ArticleProceedings of the National Academy of Sciences of the United States of America2019
Limits of multifunctionality in tunable networks.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Evolutionary pathways in epistatic mechanical networks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Optimization hardness constrains ecological transients.PLoS computational biology · 2025Article
- Learning to learn by using nonequilibrium training protocols for adaptable materials.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Systematic modification of functionality in disordered elastic networks through free energy surface tailoring.Science advances · 2023Article
- Active neural coordination of motor behaviors with internal states.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Transient learning degrees of freedom for introducing function in materials.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Mathematical models to study the biology of pathogens and the infectious diseases they cause.iScience · 2022Review
- Periodic training of creeping solids.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Resilience of three-dimensional sinusoidal networks in liver tissue.PLoS computational biology · 2020Article
- Principles of convergence in nature and society and their application: from nanoscale, digits, and logic steps to global progress.Journal of nanoparticle research : an interdisciplinary forum for nanoscale science and technology · 2020Article
- Mechanics of Allostery: Contrasting the Induced Fit and Population Shift Scenarios.Biophysical journal · 2019Article
- Designed Elastic Networks: Models of Complex Protein Machinery.International journal of molecular sciences · 2018Review
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Authors and funding
5 authors.
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Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.