W Liebermeister

Summary

Affiliation: Max Planck Institute for Molecular Genetics
Country: Germany

Publications

  1. ncbi Linear modes of gene expression determined by independent component analysis
    Wolfram Liebermeister
    Theoretische Biophysik, Institut fur Biologie, Humboldt Universitat zu Berlin, Invalidenstrasse 42, 10115 Berlin, Germany
    Bioinformatics 18:51-60. 2002
  2. ncbi Mathematical modeling of intracellular signaling pathways
    Edda Klipp
    Max Planck Institute for Molecular Genetics, Ihnestr 73, 14195 Berlin, Germany
    BMC Neurosci 7:S10. 2006
  3. ncbi 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
  4. ncbi 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
  5. ncbi 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
  6. ncbi Response to temporal parameter fluctuations in biochemical networks
    Wolfram Liebermeister
    Berlin Center for Genome Based Bioinformatics, Max Planck Institute for Molecular Genetics, Ihnestr 73, 14195 Berlin, Germany
    J Theor Biol 234:423-38. 2005
  7. ncbi Biochemical network models simplified by balanced truncation
    Wolfram Liebermeister
    Max Planck Institute for Molecular Genetics, Berlin, Germany
    FEBS J 272:4034-43. 2005
  8. ncbi Predicting physiological concentrations of metabolites from their molecular structure
    Wolfram Liebermeister
    Max Planck Institute for Molecular Genetics, Kinetic Modelling Group, Berlin, Germany
    J Comput Biol 12:1307-15. 2005
  9. ncbi Biochemical networks with uncertain parameters
    W Liebermeister
    Max Planck Institute for Molecular Genetics, Berlin, Germany
    Syst Biol (Stevenage) 152:97-107. 2005
  10. ncbi Nested uncertainties in biochemical models
    J Schaber
    Humboldt University Berlin, Institute for Biology, Theoretical Biophysics, Berlin, Germany
    IET Syst Biol 3:1-9. 2009

Detail Information

Publications20

  1. ncbi Linear modes of gene expression determined by independent component analysis
    Wolfram Liebermeister
    Theoretische Biophysik, Institut fur Biologie, Humboldt Universitat zu Berlin, Invalidenstrasse 42, 10115 Berlin, Germany
    Bioinformatics 18:51-60. 2002
    ..A projection to expression modes helps to highlight particular biological functions, to reduce noise, and to compress the data in a biologically sensible way...
  2. ncbi 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...
  3. ncbi 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...
  4. ncbi 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...
  5. ncbi 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...
  6. ncbi Response to temporal parameter fluctuations in biochemical networks
    Wolfram Liebermeister
    Berlin Center for Genome Based Bioinformatics, Max Planck Institute for Molecular Genetics, Ihnestr 73, 14195 Berlin, Germany
    J Theor Biol 234:423-38. 2005
    ..The temporal response to small parameter fluctuations can be computed by Fourier synthesis. For a model of glycolysis, this approximation remains fairly accurate even for large relative fluctuations of the parameters...
  7. ncbi 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...
  8. ncbi Predicting physiological concentrations of metabolites from their molecular structure
    Wolfram Liebermeister
    Max Planck Institute for Molecular Genetics, Kinetic Modelling Group, Berlin, Germany
    J Comput Biol 12:1307-15. 2005
    ..The model explains about 22% of the variance of the logarithmic mean concentrations...
  9. ncbi Biochemical networks with uncertain parameters
    W Liebermeister
    Max Planck Institute for Molecular Genetics, Berlin, Germany
    Syst Biol (Stevenage) 152:97-107. 2005
    ..It also shows clearly how the variability of biological systems is related to the metabolic response coefficients...
  10. ncbi Nested uncertainties in biochemical models
    J Schaber
    Humboldt University Berlin, Institute for Biology, Theoretical Biophysics, Berlin, Germany
    IET Syst Biol 3:1-9. 2009
    ..Here, the authors review some issues arising from such uncertainties and sketch methods, solutions and future directions to deal with them...
  11. ncbi 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...
  12. ncbi 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...
  13. ncbi 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...
  14. ncbi 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...
  15. ncbi 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...
  16. ncbi Modular rate laws for enzymatic reactions: thermodynamics, elasticities and implementation
    Wolfram Liebermeister
    Institut fur Biologie, Theoretische Biophysik, Humboldt Universitat zu Berlin, Berlin, Germany
    Bioinformatics 26:1528-34. 2010
    ..At the same time, they need to respect thermodynamic relations between the kinetic constants and the metabolic fluxes and concentrations...
  17. ncbi Integration of enzyme kinetic data from various sources
    Simon Borger
    Computational Systems Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany
    In Silico Biol 7:S73-9. 2007
    ..All methods have been assembled into a workflow that facilitates the integration of biochemical data and the modelling of metabolic networks from scratch...
  18. ncbi Systems biology standards--the community speaks
    Edda Klipp
    Nat Biotechnol 25:390-1. 2007
  19. ncbi A comprehensive library of fluorescent transcriptional reporters for Escherichia coli
    Alon Zaslaver
    Department of Molecular Cell Biology, Weizmann Institute of Science, 76100, Israel
    Nat Methods 3:623-8. 2006
    ..coli, including putative internal promoters within previously known operons, such as the lac operon. This library can serve as a tool for accurate, high-resolution analysis of transcription networks in living E. coli cells...
  20. ncbi Does mapping reveal correlation between gene expression and protein-protein interaction?
    Ralf Mrowka
    Nat Genet 33:15-6; author reply 16-7. 2003