@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{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}
}