..Finally, we demonstrate the validity of the presented approach by fitting the dynamic model to a time course of volume change upon osmotic shock in yeast...
  30. ncbi request reprint SBML-PET: a Systems Biology Markup Language-based parameter estimation tool
    Zhike Zi
    Max Planck Institute for Molecular Genetics, Ihnestr 73 14195 Berlin, Germany
    Bioinformatics 22:2704-5. 2006
    ..Stochastic Ranking Evolution Strategy (SRES) is incorporated in SBML-PET for parameter estimation jobs. A classic ODE Solver called ODEPACK is used to solve the Ordinary Differential Equation (ODE) system...
  31. ncbi request reprint Cellular signaling is potentially regulated by cell density in receptor trafficking networks
    Zhike Zi
    Computational Systems Biology, Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany
    FEBS Lett 581:4589-95. 2007
    ..Furthermore, cell density also affects the robustness of dose-response curve upon the variation of binding affinity...
  32. ncbi request reprint A modelling approach to quantify dynamic crosstalk between the pheromone and the starvation pathway in baker's yeast
    Jörg Schaber
    Max Planck Institute for Molecular Genetics, Berlin, Germany
    FEBS J 273:3520-33. 2006
    ..Studying signals that are transmitted in parallel gives us new insights about how pathways and signals interact in a dynamical way, e.g., whether they amplify, inhibit, delay or accelerate each other...
  33. ncbi request reprint A theory of optimal differential gene expression
    Wolfram Liebermeister
    Berlin Center for Genome Based Bioinformatics, Max Planck Institute for Molecular Genetics, Berlin, Germany
    Biosystems 76:261-78. 2004
    ..Where the optimality assumption is valid, our results justify the use of expression data for functional annotation and for pathway reconstruction and suggest the use of linear factor models for the analysis of gene expression data...
  34. ncbi request reprint Finding kinetic parameters using text mining
    Jörg Hakenberg
    Humboldt Universitat zu Berlin, Department of Computer Science, Berlin, Germany
    OMICS 8:131-52. 2004
    ..We evaluated our approach on a manually tagged corpus of 800 documents and found that it outperforms keyword searches in abstracts by a factor of five in terms of precision...
  35. ncbi request reprint Modelling the dynamics of the yeast pheromone pathway
    Bente Kofahl
    Humboldt University Berlin, Theoretical Biophysics, Invalidenstrasse 43, 10115 Berlin, Germany
    Yeast 21:831-50. 2004
    ..Furthermore, we can explain the phenotype of more than a dozen well-characterized mutants and also the graded response of yeast cells to varying concentrations of the stimulating pheromone...
  36. ncbi request reprint Minimum information requested in the annotation of biochemical models (MIRIAM)
    Nicolas Le Novère
    European Bioinformatics Institute, Hinxton, CB10 1SD, UK
    Nat Biotechnol 23:1509-15. 2005
    ....
  37. pmc Cell size at S phase initiation: an emergent property of the G1/S network
    Matteo Barberis
    Department of Biotechnology and Biosciences, University of Milano Bicocca, Milan, Italy
    PLoS Comput Biol 3:e64. 2007
    ..Sensitivity analysis of PS provides a novel relevant conclusion: PS is an emergent property of the G1 to S network that strongly depends on growth rate...
  38. ncbi request reprint Systems biology standards--the community speaks
    Edda Klipp
    Nat Biotechnol 25:390-1. 2007
  39. ncbi request reprint Prediction of temporal gene expression. Metabolic opimization by re-distribution of enzyme activities
    Edda Klipp
    Max Planck Institute of Molecular Genetics, Berlin, Germany
    Eur J Biochem 269:5406-13. 2002
    ..These enzyme profiles are in close correlation with observed gene expression data. Our results demonstrate that optimality principles help to rationalize observed gene expression profiles...