The paper discusses the design of biologically-inspired adaptive networks, focusing on the slime mold *Physarum polycephalum* as a model for understanding how biological systems form efficient and resilient networks. The authors compare the network formation of *Physarum* with real-world infrastructure networks, such as the Tokyo rail system, to highlight the similarities in terms of cost, transport efficiency, and fault tolerance. They demonstrate that *Physarum* can form networks with comparable performance to these infrastructure networks, despite lacking centralized control and evolving through natural selection. The core mechanisms of adaptive network formation in *Physarum* are captured in a mathematical model that simulates the growth and adaptation of the plasmodium based on feedback loops between tube thickness and internal protoplasmic flow. This model can be adjusted to optimize specific network features, such as fault tolerance or transport efficiency, while keeping costs low. The authors conclude that this biologically-inspired model can provide a useful framework for improving routing protocols and topology control in self-organized networks, such as remote sensor arrays and wireless mesh networks.The paper discusses the design of biologically-inspired adaptive networks, focusing on the slime mold *Physarum polycephalum* as a model for understanding how biological systems form efficient and resilient networks. The authors compare the network formation of *Physarum* with real-world infrastructure networks, such as the Tokyo rail system, to highlight the similarities in terms of cost, transport efficiency, and fault tolerance. They demonstrate that *Physarum* can form networks with comparable performance to these infrastructure networks, despite lacking centralized control and evolving through natural selection. The core mechanisms of adaptive network formation in *Physarum* are captured in a mathematical model that simulates the growth and adaptation of the plasmodium based on feedback loops between tube thickness and internal protoplasmic flow. This model can be adjusted to optimize specific network features, such as fault tolerance or transport efficiency, while keeping costs low. The authors conclude that this biologically-inspired model can provide a useful framework for improving routing protocols and topology control in self-organized networks, such as remote sensor arrays and wireless mesh networks.