TY - JOUR A1 - Ameri, Pietro A1 - Schiattarella, Gabriele Giacomo A1 - Crotti, Lia A1 - Torchio, Margherita A1 - Bertero, Edoardo A1 - Rodolico, Daniele A1 - Forte, Maurizio A1 - Di Mauro, Vittoria A1 - Paolillo, Roberta A1 - Chimenti, Cristina A1 - Torella, Daniele A1 - Catalucci, Daniele A1 - Sciarretta, Sebastiano A1 - Basso, Cristina A1 - Indolfi, Ciro A1 - Perrino, Cinzia T1 - 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 JF - International Journal of Molecular Sciences N2 - 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. KW - heart failure KW - basic KW - translational KW - research KW - mechanisms Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-285085 SN - 1422-0067 VL - 21 IS - 4 ER - TY - JOUR A1 - Paul, Torsten Johann A1 - Kollmannsberger, Philip T1 - Biological network growth in complex environments: A computational framework JF - PLoS Computational Biology N2 - 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. KW - osteocyte network KW - connectome KW - mechanisms KW - generation KW - shape Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-231373 VL - 16 IS - 11 ER -