Reinforcement in Biology: Stochastic models of group formation and network construction
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Empirical studies show that similar patterns emerge from a large number of different biological systems. For example, the group size distributions of several fish species and house sparrows all follow power law distributions with an exponential truncation. Networks built by ant colonies, slime mold and those are designed by engineers resemble each other in terms of structure and transportation efficiency. Based on the investigation of experimental data, we propose a variety of simple stochastic models to unravel the underlying mechanisms which lead to the collective phenomena in different systems. All the mechanisms employed in these models are rooted in the concept of selective reinforcement. In some systems the reinforcement can build optimal solutions for biological problem solving. This thesis consists of five papers. In the first three papers, I collaborate with biologists to look into group formation in house sparrows and the movement decisions of damsel fish. In the last two articles, I look at how shortest paths and networks are constructed by slime molds and pheromone laying ants, as well as studying speed-accuracy tradeoffs in slime molds' decision making. The general goal of the study is to better understand how macro level patterns and behaviors emerges from micro level interactions in both spatial and non-spatial biological systems. With the combination of mathematical modeling and experimentation, we are able to reproduce the macro level patterns in the studied biological systems and predict behaviors of the systems using minimum number of parameters.
Place, publisher, year, edition, pages
Uppsala: Department of Mathematics, 2012. , 31 p.
Uppsala Dissertations in Mathematics, ISSN 1401-2049 ; 80
reinforcement in biology, merge and split model, preferential attachment, reinforced random walk, network construction, shortest path problem, transport networks, ant algorithm, slime mould, physarum polycephalum, speed-accuracy tradeoff.
Research subject Mathematics with specialization in Applied Mathematics
IdentifiersURN: urn:nbn:se:uu:diva-186989ISBN: 978-91-506-2327-7OAI: oai:DiVA.org:uu-186989DiVA: diva2:574200
2013-01-10, Häggsalen, Ångström Laboratory, Lägerhyddsvägen 1, Uppsala, 13:15 (English)
Sumpter, David, Professor
List of papers