Spatial Moment Description of Birth–Death–Movement Processes Incorporating the Effects of Crowding and Obstacles
Author(s) -
Anudeep Surendran,
Michael J. Plank,
Matthew J. Simpson
Publication year - 2018
Publication title -
bulletin of mathematical biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 89
eISSN - 1522-9602
pISSN - 0092-8240
DOI - 10.1007/s11538-018-0488-1
Subject(s) - population , birth–death process , ibm , moment (physics) , moment closure , feature (linguistics) , cluster analysis , field (mathematics) , computer science , statistical physics , mathematics , artificial intelligence , geography , physics , classical mechanics , turbulence , linguistics , demography , philosophy , sociology , meteorology , optics , pure mathematics
Birth-death-movement processes, modulated by interactions between individuals, are fundamental to many cell biology processes. A key feature of the movement of cells within in vivo environments is the interactions between motile cells and stationary obstacles. Here we propose a multi-species model of individual-level motility, proliferation and death. This model is a spatial birth-death-movement stochastic process, a class of individual-based model (IBM) that is amenable to mathematical analysis. We present the IBM in a general multi-species framework and then focus on the case of a population of motile, proliferative agents in an environment populated by stationary, non-proliferative obstacles. To analyse the IBM, we derive a system of spatial moment equations governing the evolution of the density of agents and the density of pairs of agents. This approach avoids making the usual mean-field assumption so that our models can be used to study the formation of spatial structure, such as clustering and aggregation, and to understand how spatial structure influences population-level outcomes. Overall the spatial moment model provides a reasonably accurate prediction of the system dynamics, including important effects such as how varying the properties of the obstacles leads to different spatial patterns in the population of agents.
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