@article{WeisseHeddergottHeydtetal.2012,
  author    = {Weiße, Sebastian and Heddergott, Niko and Heydt, Matthias and Pfl{\"a}sterer, Daniel and Maier, Timo and Haraszti, Tamas and Grunze, Michael and Engstler, Markus and Rosenhahn, Axel},
  title     = {A Quantitative 3D Motility Analysis of Trypanosoma brucei by Use of Digital In-line Holographic Microscopy},
  series = {PLoS One},
  volume    = {7},
  journal   = {PLoS One},
  number    = {5},
  doi       = {10.1371/journal.pone.0037296},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-130666},
  pages     = {e37296},
  year      = {2012},
  abstract  = {We present a quantitative 3D analysis of the motility of the blood parasite Trypanosoma brucei. Digital in-line holographic microscopy has been used to track single cells with high temporal and spatial accuracy to obtain quantitative data on their behavior. Comparing bloodstream form and insect form trypanosomes as well as mutant and wildtype cells under varying external conditions we were able to derive a general two-state-run-and-tumble-model for trypanosome motility. Differences in the motility of distinct strains indicate that adaption of the trypanosomes to their natural environments involves a change in their mode of swimming.},
  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}
}