Limits of multifunctionality in tunable networks

Nature is rife with networks that are functionally optimized to propagate inputs to perform specific tasks. Whether via genetic evolution or dynamic adaptation, many networks create functionality by locally tuning interactions between nodes. Here we explore this behavior in two contexts: strain prop...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2019-02, Vol.116 (7), p.2506-2511
Main Authors: Rocks, Jason W., Ronellenfitsch, Henrik, Liu, Andrea J., Nagel, Sidney R., Katifori, Eleni
Format: Article
Language:English
Subjects:
allostery
Biological evolution
CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY
constraint–satisfaction problems
Evolution & development
flow networks
functional materials
mechanical metamaterials
mechanical networks
multifunctionality
network optimization
Optimization
Phase transitions
Physical Sciences
Scaling
Strain
Transportation networks
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Summary:Nature is rife with networks that are functionally optimized to propagate inputs to perform specific tasks. Whether via genetic evolution or dynamic adaptation, many networks create functionality by locally tuning interactions between nodes. Here we explore this behavior in two contexts: strain propagation in mechanical networks and pressure redistribution in flow networks. By adding and removing links, we are able to optimize both types of networks to perform specific functions. We define a single function as a tuned response of a single “target” link when another, predetermined part of the network is activated. Using network structures generated via such optimization, we investigate how many simultaneous functions such networks can be programed to fulfill. We find that both flow and mechanical networks display qualitatively similar phase transitions in the number of targets that can be tuned, along with the same robust finite-size scaling behavior. We discuss how these properties can be understood in the context of constraint–satisfaction problems.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1806790116