Biologically-inspired adaptive network design is explored through the study of Physarum polycephalum, a slime mold that forms efficient transport networks. The slime mold's network formation mirrors real-world infrastructure, such as the Tokyo rail system, in terms of cost, efficiency, and fault tolerance. The network is self-organized without centralized control, using selective reinforcement of preferred routes and removal of redundant connections. A mathematical model was developed to emulate this behavior, based on feedback loops between tube thickness and internal protoplasmic flow. The model captures the dynamics of network adaptability through local rules, producing solutions comparable to real-world infrastructure. The model's parameters can be tuned to adjust the benefit-cost ratio, enhancing specific features like fault tolerance or transport efficiency while keeping costs low. The study highlights the potential of biologically-inspired models for improving routing protocols and topology control in self-organized networks, such as remote sensor arrays or wireless mesh networks. The model's performance was evaluated against the Tokyo rail network, showing similar characteristics in terms of cost, transport efficiency, and fault tolerance. The model achieved a better benefit-cost ratio than the rail network, with comparable transport efficiency. The study underscores the importance of balancing cost, efficiency, and resilience in network design, and demonstrates the potential of biological systems as a source of inspiration for solving complex optimization problems.Biologically-inspired adaptive network design is explored through the study of Physarum polycephalum, a slime mold that forms efficient transport networks. The slime mold's network formation mirrors real-world infrastructure, such as the Tokyo rail system, in terms of cost, efficiency, and fault tolerance. The network is self-organized without centralized control, using selective reinforcement of preferred routes and removal of redundant connections. A mathematical model was developed to emulate this behavior, based on feedback loops between tube thickness and internal protoplasmic flow. The model captures the dynamics of network adaptability through local rules, producing solutions comparable to real-world infrastructure. The model's parameters can be tuned to adjust the benefit-cost ratio, enhancing specific features like fault tolerance or transport efficiency while keeping costs low. The study highlights the potential of biologically-inspired models for improving routing protocols and topology control in self-organized networks, such as remote sensor arrays or wireless mesh networks. The model's performance was evaluated against the Tokyo rail network, showing similar characteristics in terms of cost, transport efficiency, and fault tolerance. The model achieved a better benefit-cost ratio than the rail network, with comparable transport efficiency. The study underscores the importance of balancing cost, efficiency, and resilience in network design, and demonstrates the potential of biological systems as a source of inspiration for solving complex optimization problems.