@article{SalihogluSrivastavaLiangetal.2023,
  author    = {Salihoglu, Rana and Srivastava, Mugdha and Liang, Chunguang and Schilling, Klaus and Szalay, Aladar and Bencurova, Elena and Dandekar, Thomas},
  title     = {PRO-Simat: Protein network simulation and design tool},
  series = {Computational and Structural Biotechnology Journal},
  volume    = {21},
  journal   = {Computational and Structural Biotechnology Journal},
  issn      = {2001-0370},
  doi       = {10.1016/j.csbj.2023.04.023},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-350034},
  pages     = {2767-2779},
  year      = {2023},
  abstract  = {PRO-Simat is a simulation tool for analysing protein interaction networks, their dynamic change and pathway engineering. It provides GO enrichment, KEGG pathway analyses, and network visualisation from an integrated database of more than 8 million protein-protein interactions across 32 model organisms and the human proteome. We integrated dynamical network simulation using the Jimena framework, which quickly and efficiently simulates Boolean genetic regulatory networks. It enables simulation outputs with in-depth analysis of the type, strength, duration and pathway of the protein interactions on the website. Furthermore, the user can efficiently edit and analyse the effect of network modifications and engineering experiments. In case studies, applications of PRO-Simat are demonstrated: (i) understanding mutually exclusive differentiation pathways in Bacillus subtilis, (ii) making Vaccinia virus oncolytic by switching on its viral replication mainly in cancer cells and triggering cancer cell apoptosis and (iii) optogenetic control of nucleotide processing protein networks to operate DNA storage. Multilevel communication between components is critical for efficient network switching, as demonstrated by a general census on prokaryotic and eukaryotic networks and comparing design with synthetic networks using PRO-Simat. The tool is available at https://prosimat.heinzelab.de/ as a web-based query server.},
  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}
}