@article{KunzLiangNillaetal.2016,
  author    = {Kunz, Meik and Liang, Chunguang and Nilla, Santosh and Cecil, Alexander and Dandekar, Thomas},
  title     = {The drug-minded protein interaction database (DrumPID) for efficient target analysis and drug development},
  series = {Database},
  volume    = {2016},
  journal   = {Database},
  doi       = {10.1093/database/baw041},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-147369},
  pages     = {baw041},
  year      = {2016},
  abstract  = {The drug-minded protein interaction database (DrumPID) has been designed to provide fast, tailored information on drugs and their protein networks including indications, protein targets and side-targets. Starting queries include compound, target and protein interactions and organism-specific protein families. Furthermore, drug name, chemical structures and their SMILES notation, affected proteins (potential drug targets), organisms as well as diseases can be queried including various combinations and refinement of searches. Drugs and protein interactions are analyzed in detail with reference to protein structures and catalytic domains, related compound structures as well as potential targets in other organisms. DrumPID considers drug functionality, compound similarity, target structure, interactome analysis and organismic range for a compound, useful for drug development, predicting drug side-effects and structure-activity relationships.},
  language  = {en}
}
@article{CecilRikanovicOhlsenetal.2011,
  author    = {Cecil, Alexander and Rikanovic, Carina and Ohlsen, Knut and Liang, Chunguang and Bernhardt, Jorg and Oelschlaeger, Tobias A. and Gulder, Tanja and Bringmann, Gerd and Holzgrabe, Ulrike and Unger, Matthias and Dandekar, Thomas},
  title     = {Modeling antibiotic and cytotoxic effects of the dimeric isoquinoline IQ-143 on metabolism and its regulation in Staphylococcus aureus, Staphylococcus epidermidis and human cells},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-68802},
  year      = {2011},
  abstract  = {Background: Xenobiotics represent an environmental stress and as such are a source for antibiotics, including the isoquinoline (IQ) compound IQ-143. Here, we demonstrate the utility of complementary analysis of both host and pathogen datasets in assessing bacterial adaptation to IQ-143, a synthetic analog of the novel type N,C-coupled naphthyl-isoquinoline alkaloid ancisheynine. Results: Metabolite measurements, gene expression data and functional assays were combined with metabolic modeling to assess the effects of IQ-143 on Staphylococcus aureus, Staphylococcus epidermidis and human cell lines, as a potential paradigm for novel antibiotics. Genome annotation and PCR validation identified novel enzymes in the primary metabolism of staphylococci. Gene expression response analysis and metabolic modeling demonstrated the adaptation of enzymes to IQ-143, including those not affected by significant gene expression changes. At lower concentrations, IQ-143 was bacteriostatic, and at higher concentrations bactericidal, while the analysis suggested that the mode of action was a direct interference in nucleotide and energy metabolism. Experiments in human cell lines supported the conclusions from pathway modeling and found that IQ-143 had low cytotoxicity. Conclusions: The data suggest that IQ-143 is a promising lead compound for antibiotic therapy against staphylococci. The combination of gene expression and metabolite analyses with in silico modeling of metabolite pathways allowed us to study metabolic adaptations in detail and can be used for the evaluation of metabolic effects of other xenobiotics.},
  subject      = {Staphylococcus aureus},
  language  = {en}
}
@article{CecilGentschevAdelfingeretal.2019,
  author    = {Cecil, Alexander and Gentschev, Ivaylo and Adelfinger, Marion and Dandekar, Thomas and Szalay, Aladar A.},
  title     = {Vaccinia virus injected human tumors: oncolytic virus efficiency predicted by antigen profiling analysis fitted boolean models},
  series = {Bioengineered},
  volume    = {10},
  journal   = {Bioengineered},
  number    = {1},
  doi       = {10.1080/21655979.2019.1622220},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-200507},
  pages     = {190-196},
  year      = {2019},
  abstract  = {Virotherapy on the basis of oncolytic vaccinia virus (VACV) strains is a promising approach for cancer therapy. Recently, we showed that the oncolytic vaccinia virus GLV-1h68 has a therapeutic potential in treating human prostate and hepatocellular carcinomas in xenografted mice. In this study, we describe the use of dynamic boolean modeling for tumor growth prediction of vaccinia virus-injected human tumors. Antigen profiling data of vaccinia virus GLV-1h68-injected human xenografted mice were obtained, analyzed and used to calculate differences in the tumor growth signaling network by tumor type and gender. Our model combines networks for apoptosis, MAPK, p53, WNT, Hedgehog, the T-killer cell mediated cell death, Interferon and Interleukin signaling networks. The in silico findings conform very well with in vivo findings of tumor growth. Similar to a previously published analysis of vaccinia virus-injected canine tumors, we were able to confirm the suitability of our boolean modeling for prediction of human tumor growth after virus infection in the current study as well. In summary, these findings indicate that our boolean models could be a useful tool for testing of the efficacy of VACV-mediated cancer therapy already before its use in human patients.},
  language  = {en}
}