@misc{HischeLarhlimiSchwarzetal.2012,
  author    = {Hische, Manuela and Larhlimi, Abdelhalim and Schwarz, Franziska and Fischer-Rosinsk{\´y}, Antje and Bobbert, Thomas and Assmann, Anke and Catchpole, Gareth S. and Pfeiffer, Andreas F. H. and Willmitzer, Lothar and Selbig, Joachim and Spranger, Joachim},
  title     = {A distinct metabolic signature predictsdevelopment of fasting plasma glucose},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch Naturwissenschaftliche Reihe},
  number    = {850},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-42740},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-427400},
  pages     = {12},
  year      = {2012},
  abstract  = {Background High blood glucose and diabetes are amongst the conditions causing the greatest losses in years of healthy life worldwide. Therefore, numerous studies aim to identify reliable risk markers for development of impaired glucose metabolism and type 2 diabetes. However, the molecular basis of impaired glucose metabolism is so far insufficiently understood. The development of so called 'omics' approaches in the recent years promises to identify molecular markers and to further understand the molecular basis of impaired glucose metabolism and type 2 diabetes. Although univariate statistical approaches are often applied, we demonstrate here that the application of multivariate statistical approaches is highly recommended to fully capture the complexity of data gained using high-throughput methods. Methods We took blood plasma samples from 172 subjects who participated in the prospective Metabolic Syndrome Berlin Potsdam follow-up study (MESY-BEPO Follow-up). We analysed these samples using Gas Chromatography coupled with Mass Spectrometry (GC-MS), and measured 286 metabolites. Furthermore, fasting glucose levels were measured using standard methods at baseline, and after an average of six years. We did correlation analysis and built linear regression models as well as Random Forest regression models to identify metabolites that predict the development of fasting glucose in our cohort. Results We found a metabolic pattern consisting of nine metabolites that predicted fasting glucose development with an accuracy of 0.47 in tenfold cross-validation using Random Forest regression. We also showed that adding established risk markers did not improve the model accuracy. However, external validation is eventually desirable. Although not all metabolites belonging to the final pattern are identified yet, the pattern directs attention to amino acid metabolism, energy metabolism and redox homeostasis. Conclusions We demonstrate that metabolites identified using a high-throughput method (GC-MS) perform well in predicting the development of fasting plasma glucose over several years. Notably, not single, but a complex pattern of metabolites propels the prediction and therefore reflects the complexity of the underlying molecular mechanisms. This result could only be captured by application of multivariate statistical approaches. Therefore, we highly recommend the usage of statistical methods that seize the complexity of the information given by high-throughput methods.},
  language  = {en}
}
@article{DavidMarashiLarhlimietal.2011,
  author    = {David, Laszlo and Marashi, Sayed-Amir and Larhlimi, Abdelhalim and Mieth, Bettina and Bockmayr, Alexander},
  title     = {FFCA a feasibility-based method for flux coupling analysis of metabolic networks},
  series = {BMC bioinformatics},
  volume    = {12},
  journal   = {BMC bioinformatics},
  number    = {12},
  publisher = {BioMed Central},
  address   = {London},
  issn      = {1471-2105},
  doi       = {10.1186/1471-2105-12-236},
  pages     = {7},
  year      = {2011},
  abstract  = {Background: Flux coupling analysis (FCA) is a useful method for finding dependencies between fluxes of a metabolic network at steady-state. FCA classifies reactions into subsets (called coupled reaction sets) in which activity of one reaction implies activity of another reaction. Several approaches for FCA have been proposed in the literature. Results: We introduce a new FCA algorithm, FFCA (Feasibility-based Flux Coupling Analysis), which is based on checking the feasibility of a system of linear inequalities. We show on a set of benchmarks that for genome-scale networks FFCA is faster than other existing FCA methods. Conclusions: We present FFCA as a new method for flux coupling analysis and prove it to be faster than existing approaches. A corresponding software tool is freely available for non-commercial use at http://www.bioinformatics.org/ffca/.},
  language  = {en}
}
@article{LarhlimiDavidSelbigetal.2012,
  author    = {Larhlimi, Abdelhalim and David, Laszlo and Selbig, Joachim and Bockmayr, Alexander},
  title     = {F2C2: a fast tool for the computation of flux coupling in genome-scale metabolic networks},
  series = {BMC bioinformatics},
  volume    = {13},
  journal   = {BMC bioinformatics},
  publisher = {BioMed Central},
  address   = {London},
  issn      = {1471-2105},
  doi       = {10.1186/10.1186/1471-2105-13-57},
  pages     = {9},
  year      = {2012},
  abstract  = {Background: Flux coupling analysis (FCA) has become a useful tool in the constraint-based analysis of genome-scale metabolic networks. FCA allows detecting dependencies between reaction fluxes of metabolic networks at steady-state. On the one hand, this can help in the curation of reconstructed metabolic networks by verifying whether the coupling between reactions is in agreement with the experimental findings. On the other hand, FCA can aid in defining intervention strategies to knock out target reactions. Results: We present a new method F2C2 for FCA, which is orders of magnitude faster than previous approaches. As a consequence, FCA of genome-scale metabolic networks can now be performed in a routine manner. Conclusions: We propose F2C2 as a fast tool for the computation of flux coupling in genome-scale metabolic networks. F2C2 is freely available for non-commercial use at https://sourceforge.net/projects/f2c2/files/.},
  language  = {en}
}
@misc{LarhlimiDavidSelbigetal.2012,
  author    = {Larhlimi, Abdelhalim and David, Laszlo and Selbig, Joachim and Bockmayr, Alexander},
  title     = {F2C2},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {921},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-43243},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-432431},
  pages     = {11},
  year      = {2012},
  abstract  = {Background: Flux coupling analysis (FCA) has become a useful tool in the constraint-based analysis of genome-scale metabolic networks. FCA allows detecting dependencies between reaction fluxes of metabolic networks at steady-state. On the one hand, this can help in the curation of reconstructed metabolic networks by verifying whether the coupling between reactions is in agreement with the experimental findings. On the other hand, FCA can aid in defining intervention strategies to knock out target reactions. Results: We present a new method F2C2 for FCA, which is orders of magnitude faster than previous approaches. As a consequence, FCA of genome-scale metabolic networks can now be performed in a routine manner. Conclusions: We propose F2C2 as a fast tool for the computation of flux coupling in genome-scale metabolic networks. F2C2 is freely available for non-commercial use at https://sourceforge.net/projects/f2c2/files/.},
  language  = {en}
}