@misc{SzymanskiJozefczukNikoloskietal.2009,
  author    = {Szymanski, Jedrzej and Jozefczuk, Szymon and Nikoloski, Zoran and Selbig, Joachim and Nikiforova, Victoria and Catchpole, Gareth and Willmitzer, Lothar},
  title     = {Stability of metabolic correlations under changing environmental conditions in Escherichia coli : a systems approach},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-45253},
  year      = {2009},
  abstract  = {Background: Biological systems adapt to changing environments by reorganizing their cellula r and physiological program with metabolites representing one important response level. Different stresses lead to both conserved and specific responses on the metabolite level which should be reflected in the underl ying metabolic network. Methodology/Principal Findings: Starting from experimental data obtained by a GC-MS based high-throughput metabolic profiling technology we here develop an approach that: (1) extracts network representations from metabolic conditiondependent data by using pairwise correlations, (2) determines the sets of stable and condition-dependent correlations based on a combination of statistical significance and homogeneity tests, and (3) can identify metabolites related to the stress response, which goes beyond simple ob servation s about the changes of metabolic concentrations. The approach was tested with Escherichia colias a model organism observed under four different environmental stress conditions (cold stress, heat stress, oxidative stress, lactose diau xie) and control unperturbed conditions. By constructing the stable network component, which displays a scale free topology and small-world characteristics, we demonstrated that: (1) metabolite hubs in this reconstructed correlation networks are significantly enriched for those contained in biochemical networks such as EcoCyc, (2) particular components of the stable network are enriched for functionally related biochemical path ways, and (3) ind ependently of the response scale, based on their importance in the reorganization of the cor relation network a set of metabolites can be identified which represent hypothetical candidates for adjusting to a stress-specific response. Conclusions/Significance: Network-based tools allowed the identification of stress-dependent and general metabolic correlation networks. This correlation-network-ba sed approach does not rely on major changes in concentration to identify metabolites important for st ress adaptation, but rather on the changes in network properties with respect to metabolites. This should represent a useful complementary technique in addition to more classical approaches.},
  language  = {en}
}
@article{JuerchottGuoCatchpoleetal.2011,
  author    = {Juerchott, Kathrin and Guo, Ke-Tai and Catchpole, Gareth and Feher, Kristen and Willmitzer, Lothar and Schichor, Christian and Selbig, Joachim},
  title     = {Comparison of metabolite profiles in U87 glioma cells and mesenchymal stem cells},
  series = {Biosystems : journal of biological and information processing sciences},
  volume    = {105},
  journal   = {Biosystems : journal of biological and information processing sciences},
  number    = {2},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0303-2647},
  doi       = {10.1016/j.biosystems.2011.05.005},
  pages     = {130 -- 139},
  year      = {2011},
  abstract  = {Gas chromatography-mass spectrometry (GC-MS) profiles were generated from U87 glioma cells and human mesenchymal stem cells (hMSC). 37 metabolites representing glycolysis intermediates, TCA cycle metabolites, amino acids and lipids were selected for a detailed analysis. The concentrations of these. metabolites were compared and Pearson correlation coefficients were used to calculate the relationship between pairs of metabolites. Metabolite profiles and correlation patterns differ significantly between the two cell lines. These profiles can be considered as a signature of the underlying biochemical system and provide snap-shots of the metabolism in mesenchymal stem cells and tumor cells.},
  language  = {en}
}
@article{CatchpolePlatzerWeikertetal.2011,
  author    = {Catchpole, Gareth and Platzer, Alexander and Weikert, Cornelia and Kempkensteffen, Carsten and Johannsen, Manfred and Krause, Hans and Jung, Klaus and Miller, Kurt and Willmitzer, Lothar and Selbig, Joachim and Weikert, Steffen},
  title     = {Metabolic profiling reveals key metabolic features of renal cell carcinoma},
  series = {Journal of cellular and molecular medicine : a journal of translational medicine},
  volume    = {15},
  journal   = {Journal of cellular and molecular medicine : a journal of translational medicine},
  number    = {1},
  publisher = {Wiley-Blackwell},
  address   = {Malden},
  issn      = {1582-1838},
  doi       = {10.1111/j.1582-4934.2009.00939.x},
  pages     = {109 -- 118},
  year      = {2011},
  abstract  = {Recent evidence suggests that metabolic changes play a pivotal role in the biology of cancer and in particular renal cell carcinoma (RCC). Here, a global metabolite profiling approach was applied to characterize the metabolite pool of RCC and normal renal tissue. Advanced decision tree models were applied to characterize the metabolic signature of RCC and to explore features of metastasized tumours. The findings were validated in a second independent dataset. Vitamin E derivates and metabolites of glucose, fatty acid, and inositol phosphate metabolism determined the metabolic profile of RCC. alpha-tocopherol, hippuric acid, myoinositol, fructose-1-phosphate and glucose-1-phosphate contributed most to the tumour/normal discrimination and all showed pronounced concentration changes in RCC. The identified metabolic profile was characterized by a low recognition error of only 5\% for tumour versus normal samples. Data on metastasized tumours suggested a key role for metabolic pathways involving arachidonic acid, free fatty acids, proline, uracil and the tricarboxylic acid cycle. These results illustrate the potential of mass spectroscopy based metabolomics in conjunction with sophisticated data analysis methods to uncover the metabolic phenotype of cancer. Differentially regulated metabolites, such as vitamin E compounds, hippuric acid and myoinositol, provide leads for the characterization of novel pathways in RCC.},
  language  = {en}
}