Biological transport webs, such as the blood circulatory system in the brain and other animal organs, or the slime mold Physarum polycephalum, are frequently dominated by dense sets of nested cycles. The architecture of these networks, as defined by the topology and edge weights, determines how efficiently the networks perform their function. In this talk we present some general models regarding the emergence and extraction of hierarchical nestedness in biological transport networks. In particular, we discuss how a hierarchically organized vascular system is optimal under conditions of variable, time-dependent flow, but also how it emerges naturally from a set of simple local feedback rules. To characterize the topology of these weighted cycle-rich network architectures, we develop an algorithmic framework that analyzes how the cycles are nested. Finally, using this algorithmic framework and an extensive dataset of more than 180 leaves and leaflets, we show how the hierarchical organization of the nested architecture is in fact a distinct phenotypic trait, akin to a fingerprint, that characterizes the vascular systems of plants and can be used to assist species identification from leaf fragments.
Lunch@ 11:45, Seminar 12-1:00