@article{BaslerEbenhoehSelbigetal.2011,
  author    = {Basler, Georg and Ebenhoeh, Oliver and Selbig, Joachim and Nikoloski, Zoran},
  title     = {Mass-balanced randomization of metabolic networks},
  series = {Bioinformatics},
  volume    = {27},
  journal   = {Bioinformatics},
  number    = {10},
  publisher = {Oxford Univ. Press},
  address   = {Oxford},
  issn      = {1367-4803},
  doi       = {10.1093/bioinformatics/btr145},
  pages     = {1397 -- 1403},
  year      = {2011},
  abstract  = {Motivation: Network-centered studies in systems biology attempt to integrate the topological properties of biological networks with experimental data in order to make predictions and posit hypotheses. For any topology-based prediction, it is necessary to first assess the significance of the analyzed property in a biologically meaningful context. Therefore, devising network null models, carefully tailored to the topological and biochemical constraints imposed on the network, remains an important computational problem. Results: We first review the shortcomings of the existing generic sampling scheme-switch randomization-and explain its unsuitability for application to metabolic networks. We then devise a novel polynomial-time algorithm for randomizing metabolic networks under the (bio)chemical constraint of mass balance. The tractability of our method follows from the concept of mass equivalence classes, defined on the representation of compounds in the vector space over chemical elements. We finally demonstrate the uniformity of the proposed method on seven genome-scale metabolic networks, and empirically validate the theoretical findings. The proposed method allows a biologically meaningful estimation of significance for metabolic network properties.},
  language  = {en}
}
@article{BaslerNikoloski2011,
  author    = {Basler, Georg and Nikoloski, Zoran},
  title     = {JMassBalance - mass-balanced randomization and analysis of metabolic networks},
  series = {Bioinformatics},
  volume    = {27},
  journal   = {Bioinformatics},
  number    = {19},
  publisher = {Oxford Univ. Press},
  address   = {Oxford},
  issn      = {1367-4803},
  doi       = {10.1093/bioinformatics/btr448},
  pages     = {2761 -- 2762},
  year      = {2011},
  abstract  = {Analysis of biological networks requires assessing the statistical significance of network-based predictions by using a realistic null model. However, the existing network null model, switch randomization, is unsuitable for metabolic networks, as it does not include physical constraints and generates unrealistic reactions. We present JMassBalance, a tool for mass-balanced randomization and analysis of metabolic networks. The tool allows efficient generation of large sets of randomized networks under the physical constraint of mass balance. In addition, various structural properties of the original and randomized networks can be calculated, facilitating the identification of the salient properties of metabolic networks with a biologically meaningful null model.},
  language  = {en}
}