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Networks for Resilience

Scientists are finding models of resilience in networks in nature. When we look at a leaf or a dragonfly’s wings, we find that there is a complex network of nested loops. It has been found that nested loop structures are resistant to damage and are able to deal with fluctuations in fluid flow.

These networks must be resilient because, for instance if an insect takes a bite out of a leaf, it should still be able to function, and carry water/nutrients to the remaining parts of the leaf. Similarly, if the dragonfly’s wing gets slightly damaged, it should still be able to fly afterwards. It was found that nested loops, that is, loops within loops are the most resistant to damage. It is also seen that nature has evolved to develop this characteristic, because in a leaf of ‘ginko biloba’, an evolutionarily ancient plant, there are no looped networks, and it does not show the same resilience to damage. It was also found that these loop networks can handle fluid fluctuations better as environmental conditions vary since there are multiple channels present.

Understanding these networks can also help us understand more complex networks like the relation between brain activity and blood flow. Mapping the networks in the brain can prove to be extremely useful- it can provide insight into the role of blood flow in Alzheimer’s disease and other cognitive diseases. These networks can be analyzed similar to a network of pipes, as a fluid flow model. They can then be extended to understanding neurons in the brain, or networks of interacting genes.

This resilience strategy seen in nature has been carried over even in structural architecture, where bridges and support structures have many interconnecting rods that provide extra balance. In class, we saw that networks having strong bonds (or showing strong triadic closure property) is a sign of a well connected network. Even if one of the nodes gets disconnected from the network, the rest still remain interconnected. Even in nature, it is seen that the more interconnections there are present in a network, the stronger the network is- as it can afford to lose some connections but the rest of the nodes remain connected. Being able to model a simple network, like the veins of a leaf or a dragonfly’s wings, can enable us to model larger, more complex networks.



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