  Cardiovascular networks span the body by branching across many generations of
vessels. The resulting structure delivers blood over long distances to supply
all cells with oxygen via the relatively short-range process of diffusion at
the capillary level. The structural features of the network that accomplish
this density and ubiquity of capillaries are often called space-filling. There
are multiple strategies to fill a space, but some strategies do not lead to
biologically adaptive structures by requiring too much construction material or
space, delivering resources too slowly, or using too much power to move blood
through the system. We empirically measure the structure of real networks (18
humans and 1 mouse) and compare these observations with predictions of model
networks that are space-filling and constrained by a few guiding biological
principles. We devise a numerical method that enables the investigation of
space-filling strategies and determination of which biological principles
influence network structure. Optimization for only a single principle creates
unrealistic networks that represent an extreme limit of the possible structures
that could be observed in nature. We first study these extreme limits for two
competing principles, minimal total material and minimal path lengths. We
combine these two principles and enforce various thresholds for balance in the
network hierarchy, which provides a novel approach that highlights the
trade-offs faced by biological networks and yields predictions that better
match our empirical data.
