How to Pare a Pair: Topology Control and Pruning in Intertwined Complex Networks

TL;DR

This paper extends a 3D coupled network model to study how spatial and flow fluctuations influence the topology and structure of complex biological networks, revealing new scaling laws and a modified Murray's law.

Contribution

It introduces an advanced coupled network model that captures the effects of spatial coupling and flow fluctuations on network topology and proposes a new geometric law for vessel interactions.

Findings

Coupled networks control topological complexity onset.

Flow fluctuations and spatial coupling induce topology transitions.

Derived a modified Murray's law incorporating local interactions.

Abstract

Recent work on self-organized remodeling of vasculature in slime-mold, leaf venation systems and vessel systems in vertebrates has put forward a plethora of potential adaptation mechanisms. All these share the underlying hypothesis of a flow-driven machinery, meant to alter rudimentary vessel networks in order to optimize the system's dissipation, flow uniformity, or more, with different versions of constraints. Nevertheless, the influence of environmental factors on the long-term adaptation dynamics as well as the networks structure and function have not been fully understood. Therefore, interwoven capillary systems such as found in the liver, kidney and pancreas, present a novel challenge and key opportunity regarding the field of coupled distribution networks. We here present an advanced version of the discrete Hu--Cai model, coupling two spatial networks in 3D. We show that spatial coupling of two flow-adapting networks can control the onset of topological complexity in concert with short-term flow fluctuations. We find that both fluctuation-induced and spatial coupling induced topology transitions undergo curve collapse obeying simple functional rescaling. Further, our approach results in an alternative form of Murray's law, which incorporates local vessel interactions and flow interactions. This geometric law allows for the estimation of the model parameters in ideal Kirchhoff networks and respective experimentally acquired network skeletons.