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Mean-field approach to evolving spatial networks, with an application to osteocyte network formation.
- Source :
-
Physical Review E . Jul2017, Vol. 96 Issue 1, p1-1. 1p. - Publication Year :
- 2017
-
Abstract
- We consider evolving networks in which each node can have various associated properties (a state) in addition to those that arise from network structure. For example, each node can have a spatial location and a velocity, or it can have some more abstract internal property that describes something like a social trait. Edges between nodes are created and destroyed, and new nodes enter the system. We introduce a "local state degree distribution" (LSDD) as the degree distribution at a particular point in state space. We then make a mean-field assumption and thereby derive an integro-partial differential equation that is satisfied by the LSDD. We perform numerical experiments and find good agreement between solutions of the integro-differential equation and the LSDD from stochastic simulations of the full model. To illustrate our theory, we apply it to a simple model for osteocyte network formation within bones, with a view to understanding changes that may take place during cancer. Our results suggest that increased rates of differentiation lead to higher densities of osteocytes, but with a smaller number of dendrites. To help provide biological context, we also include an introduction to osteocytes, the formation of osteocyte networks, and the role of osteocytes in bone metastasis. [ABSTRACT FROM AUTHOR]
- Subjects :
- *DIFFERENTIAL equations
*OSTEOCYTES
*BONE metastasis
Subjects
Details
- Language :
- English
- ISSN :
- 24700045
- Volume :
- 96
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Physical Review E
- Publication Type :
- Academic Journal
- Accession number :
- 124591604
- Full Text :
- https://doi.org/10.1103/PhysRevE.96.012301