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Active assembly and non-reciprocal dynamics of elastic membranes

Nature Physics (2026)Cite this article

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Abstract

Equilibrium self-assembly and conventional materials processing techniques fall far short of mimicking dynamic self-actuating processes that are commonplace throughout biology. Here, to bridge the gap between living and synthetic matter, we study passive adhesive non-thermal actin fibres immersed in an active microtubule-based fluid. We show that autonomous chaotic flows power non-equilibrium fibre dynamics, thus inducing collisions, generating connections and weaving a membrane-like elastic network. The ensuing active assembly generates a hierarchy of shapes, structures and dynamical processes spanning nanometres to centimetres. Ultimately, it generates an active membrane that exhibits global limit cycles induced by a non-reciprocal coupling between deformations of the elastic membrane and the alignment axis of the nematic active fluid. Our work merges self-assembly with active matter to demonstrate self-processing materials wherein hierarchical life-like structures and dynamics emerge from an initially structureless suspension.

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Fig. 1: Active assembly of elastic membranes.
Fig. 2: Active assembly from preformed bundles.
Fig. 3: Active assembly from a homogeneous suspension.
Fig. 4: Active membrane bending and stretching dynamics.
Fig. 5: System-spanning oscillations.

Data availability

Representative data from this study are available via Zenodo at https://doi.org/10.5281/zenodo.17215226 (ref. 67). Source data are provided with this paper.

Code availability

The code used for data analysis is available via Zenodo at https://doi.org/10.5281/zenodo.17215226 (ref. 67).

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Acknowledgements

We thank L. Frechette, B. Chakraborty and X. Mao for insightful discussions. This work was primarily supported by the US Department of Energy (Grant No. DE-SC0022291 to J.B., S.R., I.K., S.F. and Z.D.). F.B. acknowledges support from a Gordon and Betty Moore post-doctoral fellowship (Award No. 2919). The theoretical work was supported in part by the National Science Foundation through Grant No. PHY-2309135 to the Kavli Institute for Theoretical Physics (M.B.). V.V. and S.C. acknowledge partial support from the Army Research Office (Grant Nos. W911NF-22-2-0109 and W911NF-23-1-0212), the National Science Foundation through the Center for Living Systems (Grant No. 2317138), the National Institute for Theory and Mathematics in Biology, the Simons Foundation and the Chan Zuckerberg Foundation.

Author information

Author notes

  1. These authors contributed equally: John Berezney, Sattvic Ray, Itamar Kolvin.

Authors and Affiliations

  1. Department of Physics, Brandeis University, Waltham, MA, USA

    John Berezney & Seth Fraden

  2. Department of Physics, University of California at Santa Barbara, Santa Barbara, CA, USA

    John Berezney, Sattvic Ray, Fridtjof Brauns, Mark Bowick & Zvonimir Dogic

  3. School of Physics, Georgia Institute of Technology, Atlanta, GA, USA

    Itamar Kolvin

  4. Kavli Institute of Theoretical Physics, Santa Barbara, CA, USA

    Fridtjof Brauns & Mark Bowick

  5. Leinweber Institute for Theoretical Physics, University of Chicago, Chicago, IL, USA

    Sihan Chen & Vincenzo Vitelli

  6. James Frank Institute, University of Chicago, Chicago, IL, USA

    Sihan Chen & Vincenzo Vitelli

  7. Interdisciplinary Program in Quantitative Biosciences, University of California, Santa Barbara, CA, USA

    Zvonimir Dogic

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Contributions

J.B., S.R., I.K. and Z.D. conceptualized the work. J.B., S.R. and I.K. performed the experiments. J.B., S.R., I.K. and F.B. analysed the data. F.B., S.C., M.B. and V.V. developed the theoretical analysis. All authors contributed to the writing of the paper.

Corresponding author

Correspondence to Zvonimir Dogic.

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The authors declare no competing interests.

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Peer review information

Nature Physics thanks Jennifer Ross and Ishant Tiwari for their contribution to the peer review of this work. Peer reviewer reports are available.

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Supplementary information

Supplementary Information (download PDF )

Theoretical model, experimental methods, Figs. 1–5, Videos 1–9 and video captions.

Reporting Summary (download PDF )

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Supplementary Video 1 (download MP4 )

Active MT fluid rearranges the passive actin network in the quasi-2D chamber. Preformed actin–fascin bundles (green) form anetwork and contract into a membrane under the influence of an active MT fluid (magenta). Rendering from a 3D imageacquired with a spinning-disc confocal microscope. Same conditions as in Fig 1a–e.

Supplementary Video 2 (download MP4 )

Active assembly of actin–fascin bundles. Maximum intensity of the z-projection of actin–fascin bundles forming a networkunder the influence of active MT flows (1.5 μM actin, 0.5 μM fascin, active buffer 1). Same sample as in Fig. 2. The averagetranslation within the displayed region was removed to highlight network reconfiguration during periods of large in-planemotion.

Supplementary Video 3 (download MP4 )

Actin assembly from very dilute preformed bundles. Active assembly generates a percolated actin network with a very largemesh size. Large deformations driven by the active fluid indicate that the percolated network lacks a finite elastic modulus andis floppy (1.0 μM actin, 0.33 μM fascin, active buffer 1).

Supplementary Video 4 (download MP4 )

Active assembly of actin–fascin network from unbundled actin. The active fluid induces both bundling and network assembly. Inthe initial state, the sample consists of unbundled actin (1.5 μM actin,  μM fascin, active buffer 2). Same sample as in Fig. 3.

Supplementary Video 5 (download MP4 )

Height fluctuations of actin membrane. Top left: 3D rendering of segmented actin network. Colour indicates local height (samecolour bar as Supplementary Fig. 1a). Top right: reconstruction of the surface based on actin segmentation. Bottom: sideview of the actin membrane (x – z slice).

Supplementary Video 6 (download MP4 )

Height fluctuations of actin membrane. Top left: 3D rendering of segmented actin network. Colour indicates local height (samecolour bar as Supplementary Fig. 1a). Top right: reconstruction of the surface based on actin segmentation. Bottom: sideview of the actin membrane (x – z slice).

Supplementary Video 7 (download MOV )

Macroscopic shear oscillations. System-sized shear oscillations emerge in the actin membrane (3.0 μM actin, 2.2 μM fascin,active buffer 2). Same sample as in Fig. 5a–c.

Supplementary Video 8 (download MOV )

Simulation of actin membrane oscillations. Velocity field (arrows) and vorticity (colour) showing self-excited waves in asimulation with w / ℓv−el = A = 1.6 and channel length L = 4w.

Supplementary Video 9 (download MOV )

Shear oscillations emerge in membranes above the critical width. Width dependence of system-sized shear oscillations (6.0 μMactin, 2.0 μM fascin, active buffer 1). Same sample as in Fig. 5e,f.

Source data

Source Data Fig. 2 (download XLSX )

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Source Data Fig. 3 (download XLSX )

Numerical source data for figure.

Source Data Fig. 4 (download XLSX )

Numerical source data for figure.

Source Data Fig. 5 (download XLSX )

Numerical source data for figure.

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Berezney, J., Ray, S., Kolvin, I. et al. Active assembly and non-reciprocal dynamics of elastic membranes. Nat. Phys. (2026). https://doi.org/10.1038/s41567-026-03215-5

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