@misc{DworschakGrellNikiforovaetal.2008,
  author    = {Dworschak, Steve and Grell, Susanne and Nikiforova, Victoria J. and Schaub, Torsten H. and Selbig, Joachim},
  title     = {Modeling biological networks by action languages via answer set programming},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch Naturwissenschaftliche Reihe},
  number    = {843},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-42984},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-429846},
  pages     = {47},
  year      = {2008},
  abstract  = {We describe an approach to modeling biological networks by action languages via answer set programming. To this end, we propose an action language for modeling biological networks, building on previous work by Baral et al. We introduce its syntax and semantics along with a translation into answer set programming, an efficient Boolean Constraint Programming Paradigm. Finally, we describe one of its applications, namely, the sulfur starvation response-pathway of the model plant Arabidopsis thaliana and sketch the functionality of our system and its usage.},
  language  = {en}
}
@misc{RepsilberKernTelaaretal.2010,
  author    = {Repsilber, Dirk and Kern, Sabine and Telaar, Anna and Walzl, Gerhard and Black, Gillian F. and Selbig, Joachim and Parida, Shreemanta K. and Kaufmann, Stefan H. E. and Jacobsen, Marc},
  title     = {Biomarker discovery in heterogeneous tissue samples},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {854},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-42934},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-429343},
  pages     = {17},
  year      = {2010},
  abstract  = {Background: For heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or in-silico deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues. Results: Experimental data and simulation studies involving noise parameters estimated from these data revealed that for valid detection of differential gene expression, quantile normalization and use of non-log data are optimal. We demonstrate the feasibility of predicting proportions of constituting cell types from gene expression data of single samples, as a prerequisite for a deconfounding-based classification approach. Classification cross-validation errors with and without using deconfounding results are reported as well as sample-size dependencies. Implementation of the algorithm, simulation and analysis scripts are available. Conclusions: The deconfounding algorithm without decorrelation using quantile normalization on non-log data is proposed for biomarkers that are difficult to detect, and for cases where confounding by varying proportions of cell types is the suspected reason. In this case, a deconfounding ranking approach can be used as a powerful alternative to, or complement of, other statistical learning approaches to define candidate biomarkers for molecular diagnosis and prediction in biomedicine, in realistically noisy conditions and with moderate sample sizes.},
  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}
}