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@ARTICLE{BernardoFaura:119649,
      author       = {M. Bernardo-Faura$^*$ and S. Massen$^*$ and C. S. Falk and
                      N. Brady$^*$ and R. Eils$^*$},
      title        = {{D}ata-derived modeling characterizes plasticity of {MAPK}
                      signaling in melanoma.},
      journal      = {PLoS Computational Biology},
      volume       = {10},
      number       = {9},
      issn         = {1553-7358},
      address      = {San Francisco, Calif.},
      publisher    = {Public Library of Science},
      reportid     = {DKFZ-2017-00280},
      pages        = {e1003795 -},
      year         = {2014},
      abstract     = {The majority of melanomas have been shown to harbor somatic
                      mutations in the RAS-RAF-MEK-MAPK and PI3K-AKT pathways,
                      which play a major role in regulation of proliferation and
                      survival. The prevalence of these mutations makes these
                      kinase signal transduction pathways an attractive target for
                      cancer therapy. However, tumors have generally shown
                      adaptive resistance to treatment. This adaptation is
                      achieved in melanoma through its ability to undergo
                      neovascularization, migration and rearrangement of signaling
                      pathways. To understand the dynamic, nonlinear behavior of
                      signaling pathways in cancer, several computational modeling
                      approaches have been suggested. Most of those models require
                      that the pathway topology remains constant over the entire
                      observation period. However, changes in topology might
                      underlie adaptive behavior to drug treatment. To study
                      signaling rearrangements, here we present a new approach
                      based on Fuzzy Logic (FL) that predicts changes in network
                      architecture over time. This adaptive modeling approach was
                      used to investigate pathway dynamics in a newly acquired
                      experimental dataset describing total and phosphorylated
                      protein signaling over four days in A375 melanoma cell line
                      exposed to different kinase inhibitors. First, a generalized
                      strategy was established to implement a parameter-reduced FL
                      model encoding non-linear activity of a signaling network in
                      response to perturbation. Next, a literature-based topology
                      was generated and parameters of the FL model were derived
                      from the full experimental dataset. Subsequently, the
                      temporal evolution of model performance was evaluated by
                      leaving time-defined data points out of training. Emerging
                      discrepancies between model predictions and experimental
                      data at specific time points allowed the characterization of
                      potential network rearrangement. We demonstrate that this
                      adaptive FL modeling approach helps to enhance our
                      mechanistic understanding of the molecular plasticity of
                      melanoma.},
      cin          = {B080 / B170},
      ddc          = {570},
      cid          = {I:(DE-He78)B080-20160331 / I:(DE-He78)B170-20160331},
      pnm          = {312 - Functional and structural genomics (POF3-312)},
      pid          = {G:(DE-HGF)POF3-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:25188314},
      pmc          = {pmc:PMC4154640},
      doi          = {10.1371/journal.pcbi.1003795},
      url          = {https://inrepo02.dkfz.de/record/119649},
}