@article{AmeriSchiattarellaCrottietal.2020,
  author    = {Ameri, Pietro and Schiattarella, Gabriele Giacomo and Crotti, Lia and Torchio, Margherita and Bertero, Edoardo and Rodolico, Daniele and Forte, Maurizio and Di Mauro, Vittoria and Paolillo, Roberta and Chimenti, Cristina and Torella, Daniele and Catalucci, Daniele and Sciarretta, Sebastiano and Basso, Cristina and Indolfi, Ciro and Perrino, Cinzia},
  title     = {Novel basic science insights to improve the management of heart failure: Review of the working group on cellular and molecular biology of the heart of the Italian Society of Cardiology},
  series = {International Journal of Molecular Sciences},
  volume    = {21},
  journal   = {International Journal of Molecular Sciences},
  number    = {4},
  issn      = {1422-0067},
  doi       = {10.3390/ijms21041192},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-285085},
  year      = {2020},
  abstract  = {Despite important advances in diagnosis and treatment, heart failure (HF) remains a syndrome with substantial morbidity and dismal prognosis. Although implementation and optimization of existing technologies and drugs may lead to better management of HF, new or alternative strategies are desirable. In this regard, basic science is expected to give fundamental inputs, by expanding the knowledge of the pathways underlying HF development and progression, identifying approaches that may improve HF detection and prognostic stratification, and finding novel treatments. Here, we discuss recent basic science insights that encompass major areas of translational research in HF and have high potential clinical impact.},
  language  = {en}
}
@article{PaulKollmannsberger2020,
  author    = {Paul, Torsten Johann and Kollmannsberger, Philip},
  title     = {Biological network growth in complex environments: A computational framework},
  series = {PLoS Computational Biology},
  volume    = {16},
  journal   = {PLoS Computational Biology},
  number    = {11},
  doi       = {10.1371/journal.pcbi.1008003},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-231373},
  year      = {2020},
  abstract  = {Spatial biological networks are abundant on all scales of life, from single cells to ecosystems, and perform various important functions including signal transmission and nutrient transport. These biological functions depend on the architecture of the network, which emerges as the result of a dynamic, feedback-driven developmental process. While cell behavior during growth can be genetically encoded, the resulting network structure depends on spatial constraints and tissue architecture. Since network growth is often difficult to observe experimentally, computer simulations can help to understand how local cell behavior determines the resulting network architecture. We present here a computational framework based on directional statistics to model network formation in space and time under arbitrary spatial constraints. Growth is described as a biased correlated random walk where direction and branching depend on the local environmental conditions and constraints, which are presented as 3D multilayer grid. To demonstrate the application of our tool, we perform growth simulations of a dense network between cells and compare the results to experimental data from osteocyte networks in bone. Our generic framework might help to better understand how network patterns depend on spatial constraints, or to identify the biological cause of deviations from healthy network function. Author summary We present a novel modeling approach and computational implementation to better understand the development of spatial biological networks under the influence of external signals. Our tool allows us to study the relationship between local biological growth parameters and the emerging macroscopic network function using simulations. This computational approach can generate plausible network graphs that take local feedback into account and provide a basis for comparative studies using graph-based methods.},
  language  = {en}
}