@article{KumarNaumannAigneretal.2015,
  author    = {Kumar, Praveen and Naumann, Ulrike and Aigner, Ludwig and Wischhusen, Joerg and Beier, Christoph P and Beier, Dagmar},
  title     = {Impaired TGF-β induced growth inhibition contributes to the increased proliferation rate of neural stem cells harboring mutant p53},
  series = {American Journal of Cancer Research},
  volume    = {5},
  journal   = {American Journal of Cancer Research},
  number    = {11},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-144262},
  pages     = {3436-3445},
  year      = {2015},
  abstract  = {Gliomas have been classified according to their histological properties. However, their respective cells of origin are still unknown. Neural progenitor cells (NPC) from the subventricular zone (SVZ) can initiate tumors in murine models of glioma and are likely cells of origin in the human disease. In both, p53 signaling is often functionally impaired which may contribute to tumor formation. Also, TGF-beta, which under physiological conditions exerts a strong control on the proliferation of NPCs in the SVZ, is a potent mitogen on glioma cells. Here, we approach on the crosstalk between p53 and TGF-beta by loss of function experiments using NPCs derived from p53 mutant mice, as well as pharmacological inhibition of TGF-beta signaling using TGF-beta receptor inhibitors. NPC derived from p53 mutant mice showed increased clonogenicity and more rapid proliferation than their wildtype counterparts. Further, NPC derived from p53\(^{mut/mut}\) mice were insensitive to TGF-beta induced growth arrest. Still, the canonical TGF-beta signaling pathway remained functional in the absence of p53 signaling and expression of key proteins as well as phosphorylation and nuclear translocation of SMAD2 were unaltered. TGF-beta-induced p21 expression could, in contrast, only be detected in p53\(^{wt/wt}\) but not in p53\(^{mut/mut}\) NPC. Conversely, inhibition of TGF-beta signaling using SB431542 increased proliferation of p53\(^{wt/wt}\) but not of p53\(^{mut/mut}\) NPC. In conclusion, our data suggest that the TGF-beta induced growth arrest in NPC depends on functional p53. Mutational inactivation of p53 hence contributes to increased proliferation of NPC and likely to the formation of hyperplasia of the SVZ observed in p53 deficient mice in vivo.},
  language  = {en}
}
@article{KarlDandekar2015,
  author    = {Karl, Stefan and Dandekar, Thomas},
  title     = {Convergence behaviour and control in non-linear biological networks},
  series = {Scientific Reports},
  volume    = {5},
  journal   = {Scientific Reports},
  number    = {09746},
  doi       = {10.1038/srep09746},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-148510},
  year      = {2015},
  abstract  = {Control of genetic regulatory networks is challenging to define and quantify. Previous control centrality metrics, which aim to capture the ability of individual nodes to control the system, have been found to suffer from plausibility and applicability problems. Here we present a new approach to control centrality based on network convergence behaviour, implemented as an extension of our genetic regulatory network simulation framework Jimena (http://stefan-karl.de/jimena). We distinguish three types of network control, and show how these mathematical concepts correspond to experimentally verified node functions and signalling pathways in immunity and cell differentiation: Total control centrality quantifies the impact of node mutations and identifies potential pharmacological targets such as genes involved in oncogenesis (e.g. zinc finger protein GLI2 or bone morphogenetic proteins in chondrocytes). Dynamic control centrality describes relaying functions as observed in signalling cascades (e.g. src kinase or Jak/Stat pathways). Value control centrality measures the direct influence of the value of the node on the network (e.g. Indian hedgehog as an essential regulator of proliferation in chondrocytes). Surveying random scale-free networks and biological networks, we find that control of the network resides in few high degree driver nodes and networks can be controlled best if they are sparsely connected.},
  language  = {en}
}