Biological network growth in complex environments: A computational framework

Please always quote using this URN: urn:nbn:de:bvb:20-opus-231373
  • 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 observeSpatial 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.show moreshow less

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Metadaten
Author: Torsten Johann Paul, Philip KollmannsbergerORCiD
URN:urn:nbn:de:bvb:20-opus-231373
Document Type:Journal article
Faculties:Fakultät für Biologie / Center for Computational and Theoretical Biology
Language:English
Parent Title (English):PLoS Computational Biology
Year of Completion:2020
Volume:16
Issue:11
Article Number:e1008003
Source:PLoS Computational Biology 2020, 16(11): e1008003. https://doi.org/ 10.1371/journal.pcbi.1008003
DOI:https://doi.org/10.1371/journal.pcbi.1008003
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Tag:connectome; generation; mechanisms; osteocyte network; shape
Release Date:2021/04/21
Collections:Open-Access-Publikationsfonds / Förderzeitraum 2020
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International