@article{MorgenthalWeckwerthSteuer2006,
  author    = {Morgenthal, Katja and Weckwerth, Wolfram and Steuer, Ralf},
  title     = {Metabolomic networks in plants : transitions from pattern recognition to biological interpretation},
  doi       = {10.1016/j.biosystems.2005.05.017},
  year      = {2006},
  abstract  = {Nowadays techniques for non-targeted metabolite profiling allow for the generation of huge amounts of relevant data essential for the construction of dynamic metabolomic networks. Thus, metabolomics, besides transcriptomics or proteomics, provides a major tool for the characterization of postgenomic processes. In this work, we introduce comparative correlation analysis as a complementary approach to characterize the physiological states of various organs of diverse plant species with focus on specific participation of metabolites in different reaction networks. The correlations observed are induced by diminutive fluctuations in environmental conditions, which propagate through the system and induce specific patterns depending on the genomic background. In order to examine this hypothesis, numeric examples of such fluctuations are computed and compared with experimentally obtained metabolite data.},
  language  = {en}
}
@article{SteuerGrossSelbigetal.2006,
  author    = {Steuer, Ralf and Gross, Thilo and Selbig, Joachim and Blasius, Bernd},
  title     = {Structural kinetic modeling of metabolic networks},
  series = {Proceedings of the National Academy of Sciences of the United States of America},
  volume    = {103},
  journal   = {Proceedings of the National Academy of Sciences of the United States of America},
  number    = {32},
  publisher = {National Academy of Sciences},
  address   = {Washington},
  issn      = {0027-8424},
  doi       = {10.1073/pnas.0600013103},
  pages     = {11868 -- 11873},
  year      = {2006},
  abstract  = {To develop and investigate detailed mathematical models of metabolic processes is one of the primary challenges in systems biology. However, despite considerable advance in the topological analysis of metabolic networks, kinetic modeling is still often severely hampered by inadequate knowledge of the enzyme-kinetic rate laws and their associated parameter values. Here we propose a method that aims to give a quantitative account of the dynamical capabilities of a metabolic system, without requiring any explicit information about the functional form of the rate equations. Our approach is based on constructing a local linear model at each point in parameter space, such that each element of the model is either directly experimentally accessible or amenable to a straightforward biochemical interpretation. This ensemble of local linear models, encompassing all possible explicit kinetic models, then allows for a statistical exploration of the comprehensive parameter space. The method is exemplified on two paradigmatic metabolic systems: the glycolytic pathway of yeast and a realistic-scale representation of the photosynthetic Calvin cycle.},
  language  = {en}
}
@article{SteuerHumburgSelbig2006,
  author    = {Steuer, Ralf and Humburg, Peter and Selbig, Joachim},
  title     = {Validation and functional annotation of expression-based clusters based on gene ontology},
  series = {BMC bioinformatics},
  volume    = {7},
  journal   = {BMC bioinformatics},
  number    = {380},
  publisher = {BioMed Central},
  address   = {London},
  issn      = {1471-2105},
  doi       = {10.1186/1471-2105-7-380},
  pages     = {12},
  year      = {2006},
  abstract  = {Background: The biological interpretation of large-scale gene expression data is one of the paramount challenges in current bioinformatics. In particular, placing the results in the context of other available functional genomics data, such as existing bio-ontologies, has already provided substantial improvement for detecting and categorizing genes of interest. One common approach is to look for functional annotations that are significantly enriched within a group or cluster of genes, as compared to a reference group. Results: In this work, we suggest the information-theoretic concept of mutual information to investigate the relationship between groups of genes, as given by data-driven clustering, and their respective functional categories. Drawing upon related approaches (Gibbons and Roth, Genome Research 12: 1574-1581, 2002), we seek to quantify to what extent individual attributes are sufficient to characterize a given group or cluster of genes. Conclusion: We show that the mutual information provides a systematic framework to assess the relationship between groups or clusters of genes and their functional annotations in a quantitative way. Within this framework, the mutual information allows us to address and incorporate several important issues, such as the interdependence of functional annotations and combinatorial combinations of attributes. It thus supplements and extends the conventional search for overrepresented attributes within a group or cluster of genes. In particular taking combinations of attributes into account, the mutual information opens the way to uncover specific functional descriptions of a group of genes or clustering result. All datasets and functional annotations used in this study are publicly available. All scripts used in the analysis are provided as additional files.},
  language  = {en}
}