given number equally large sub-domains. The whole process is independent of the shape of the divided domain. More, if you dynamically insert a new domain ...
Supplementary information to the paper (porting to other biological applications and a computational example of self-organization)
New meanings: This supplement material is about how this paper might be understood from the biological point of view. Although the developed algorithm is applied within the field of computer science, the original motivation of it comes from biology. Originally, there was an initial idea of growth of living cells, bacterial colonies, or animal herds—beside other possibilities—competing for a territory. The algorithm itself can be easily rewritten to numerous biological models (you might ask for it). May be you are working on an important biological research that might benefit from this approach but did not realize it.
Self-organization example: Very important feature of the algorithm is its self-organizing property. Irrespectively of the initial condition, the final solution always leads to a division of area into a given number equally large sub-domains. The whole process is independent of the shape of the divided domain. More, if you dynamically insert a new domain then the algorithm correctly adjust all domains to the new situation (published elsewhere, in a book chapter). In other words, sizes of sub-domains again achieve equally large subdomains. Untested feature of the algorithm is the opposite situation, i.e. ‘death’ of a sub-domain and subsequent enlargement of remaining (alive) sub-domains.
Biomedical Center Faculty of Medicine in Pilsen, Charles University in Prague alej Svobody 1655/76 | 323 00 Pilsen | Czech Republic | www.biomedic-plzen.cz
