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Designing allostery-inspired response in mechanical networks
Open accessAbstract
Recent advances in designing metamaterials have demonstrated that global mechanical properties of disordered spring networks can be tuned by selectively modifying only a small subset of bonds. Here, using a computationally efficient approach, we extend this idea to tune more general properties of networks. With nearly complete success, we are able to produce a strain between any two target nodes in a network in response to an applied source strain on any other pair of nodes by removing only ∼1% of the bonds. We are also able to control multiple pairs of target nodes, each with a different individual response, from a single source, and to tune multiple independent source/target responses simultaneously into a network. We have fabricated physical networks in macroscopic 2D and 3D systems that exhibit these responses. This work is inspired by the long-range coupled conformational changes that constitute allosteric function in proteins. The fact that allostery is a common means for regulation in biological molecules suggests that it is a relatively easy property to develop through evolution. In analogy, our results show that long-range coupled mechanical responses are similarly easy to achieve in disordered networks.
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“Optimizing the networks for a geometric task, by selecting network structures where binding a ligand leads to a defined displacement on the other side of the network (“active site”), yields networks displaying a powerful lever at the active site, where the signal is required (Flechsig, 2017; Rocks et al., 2017; Yan et al., 2017a, 2017b).”
From Allostery in Its Many Disguises: From Theory to Applications (2019) · cites this paper as Rocks et al., 2017
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Cites
Jamming at zero temperature and zero applied stress: The epitome of disorder
O’Hern, C. S., Silbert, L. E., Liu, A. J., & Nagel, S. R. (2003) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.68.011306 · Cited by 1,604 · cited 1×
“Our networks are generated from random configurations of soft spheres in three dimensions or disks in two dimensions with periodic boundary conditions that have been brought to a local energy minimum using standard jamming algorithms (5, 6); the spheres overlap and are in mechanical equilibrium.”
MethodAdjustment of an Inverse Matrix Corresponding to a Change in One Element of a Given Matrix
Sherman, J., & Morrison, W. J. (1950) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729893 · Cited by 1,402 · cited 2×
“The total energy can then be written as E = 1 2 〈u|H|u〉, [4] where the Hessian matrix H = QF−1QT is a dN× dN matrix.”
MethodThe ensemble nature of allostery
Motlagh, H. N., Wrabl, J. O., Li, J., & Hilser, V. J. (2014) · Nature · doi:10.1038/nature13001 · Cited by 1,247 · cited 1×
“Statistical fluctuations in the structure of proteins have been shown to play an important role in allosteric functionality (22, 23).”
BackgroundIs allostery an intrinsic property of all dynamic proteins?
Gunasekaran, K., Ma, B., & Nussinov, R. (2004) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20232 · Cited by 896 · cited 1×
“This finding can be viewed as a first step towards understanding why allosteric behavior is so common in biopolymers [5].”
BackgroundThe Jamming Transition and the Marginally Jammed Solid
Liu, A. J., & Nagel, S. R. (2010) · Annual Review of Condensed Matter Physics · doi:10.1146/annurev-conmatphys-070909-104045 · Cited by 860 · cited 1×
“Our networks are generated from random configurations of soft spheres in three dimensions or disks in two dimensions with periodic boundary conditions that have been brought to a local energy minimum using standard jamming algorithms (5, 6); the spheres overlap and are in mechanical equilibrium.”
MethodAllostery in Disease and in Drug Discovery
Nussinov, R., & Tsai, C. (2013) · Cell · doi:10.1016/j.cell.2013.03.034 · Cited by 769
Calladine, C. R. (1978) · International Journal of Solids and Structures · doi:10.1016/0020-7683(78)90052-5 · Cited by 657 · cited 3×
“By Maxwell-Calladine counting, N0 − Ns = dN − Nb, where N0 is the number of zero modes [13].”
BackgroundInfluentialGlobal dynamics of proteins: bridging between structure and function
Bahar, I., Lezon, T. R., Yang, L.-W., & Eyal, E. (2010) · Annual Review of Biophysics · doi:10.1146/annurev.biophys.093008.131258 · Cited by 610 · cited 1×
“In fact, folded proteins have long been modeled as elastic networks (13), and the response to localized forces in the resulting networks has been studied (14).”
BackgroundProtein promiscuity and its implications for biotechnology
Nobeli, I., Favia, A. D., & Thornton, J. M. (2009) · Nature Biotechnology · doi:10.1038/nbt1519 · Cited by 508
Allostery: Absence of a change in shape does not imply that allostery is not at play
Tsai, C.-J., del Sol, A., & Nussinov, R. (2008) · Journal of Molecular Biology · doi:10.1016/j.jmb.2008.02.034 · Cited by 453 · cited 1×
“Statistical fluctuations in the structure of proteins have been shown to play an important role in allosteric functionality (22, 23).”
Background
Cited by
Sorted by citations
Items 101–200 of 204
Allosteric Communication Mediated by Protein Contact Clusters: A Dynamical Model
Ali, A. A. A. I., Dorbath, E., & Stock, G. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01188 · Cited by 10
Emergence of Single- versus Multi-State Allostery
Rouvière, E., Ranganathan, R., & Rivoire, O. (2022) · PRX Life · doi:10.1103/prxlife.1.023004 · Cited by 9
Bioinspired nonequilibrium search for novel materials
Murugan, A., & Jaeger, H. M. (2019) · MRS Bulletin · doi:10.1557/mrs.2019.22 · Cited by 9
Training physical matter to matter
Jaeger, H. M., Murugan, A., & Nagel, S. R. (2024) · Soft Matter · doi:10.1039/d4sm00629a · Cited by 10
“This again is a feature of networks that mimic the functions found in biological matter – in this case the allosteric properties of proteins [40-43].”
BackgroundHigh-Speed Combinatorial Polymerization through Kinetic-Trap Encoding
Benoist, F., & Sartori, P. (2025) · Physical Review Letters · doi:10.1103/physrevlett.134.038402 · Cited by 9
Holmes-Cerfon, M. C., Theran, L., & Gortler, S. J. (2020) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.21971 · Cited by 8
“ge in Brisbane [1]; to designing the microstructure of materials with novel properties [4, 5, 29, 31], to studying arrangements of jammed particles [11, 19, 26], to understanding allostery in biology [33, 40]; to studying the properties of molecules [20, 34, 39]; to analyzing the structure of proteins [23]; to studyin…”
BackgroundNon-affinity: The emergence of networks from amorphous planar graphs
Shen, X., Zhu, G., & Li, B. (2023) · Science China Physics Mechanics and Astronomy · doi:10.1007/s11433-022-2068-9 · Cited by 8
Allan, D. C., & Phillips, J. C. (2017) · International Journal of Molecular Sciences · doi:10.3390/ijms18091995 · Cited by 9
“More generally, cracks are an example of allosteric interactions which can be generated by removing only ~1% of the bonds [23].”
BackgroundDesigning athermal disordered solids with automatic differentiation
Zu, M., & Goodrich, C. P. (2024) · Communications Materials · doi:10.1038/s43246-024-00583-4 · Cited by 7
“Finally, there are numerous other material properties that could be (simultaneously) targeted, including thermal properties, nonlinear stress-strain behavior 30,31 , density of states, and allosteric responses 3 .”
Allosteric Lever: Toward a Principle of Specific Allosteric Response
Vossel, M., de Groot, B. L., & Godec, A. (2025) · Physical Review X · doi:10.1103/wv2k-676p · Cited by 8
Mendels, D., Byléhn, F., Sirk, T. W., & Pablo, J. (2023) · Science Advances · doi:10.1126/sciadv.adf7541 · Cited by 12
“8, which measures the difference between the desired target beads’ response and the actual response (20, 54).”
BackgroundMethodInfluentialCecconi, F., Costantini, G., Guardiani, C., Baldovin, M., & Vulpiani, A. (2023) · Physical Biology · doi:10.1088/1478-3975/ace1c5 · Cited by 8
Artificial intelligence for fibrous network design and mechanics
Yang, Y., Cao, L., Ren, J., Gao, W., & Ling, S. (2025) · AI for Science · doi:10.1088/3050-287x/adfaf7 · Cited by 10
Reprogrammable, In‐Materia Matrix‐Vector Multiplication with Floppy Modes
Louvet, T., Omidvar, P., & Serra‐Garcia, M. (2025) · Advanced Intelligent Systems · doi:10.1002/aisy.202500062 · Cited by 7
Emergent elasticity relations for networks of bars with sticky magnetic ends
Yang, X., & Keten, S. (2023) · Extreme Mechanics Letters · doi:10.1016/j.eml.2023.102093 · Cited by 6
Optimizing properties on the critical rigidity manifold of underconstrained central-force networks
Hain, T., Santangelo, C. D., & Manning, M. L. (2025) · Physical review. E · doi:10.1103/physreve.111.015418 · Cited by 6
“Mechanical networks have been trained to exhibit auxetic behavior [41, 42] as well as specific allostery-inspired responses [41].”
BackgroundLoss of material trainability through an unusual transition
Bhaumik, H., & Hexner, D. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.l042044 · Cited by 6
Bifurcation instructed design of multistate machines
Yang, T., Hathcock, D. J., Chen, Y., McEuen, P. L., Sethna, J. P., Cohen, I., & Griniasty, I. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2300081120 · Cited by 8
Self-stresses control stiffness and stability in overconstrained disordered networks
Bose, A., Vermeulen, M. F. J., Storm, C., & Ellenbroek, W. G. (2019) · Physical review. E · doi:10.1103/physreve.99.023001 · Cited by 5
Temporal differential elastic wave computational metamaterials
Lv, Z., Ding, Y., & Pei, Y. (2020) · Journal of Applied Physics · doi:10.1063/5.0003973 · Cited by 6
Shaping Polyclonal Responses via Antigen-Mediated Antibody Interference
Yan, L. W., & Wang, S. (2020) · iScience · doi:10.1016/j.isci.2020.101568 · Cited by 6
Designing Phononic Band Gaps With Sticky Potentials
Zheng, Y., Mahajan, S., Chattoraj, J., & Ciamarra, M. P. (2021) · Frontiers in Physics · doi:10.3389/fphy.2021.665391 · Cited by 6
“Lastly, we emphasize that while this approach in itself, reliably generates spectral gaps without fine tuning, the adaptation of these sticky potentials in constructing elastic networks is meant to be used in conjunction with the various existing protocols that optimize for specific functionalities in mechanical respo…”
BackgroundMethodInfluentialPhysical Networks Become What They Learn.
Stern, M., Guzman, M., Martins, F., Liu, A. J., & Balasubramanian, V. (2024) · Physical Review Letters · doi:10.1103/physrevlett.134.147402 · Cited by 23
“Note that many other solutions, each with its own physical Hessian, are also possible [29] but following [20, 30, 31], systems trained for such linear responses typically find solutions with stiff cost eigenmodes and soft physical eigen-modes.”
Encoding Fast and Fault-Tolerant Memories in Bulk and Nanoscale Amorphous Solids
Adhikari, M., Sharma, R., & Karmakar, S. (2025) · Physical Review Letters · doi:10.1103/physrevlett.134.018202 · Cited by 5
Tunable band gap in distorted square lattice’s phonon spectrum
Fang, C., Wang, N., & Shen, X. (2021) · Results in Physics · doi:10.1016/j.rinp.2021.104697 · Cited by 5
Combinatorial Design of Floppy Modes and Frustrated Loops in Metamaterials
Liu, W., Sigalov, T. A., Coulais, C., & Shokef, Y. (2025) · Physical Review Letters · doi:10.1103/nqgr-tfb1 · Cited by 5
“Connecting specific edge nodes we can design floppy modes of arbitrary topology, like the catenated floppy loops shown in Fig.”
More is Less in Unpercolated Active Solids
Binysh, J., Baardink, G., Veenstra, J., Coulais, C., & Souslov, A. (2026) · Physical Review X · doi:10.1103/flhb-kjyd · Cited by 4
Jamming Memory into Acoustically Trained Dense Suspensions under Shear
Ong, E. Y. X., Barth, A., Singh, N., Ramaswamy, M., Shetty, A. M., Chakraborty, B., Sethna, J. P., & Cohen, I. (2024) · Physical Review X · doi:10.1103/physrevx.14.021027 · Cited by 4
Decomposition of Forces in Protein: Methodology and General Properties
Song, P., Huang, Q., Li, W., Li, M., & Liu, Z. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00716 · Cited by 4
Multistable Physical Neural Networks
Ben-Haim, E., Givli, S., Or, Y., & Gat, A. D. (2025) · Advanced Intelligent Systems · doi:10.1002/aisy.202400694 · Cited by 4
“In earlier studies, flow networks underwent training to demonstrate the specific function of flow allostery [19, 21] through global supervised learning, involving the minimization of a global cost function.”
Designing complex behaviors using transition-based allosteric self-assembly
Metson, J. (2025) · Physical Review Research · doi:10.1103/hx18-tm3p · Cited by 4
Modular representation and control of floppy networks
Chen, S., Giardina, F., Choi, G. P. T., & Mahadevan, L. (2022) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2022.0082 · Cited by 4
Self‐Assembled Robust 2D Networks from Magneto‐Elastic Bars
Yang, X., Leng, J., Sun, C., & Keten, S. (2023) · Advanced Materials Technologies · doi:10.1002/admt.202202189 · Cited by 4
Shape transitions in a network model of active elastic shells
Maji, A., Dasbiswas, K., & Rabin, Y. (2023) · Soft Matter · doi:10.1039/d3sm01041d · Cited by 5
“Future explorations of the model and of the parameter space to design spring networks optimized for a target response, such as shown for an “allostery”-inspired long-range but local network response [56] may be possible.”
BackgroundThe physical logic of protein machines
McBride, J. M., & Tlusty, T. (2024) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/ad1be7 · Cited by 5
Mechanical design principles in frustrated thin elastic sheets
Arieli, M., Moshe, M., & Sharon, E. (2024) · Soft Matter · doi:10.1039/d4sm00166d · Cited by 4
Guo, K., Li, C., Chen, G., Li, Y., Li, C., & Lü, Y. (2025) · Virtual and Physical Prototyping · doi:10.1080/17452759.2025.2567388 · Cited by 3
“The essence of these design strategies lies in exploiting anisotropy, graded density, and hierarchical organisation to steer where stiffness and curvature localise within the lattice [23,24].”
Fully Independent Response in Disordered Solids
Zu, M., Desai, A., & Goodrich, C. P. (2025) · Physical Review Letters · doi:10.1103/physrevlett.134.238201 · Cited by 6
“For example, tuning the bulk and shear moduli separately has enabled the design and creation of auxetic materials [1–5], and microscopic responses have been targeted to create nontrivial stress patterns [6] and allostery-like functionality in both mechanical and flow networks [7–10].”
Memory capacity of adaptive flow networks
Bhattacharyya, K., Zwicker, D., & Alim, K. (2023) · Physical review. E · doi:10.1103/physreve.107.034407 · Cited by 3
“The self-organized information processing of Physarum is reminiscent of other physical learning systems [20]: Physical networks can be trained to have unusual mechanical properties [21,22] and functionalities [23,24], either by modifying microscopic properties by global optimization [25] or as local responses [26].”
BackgroundTraining nonlinear elastic functions: nonmonotonic, sequence dependent and bifurcating
Hexner, D. (2021) · Soft Matter · doi:10.1039/d0sm02189j · Cited by 3
Tah, I., Haertter, D., Crawford, J. M., Kiehart, D. P., Schmidt, C. F., & Liu, A. J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.20.572544 · Cited by 11
“These include negative Poisson ratios (54, 55) and allostery (56) in mechanical networks, greatly enhanced stability in particle packings (57), and the ability to classify data and perform linear regression in mechanical and flow networks (58) as well as laboratory electrical networks (59).”
BackgroundMulti-frequency wave focusing in rationally pruned disordered networks
Krushynska, A. O., & van Hecke, M. (2025) · Matter · doi:10.1016/j.matt.2025.102364 · Cited by 3
Salman, S. N., Shteingolts, S. A., Levie, R., & Mendels, D. (2025) · The Journal of Chemical Physics · doi:10.1063/5.0282871 · Cited by 6
Shi, Z., Jiang, P., Zhu, X., Li, S., Wang, W., Zhao, T., He, R., & Li, Y. (2026) · Advanced Science · doi:10.1002/advs.75605 · Cited by 2
GPCR Signaling: A Study of the Interplay Between Structure, Energy, and Function
Chalopin, Y. (2024) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26724 · Cited by 2
“Based on such an approach, long-range mechanical coupling was found to be facilitated by disordered structures when they exist in proteins, a process deeply influenced by topological irregularities [7– 9].”
Designing precise dynamical steady states in disordered networks
Berneman, M., & Hexner, D. (2025) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ade590 · Cited by 4
Microscopic Imprints of Learned Solutions in Tunable Networks
Guzman, M., Martins, F., Stern, M., & Liu, A. J. (2025) · Physical Review X · doi:10.1103/f2hb-c9s1 · Cited by 2
Learning via Mechanosensitivity and Activity in Cytoskeletal Networks
Banerjee, D. S., Falk, M. J., Gardel, M. L., Walczak, A. M., Mora, T., & Vaikuntanathan, S. (2025) · PRX Life · doi:10.1103/s7tg-6q4q · Cited by 5
Zhu, C., Zhang, S., Luo, T., Ding, Y., Li, B., Zhu, G., & Shen, X. (2025) · Small Structures · doi:10.1002/sstr.202500201 · Cited by 1
Simple biological controllers drive the evolution of soft modes
Russo, C. J., Husain, K. B., Ranganathan, R., Pincus, D., & Murugan, A. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2523032123 · Cited by 4
“Selection for allosteric communication can lead to soft modes (18, 20), as can the need for low-dimensional internal models of the external world (22, 23).”
Epithelial convergent extension as a tuning process
Arzash, S., Liu, A. J., & Manning, M. L. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2532099123 · Cited by 1
Hamiltonian bridge for scale-dependent optimal control of particles and fields
Krishnan, V., Sinha, S., & Mahadevan, L. (2025) · Newton · doi:10.1016/j.newton.2025.100365 · Cited by 1
Multimodal oscillator networks learn to solve a classification problem
de Bos, D., & Serra‐Garcia, M. (2026) · npj Metamaterials · doi:10.1038/s44455-025-00015-4 · Cited by 4
“Although spring networks (and mathematically analogous resistor networks) are relatively simple physical systems, they are able to solve classification [25] and allostery [26] tasks; when they are augmented with nonlinearity, they are in fact computationally universal [27].”
Irregular metamaterial networks
Wytock, T. P., Daraio, C., Jaeger, H. M., Schuh, C. A., Valdevit, L., Vitelli, V., & Motter, A. E. (2026) · Nature Reviews Materials · doi:10.1038/s41578-026-00936-0 · Cited by 1
“Neverthe-less, the average node degree at the percolation transition — at which a giant connected component forms — increases from 1 if the decay exponent α becomes sufficiently large.”
Double descent without digital computation
Dillavou, S., Rocks, J. W., Wycoff, J. F., Liu, A. J., & Durian, D. J. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2604421123 · Cited by 1
Shobair, M., Popov, K., Dang, Y. L., He, H. X., Stutts, M. J., & Dokholyan, N. V. (2018) · Journal of Biological Chemistry · doi:10.1074/jbc.ra117.000604 · Cited by 1
“Recently, engineering studies of mechanically coupled networks have used allosteryinspired approaches to find strategies to create new functionalities in networks (30, 31).”
BackgroundBhaumik, H., & Hexner, D. (2024) · Physical Review Materials · doi:10.1103/physrevmaterials.8.l082601 · Cited by 1
Training allostery-inspired mechanical response in disordered elastic networks
Gowen, S. D. (2025) · Soft Matter · doi:10.1039/d4sm01340a · Cited by 1
Three-stage multiscale modelling of the NMDA neuroreceptor
Palma, F. D., Sauro Succi, A., Sterpone, F., Marco Lauricella, A., Pérot, F., & Melchionna, S. (2021) · Molecular Physics · doi:10.1080/00268976.2021.1928312 · Cited by 1
Almost-rigidity of frameworksPreprint
Holmes-Cerfon, M. C., Theran, L., & Gortler, S. J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.03802 · Cited by 1
“ge in Brisbane [1]; to designing the microstructure of materials with novel properties [4, 5, 29, 31], to studying arrangements of jammed particles [11, 19, 26], to understanding allostery in biology [33, 40]; to studying the properties of molecules [20, 34, 39]; to analyzing the structure of proteins [23]; to studyin…”
BackgroundModeling myosin with interacting linkagesPreprint
Omabegho, T. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.20.440673 · Cited by 2
Edgework: Viewing curiosity as fundamentally relationalPreprint
Zurn, P., Zhou, D., Lydon‐Staley, D. M., & Bassett, D. S. (2021) · doi:10.31234/osf.io/crzae · Cited by 1
“Building on the study of linkages and transformable metamaterials [84, 78, 85], one can calculate the degrees of freedom of growing knowledge networks, reflecting the flexibility of a set of related concepts in d dimensional concept space.”
BackgroundFree-then-freeze: transient learning degrees of freedom for introducing function in materialsPreprint
Hagh, V. F., Nagel, S. R., Liu, A. J., Manning, M. L., & Corwin, E. I. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.10846 · Cited by 1
“Appropriate training protocols can lead the disordered materials to occupy desired metastable states with useful properties [5, 8, 9, 11, 40–42].”
BackgroundOn the Emergence of Single Versus Multi-State AllosteryPreprint
Rouvière, E., Ranganathan, R., & Rivoire, O. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4119813 · Cited by 1
Gagnér, V. A., Jensen, M., Olsson, T. N., Cabello-Sánchez, J., Bengtsson, Å. U. J., Ekström, J. C., Zhang, Z., Jurgilaitis, A., Kroon, D., Pham, V., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.29.596429 · Cited by 1
Realizations of Isostatic Material Frameworks
Sadjadi, M., Hagh, V. F., Kang, M., Sitharam, M., Connelly, R., Gortler, S. J., Theran, L., Holmes-Cerfon, M. C., & Thorpe, M. F. (2021) · physica status solidi (b) · doi:10.1002/pssb.202000555
Perspectives on opportunities in experimental soft-matter sciencePreprint
Nagel, S. R. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.10013
“The answer appears to be in the affirmative (Rocks et al., 2016).”
BackgroundThirumalai, D., & Hyeon, C. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.07981
“We use the analogy to phonon propagation in solids to describe the SPM method, first introduced by Zheng and coworkers [25, 26] (see for related ideas in biophysics [30–33] and other areas [34, 35]) for determining the network of residues that propagate signals upon ligand (ATP and/or SP) binding to specific regions i…”
MethodSouslov, A., & Vitelli, V. (2019) · Nature Physics · doi:10.1038/s41567-019-0500-1
Adaptive and Reconfigurable Architected Materials Driven by Electrochemistry
Xia, X. (2019) · CaltechTHESIS (California Institute of Technology) · doi:10.7907/q092-p711
Symmetry, Rigidity, and Allosteric Signaling: From Monomeric Proteins to Molecular MachinesPreprint
Thirumalai, D., Hyeon, C., Zhuravlev, P. I., & Lorimer, G. H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.04969
Poisson's ratio and angle bending in spring networksPreprint
Pashine, N., Reid, D., Shen, M., Pablo, J., & Nagel, S. R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.03198
“[2] Jason W Rocks, Nidhi Pashine, Irmgard Bischofberger, Carl P Goodrich, Andrea J Liu, and Sidney R Nagel.”
Background(2021) · SciPost Physics · doi:10.21468/scipost.report.2672
(2020) · SciPost Physics Core · doi:10.21468/scipost.report.1539
(2019) · SciPost Physics Core · doi:10.21468/scipost.report.1398
(2019) · SciPost Physics Core · doi:10.21468/scipost.report.1354
(2020) · SciPost Physics Core · doi:10.21468/scipost.report.1555
Mitchell, N. (2020) · Springer theses · doi:10.1007/978-3-030-36361-1_1
(2021) · SciPost Physics · doi:10.21468/scipost.report.2772
Protein as evolvable functionally constrained amorphous matter
Tripathy, M., Srivastava, A. K., Sastry, S., & Rao, M. (2022) · Journal of Biosciences · doi:10.1007/s12038-022-00313-3
Memory capacity of flow network morphology
Bhattacharyya, K. (2023) · doi:10.53846/goediss-9722
Predicting the mechanical properties of spring networks
Grossman, D., & Boudaoud, A. (2026) · Physical Review Research · doi:10.1103/17sg-gzhl
“This is true for mechanical systems such as meta materials [26–28] and coarse grained mechanical models[25], chemical systems such as self assemblies [10, 19–21], and many biological systems as well [23, 24].”
Topological mechanics without the topology: A universal, model-free approach to mechanical response
Rocklin, D. Z. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2322681121
Shortcuts to flexible structures
Rocklin, D. Z. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2420375121
Dissipation in Multi-component Nanodevices
Lacroix, T. (2025) · Springer theses · doi:10.1007/978-3-031-83143-0_7
Mechanical Model of Erythrocyte: Allostery at the Cellular Level?
Gevorkian, S. G., Gevorgyan, D. S., & Dyakov, Y. A. (2025) · Russian Journal of Physical Chemistry B · doi:10.1134/s1990793125700514
Discrete protein dynamics enable long-range communicationPreprint
Katz, A. J., Sari, L., Manley, L. J., Toprak, E., & Lin, M. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.08.681220
Bohte, F., Louvet, T., Maillou, V., & Serra‐Garcia, M. (2025) · Physical Review Applied · doi:10.1103/5hhw-sk9c · Cited by 1
Analysis of disordered trusses using network Laplacians
Fancher, S., Sarpangala, N., Purohit, P. K., & Katifori, E. (2025) · Soft Matter · doi:10.1039/d5sm00619h
Designing unique mechanical modes through an extension of the quantum hopping method
Nie, H., Shen, X., & Xu, L. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2423603122
Lavín-Pallero, E., & Ruiz-García, M. (2025) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/ae1b86 · Cited by 1
Deterministic interrelation between elastic moduli in critically elastic materials
Jeon, H., Sadjadi, M., & Hagh, V. F. (2025) · Physical review. E · doi:10.1103/7tjt-m8s6
“This makes critically elastic systems a vastly tunable material in the case where single bond removal mechanisms can be implemented in an experimental system [19, 24].”
Tomioka, H., Inoue, K., Kuniyoshi, Y., & Nakajima, K. (2025) · Advanced Robotics Research · doi:10.1002/adrr.202500159 · Cited by 2
Harnessing intuitive local evolution rules for physical learning
Ezraty, R., Stern, M., & Rubinstein, S. M. (2026) · Physical review. E · doi:10.1103/51dl-czj3 · Cited by 2
“Low dimensional regression with a small training set and no test set had been related to problems of allostery in proteins and flow networks [30, 46, 47].”
Allosteric Logic GatePreprint
Cois, D., Rahi, S. J., & Rios, P. D. L. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.29.721721
Wave-based neuromorphic circuit networks: tunable 2D transmission-line metamaterials [Invited]
Thakkar, S., & Grbic, A. (2026) · Optical Materials Express · doi:10.1364/ome.599576
“This behavior is closely related to the phenomenon of allostery observed in biological systems, which refers to the ability of a structured system to transmit information between spatially distinct sites through its internal couplings [30,31].”
Ebrahimi‐Nejad, S., Rashidmanafi, A., Ravanbod, M., & Mollajafari, M. (2026) · Materials & Design · doi:10.1016/j.matdes.2026.116490
Design of a minimal, allosteric, and ATPase-like machine using mechanical linkagesPreprint
Omabegho, T. (2026) · eLife · doi:10.7554/elife.111064.1
Metadata
- Authors
- Rocks, J. W., Pashine, N., Bischofberger, I., Goodrich, C. P., Liu, A. J., & Nagel, S. R.
- Affiliations
- University of Pennsylvania (US)
- University of Chicago (US)
- Massachusetts Institute of Technology (US)
- Harvard University (US)
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2017-02-21
- Volume
- 114
- Issue
- 10
- Pages
- 2520-2525
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC5347623
- OpenAlex
- W2485975781
- Cited by
- 204
- References
- 33
- Citations by year
- 2026: 17 · 2025: 34 · 2024: 18 · 2023: 22 · 2022: 12 · 2021: 27 · 2020: 22 · 2019: 29 · 2018: 14 · 2017: 8
- FWCI
- 15.50
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- arXiv (Cornell University)
- Funders
- U.S. Department of Energy (DE-FG02-05ER46199, DE-FG02-03ER46088)
- National Science Foundation Graduate Research Fellowship Program (DGE-1321851)
- Simons Foundation (305547, 327939)
- DOC | National Institute of Standards and Technology (60NANB15D055)
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › Advanced Materials and Mechanics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Cellular Mechanics and Interactions
- Physical Sciences › Physics and Astronomy › Acoustics and Ultrasonics › Acoustic Wave Phenomena Research
- Keywords
- allosteric regulation, mechanical metamaterials, spring networks, strain amplification, disordered networks, protein conformational change, network design
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