Skip to main content
Scientific Reports logoLink to Scientific Reports
. 2021 Apr 12;11:7985. doi: 10.1038/s41598-021-87073-6

Undulation of a moving fluid membrane pushed by filament growth

Hiroshi Noguchi 1,2,, Olivier Pierre-Louis 2
PMCID: PMC8041810  PMID: 33846435

Abstract

Biomembranes experience out-of-equilibrium conditions in living cells. Their undulation spectra are different from those in thermal equilibrium. Here, we report on the undulation of a fluid membrane pushed by the stepwise growth of filaments as in the leading edge of migrating cells, using three-dimensional Monte Carlo simulations. The undulations are largely modified from equilibrium behavior. When the tension is constrained, the low-wave-number modes are suppressed or enhanced at small or large growth step sizes, respectively, for high membrane surface tensions. In contrast, they are always suppressed for the tensionless membrane, wherein the wave-number range of the suppression depends on the step size. When the membrane area is constrained, in addition to these features, a specific mode is excited for zero and low surface tensions. The reduction of the undulation first induces membrane buckling at the lowest wave-number, and subsequently, other modes are excited, leading to a steady state.

Subject terms: Biological physics; Statistical physics, thermodynamics and nonlinear dynamics; Membrane biophysics


In nonequilibrium, surfaces and interfaces often exhibit different fluctuations from thermal equilibrium. The fluctuations of growing surfaces have been studied by the Kardar–Parisi–Zhang equation and other models17. Scaling laws for the spatial and temporal evolution of surface roughness and correlation functions have been intensively discussed in various physical systems. On the other hand, larger undulations of fluid membranes have been observed out of equilibrium than in equilibrium811. For example, red blood cells exhibit non-thermal fluctuations10. However, nonequilibrium membrane undulations have only been investigated under limited conditions.

In living cells, membranes often interact with protein filaments (e.g., actin12,13 and microtubules14,15), which push and/or pull the membranes. For example, actin filaments grow at the front side of crawling cells and push the membrane forward. This membrane motion has been intensively studied by Brownian ratchet theories and simulations1528. However, membrane fluctuations have not yet been studied. In most studies reported in the literature1624, the membrane is modeled as a flat rigid surface; thus, no fluctuations are accounted for. Although the membrane fluctuations are assumed to be a Gaussian distribution of the membrane height in Ref.25, the spatial correlation was not accounted for. Membrane fluctuations were also considered with a one-dimensional lattice model2628, but the surface tension was treated by a rough approximation of the rectangular contour length in the absence of the bending energy. Thus, the effects of the bending rigidity have not yet been investigated.

In this study, we report on a minimal model that describes the fluctuations of a fluid membrane with the bending rigidity in interaction with growing filaments. We examine the fluctuations of the membrane pushed by filament growth using Monte Carlo (MC) simulations. The filaments grow by a stepwise random walk under the membrane and have an excluded-volume interaction with the membrane. The analysis of the fluctuation spectrum reveals that membranes deviate from the well-known equilibrium behavior under either condition in which the bending energy or surface tension is dominant. Fluctuations are enhanced or suppressed depending on the conditions. Moreover, enhancement of a specific wave-number is obtained when the membrane area is constrained.

Results

The filaments are modeled as a square array of columns growing vertically in the z direction by the addition of new growth units of Δzfil. The filaments grow only upward in the +z direction with a probability pfil, as shown in Fig. 1. The filament retraction and interactions between filaments are not considered for simplicity. The fluid membrane is described by a continuous height z, defined on a square mesh above each filament, and is moved stochastically upward and downward by the Metropolis MC method. Unless otherwise specified, the number N=642=4096 of filaments (as well as membrane vertices) and the filament growth probability pfil=0.5 are used (see “Methods” for more details). The results are displayed with the thermal energy kBT and lateral distance between neighboring filaments fil for the energy and length units, respectively.

Figure 1.

Figure 1

Motion of the membrane and filaments. (a) Schematic of the membrane and filaments. According to the Metropolis MC method, the membrane moves with a vertical (z) step taken from a uniform random number in [-Δzmb,Δzmb]. The filament grows stepwise with a probability pfil. (b) Snapshot of a part of the membrane and filaments at Δzfil=0.4, pfil=0.5, γ=0, and N=4096 in case I (tension constraint). The red mesh and blue bars represent the membrane and filaments, respectively. The origin of the z coordinate is taken at the minimum value of the filament position zfil.

Thermal equilibrium

In thermal equilibrium (no interactions with the filaments), the height z=h(x,y) of the membrane exhibits a static spectrum that is controlled by the bending energy and tension2932:

|h(q)|2=1κq4+γq2, 1

where q is the norm of the wave-vector in the xy plane, κ is the bending rigidity, and γ is the mechanical surface tension33 conjugated to the projected membrane area Axy=N, respectively (see Fig. 2a). For low wave-numbers (qγ/κ), the tension is the dominant factor in determining membrane undulation, while the bending rigidity is at high wave-numbers (qγ/κ). In this study, κ=10 is used. Thus, for γ=10, surface tension is dominant for most of the wave-number range; for γ=1, the tension and bending energies are dominant for low and high wave-numbers, respectively. To maintain the surface tension or membrane area, we employ two types of constraints: case I (tension constraint), in which the intrinsic surface tension γit conjugated to the real membrane area A is imposed by the potential Uit=γitA, and case II (area constraint), in which area A is constrained by a harmonic potential Uar=(KA/2A0)(A-A0)2, where KA is the area compression modulus. In these cases, γit or A0 is adjusted to obtain the spectrum given by Eq. (1). Note that γit is slightly greater than γ because of the membrane undulation as discussed in the literature3336. In case I, the membrane is a part of a cell membrane or liposome and the rest part of the membrane can act as a membrane reservoir to change the area, while no reservoir exist in case II. Both types of constraints lead to the same static spectrum as shown in Fig. 2a.

Figure 2.

Figure 2

Spectra of undulation modes |h(q)|2. (a) Membrane under thermal equilibrium at γ=0, 1, and 10. The symbols (×, , and +) and (, , and ) represent the spectra in cases I (tension constraint) and II (area constraint), respectively. Solid lines show 1/(κq4+γq2) with κ=10. (b, c) The undulation spectra of the membrane (red diamonds) and filament tips (blue cross marks) of tensionless membranes (γ=0) at Δzfil=0.004 for (b) case I and (c) case II. The error bars are smaller than the symbol size.

Steady-state velocity

As the filaments push the membrane upward, the membrane and filament tips relax to a steady-state, in which they move at the same speed vz. The growth speed vz exhibits a non-monotonic dependence on the size of the filament growth units Δzfil. For intermediate values of Δzfil, a maximum speed is reached concomitantly with a minimum in the average of the gap distance zgap=zmb-zfil between the membrane and filament tips, as shown in Fig. 3. Hence, the averaged motion is not sensitive to the surface tension γ or to the constraint types (case I or II). In all cases a similar dependence on Δzfil is found.

Figure 3.

Figure 3

Mean growth velocity vz and mean distance between the membrane and filament tips zgap . In (a) and (b), these quantities are plotted as a function of Δzfil for γ=0, 1, and 10. The solid and dashed lines represent the data in cases I (tension constraint) and II (area constraint), respectively. In (c) and (d), the data around the maximum velocity and minimum distance are enlarged, and the mean filament growth distance pfilΔzfil normalized by the mean distance Δzm0 of the membrane motion at one MC step. The error bars are smaller than the line thickness.

The two asymptotic regimes of large and small Δzfil present distinct behaviors. For a large step of Δzfil, the mean growth velocity vz and the gap distance zgap are independent of pfil and determined by Δzfil, as shown in Fig. S1, because frequent growth trials of the filaments are rejected until the membrane moves by a sufficiently large distance for the filament growth units to be inserted. This corresponds to the diffusion limit in the Brownian ratchet model16,17. On the other hand, the behavior in response to small steps of Δzfil is predominantly determined by the ratio of the filament growth rate pfilΔzfil to the mean membrane step size Δzm0=pmbΔzmb/2, where pmb is the mean acceptance ratio of the membrane motion in the absence of filaments (pmb0.5 in our simulations, see “Methods”). The velocity vz for pfil=0.25, 0.5, and 1 merge into one curve when they are plotted with the horizontal axis of pfilΔzfil/Δzm0 (see Fig. S1d), whereas a small difference remains in zgap (see Fig. S1e). The velocity approaches the growth velocity of free filaments, pfilΔzfil, for Δzfil0, so that this corresponds to the reaction limit in the Brownian ratchet model16,17. The maximum velocity and minimum distance are obtained at pfilΔzfilΔzm0, i.e., when the motion of the membrane opens gaps corresponding to the size of filament growth units at a frequency which is similar to that of the insertion of new growth units (see Fig. 3c and d as well as Fig. S1d and e). Furthermore, irrespective of the average value of the gap, the probability distribution P(zgap) of the gap zgap decreases monotonically with increasing zgap and exhibits a stepwise discontinuity at zgap=Δzfil, because the filament growth is rejected at a smaller distance (see Fig. S2).

Undulation spectrum

The pushed membrane exhibits a different undulation spectrum from that of equilibrium. Figure 2b and c show the spectra of the tensionless membrane (γ=0) at Δzfil=0.004. At a low wave-number q (i.e., long wavelength), the membrane and filament surface connecting their tips have identical spectra. On the other hand, at a high q (i.e., short wavelength), the membrane spectra are not modified from the equilibrium spectrum in Fig. 2a, and the filament surface exhibits a flat spectrum of white noise, as in the absence of membrane–filament interactions. The threshold qsep of separation of the spectra of the membrane and filament surface is correlated to the gap distance. With increasing Δzfil, qsep increases, reaches a maximum when zgap is minimum, and then decreases (see Fig. S3). Up to the maximum of qsep, the filament spectrum at high q is flat. However, above the maximum, the filament spectrum is not flat at high q; thus, the spectrum is modified by the membrane–filament interactions (see Fig. S3c). These high q behaviors are not qualitatively changed by the choice of constraints (cases I or II). However, a distinct difference is found in the spectra of low q. In case I (tension constraint), the membrane undulation are suppressed at low q. In contrast, the spectrum is enhanced at q0.07π in case II (area constraint; compare Fig. 2b and c). Hence, the membrane buckles to maintain the membrane area A. A similar nonequilibrium buckling scenario resulting from the suppression of membrane fluctuations has been discussed as a mechanism that induces instability of the lamellar phase in shear flow37. Note that the membrane–filament interactions cannot be interpreted by the effective change of the surface tension, since the spectrum shapes in Fig. 2b and c are different from that for any value of γ.

To further examine the spectrum changes, Fig. 4 shows the membrane spectrum normalized by the equilibrium spectrum as |h(q)|2/|heq(q)|2. Let us first discuss case I, with tension constraint. For tensionless membranes low-q undulations are suppressed for all values of Δzfil, while it is slightly enhanced at the intermediate wave-number (q0.3π) for large Δzfil (Figs. 4a and 5a). A rough measure of the range of q for which the fluctuations are suppressed (i.e., |h(q)|2/|heq(q)|2<1) during growth can be obtained from the evaluation of the value of q for which |h(q)|2/|heq(q)|2=0.6. This value of q plotted as the solid lines in Fig. 5c, is seen to exhibit a maximum for Δzfil=0.02. Hence, the low q range where the modes are suppressed is widest around Δzfil=0.02. This value is smaller than that corresponding to the minimum of the gap distance Δzfil=0.065. This maximum range of suppressed modes changes only slightly (see Fig. 5c) at higher γ. However, when pfil is increased, the maximum of the suppression range increases while the corresponding value of pfilΔzfil is constant (see Fig. S4). In addition to this suppression effect at small Δzfil, an enhancement effect is seen for large Δzfil for high tensions. For γ=10 the undulation increases at q1 with increasing Δzfil (see Figs. 4 and 5a), and a steep increase is obtained at Δzfil1.

Figure 4.

Figure 4

Undulation spectra normalized by the equilibrium values |h(q)|2/|heq(q)|2 for Δzfil=0.002, 0.01, 0.06 (or 0.065), 0.4, and 0.8. (ac) Case I (tension constraint) and (df) case II (area constraint) for (a, d) γ=0; (b, e) γ=1; and (c, f) γ=10. The blue triangles represent the data at Δzfil=0.06 or 0.065, where the distance zgap has the minimum value. The black pluses represent the data of the membrane with the totally asymmetric diffusion (tamb). The error bars are smaller than the symbol size except for low wave-numbers at small Δzfil in case II.

Figure 5.

Figure 5

Dependence of the spectrum shape on Δzfil. (a, b) Normalized undulation amplitude |h(q)|2/|heq(q)|2 at the lowest wave-number q=0.03125π for (a) case I (tension constraint) and (b) case II (area constraint). (c) The upper four lines represent the wave-number q at |h(q)|2/|heq(q)|2=0.6. The solid lines represent the data at γ=0, 1, and 10 in case I from top to bottom. The upper and lower dashed lines represent the data at γ=0 in case II for |h(q)|2/|heq(q)|2=0.6 and the peak of |h(q)|2/|heq(q)|2, respectively.

As a summary, for increasing Δzfil, there are first a range of modes that are suppressed. This range reaches a maximum for some value of Δzfil (here pfilΔzfil=0.01), and then decreases. Upon a further increase in Δzfil, the low-q modes are enhanced for non-vanishing tensions.

Under the area constraint (case II), a similar trend of suppression of low q modes with a maximum at small Δzfil together with an enhancement of low q modes at large Δzfil is found. However, there is an important superimposed feature: a peak at low q appears in the spectrum ratio for small γ. The peak can be clearly seen in Fig. 4d for γ=0. The position of this peak, shown as the lower dashed line in Fig. 5c, shifts to a higher q with increasing Δzfil. For γ=1, the undulations are similar to those of γ=10 and γ=0 in cases I and II, respectively. Globally, the membrane undulation is strikingly changed by interactions with the filaments and strongly depends on the filament growth rate and membrane constraint types.

Moreover, we examined a totally asymmetric membrane (tamb) motion as a limited condition, in which the filaments are in complete contact with the membrane, and all of the downward membrane motion are rejected. The tamb spectra are close to those of the minimum gap distance, as shown in Fig. 4. Thus, the membrane under the minimum gap distance condition is well approximated by the asymmetric membrane motion. In this limit, the undulation due to the bending energy is modified (γ=0 and 1), while that due to the tension is not (γ=10). The tamb steady velocity vz is 50% higher than that of the minimum gap distance condition: vz=0.0183 (0.0190), 0.0180 (0.0182), and 0.0162 (0.0164) for γ=0, 1, and 10 in case I (case II), respectively. This difference is reasonable as a smaller minimum distance gives a higher velocity, as shown in Fig. S1.

Vertical span

In the studies of surface growth, the time evolution and finite-size scaling of the surface thickness (or width) have been analyzed16. Here, we call this the surface vertical span to avoid confusion with the thickness of the membrane itself, and defined it as zspan2=iN(zi-zG)2/N, with zG=iNzi/N. Due to Parseval’s identity, it corresponds to the sum of the spectrum over all modes.

Figure 6 shows zspan2 as a function of Δzfil and the system size N. For very small or very large Δzfil, the filament vertical span is greater than the membrane span, reflecting the difference of the undulation spectra at high q. The Δzfil dependence of zspan2 is roughly captured by that of the undulation spectra at a low q (compare Figs. 5a, b and 6a, b). At a small Δzfil, zspan2 provides slightly large and small values for pfil=0.25 and 1, respectively, while they merge well at large Δzfil (see Fig. S1c and f). Under thermal equilibrium at γ=0, the membrane vertical span linearly increases as zspan2N following the amplitude of the lowest undulation mode30. For the pushed membrane, this increase is reduced for all values of Δzfil (see Fig. 6c). On the other hand, for γ=10, a greater increase is obtained at large Δzfil (see Fig. 6d). This observation reflects an amplitude increase in the lowest mode as seen in Fig. 5a.

Figure 6.

Figure 6

Vertical span zspan2 of the membrane and filament surfaces. (a, b) Dependence on Δzfil for γ=0, 1, and 10 in (a) case I and (b) case II. The solid and dashed lines represent the data for the membrane and filaments, respectively. (c, d) Dependence on system size N for (c) γ=0 and (d) γ=10 in case I. The thick magenta lines in (c) and (d) represent the data at thermal equilibrium. The dashed black line in (c) indicates zspan2=N/8π3κ, which overlays the thick magenta line.

The excess membrane area increases as A/Axy-1=(kBT/8πκ)ln(N)+b for the tensionless membrane (γ=0) under thermal equilibrium30,38,39, where b is a constant. The modification of this size dependence by the filament growth is similar to that in the vertical span, and the excess area increase is largely reduced at γ=0 (see Fig. S5). Note that the mean velocity vz and gap distance zgap exhibit negligibly small size dependence.

Membrane dynamics

Finally, we describe the time evolution. After the filaments contact the membrane, both surfaces relax into steady states. The choice of the initial conformations, such as a flat or thermal equilibrium membrane conformation does not lead to any notable difference in the subsequent dynamics. In most of the conditions, the undulations increase monotonously on average despite large fluctuations, as shown in Fig. 7b. However, a characteristic evolution is found at Δzfil0.01 for γ=0 under the area constraint (see Fig. 7a and c). First, the membrane and filament surface buckle together and form a bump, as shown in the middle snapshot in Fig. 7a. This bump leads to a strong peak in the first mode of the spectrum |h(q)|2 and in zspan2. Subsequently, the higher modes develop, leading to a steady state (the bottom snapshot in Fig. 7a). The initial suppression of the membrane undulation induces this overshoot buckling, because the lowest mode can evolve the fastest. It is known that a similar buckling at the lowest mode is induced by a negative surface tension40, so that it may be interpreted that an effective negative tension is yielded by the interaction with the filaments. Such buckling could share also similarities with wrinkles forming when a membrane is confined between two walls41, which are also be suppressed by tension42.

Figure 7.

Figure 7

Initial time evolution of the membrane and filaments at γ=0 and Δzfil=0.004. (a) Sequential snapshots corresponding to the data in (c). (b, c) Time evolution of the amplitudes of the first, second, and third modes along the x-axis and the vertical span zspan in (b) case I and (c) case II. The solid and dashed lines represent the data of the membrane and filaments, respectively. The time unit is MC step. In both cases, a thermal equilibrium membrane conformation is used as an initial state; the initial filament surface is flat and contacts to the minimum position of the membrane.

Discussion

We numerically studied the membrane pushed by filament growth. The growth velocity has a maximum at a slightly larger filament-growth step Δzfil than for the minimum gap distance; they are not sensitive to the surface tension and the constraint types. It is found that the membrane undulation spectrum is non-monotonously changed from that of thermal equilibrium. Under the tension constraint, the low-wave-number (long wavelength) undulations are suppressed for the tensionless membrane, and the range of the suppression displays a maximum at a value of Δzfil that is smaller than that corresponding to the maximum velocity and minimum gap. At a high surface tension, this suppression is altered to enhancement with increasing Δzfil. Under the membrane-area constraint, we find similar features as in the tension constraint, but with two main differences for low tensions. First, a peak appears at some intermediate wave-number. Second, the membrane dynamics of relaxation to the steady-state is changed. The suppression of the undulation initially induces the buckling of the membrane at the lowest wave-number to maintain the membrane area. Subsequently, other modes are excited. The spectrum of the filament surface is identical to that of the membrane for low wave-numbers but deviates for high wave-numbers. The smaller the gap distance, the wider the identical region. Consequently, the vertical spans of the membrane and filaments deviate at small or large filament-growth steps.

Let us map the present model parameters to the membrane systems in living cells. In the model, 1/fil2 is the lateral density of the filaments under the membrane. The membrane undulation is cut off at a membrane thickness of approximately 5 nm, so that the available range is fil5 nm. Our simulations clarified that the membrane undulation modes at high wave-numbers are not modified by the filament growth. Hence, we can conclude that the modes higher than 2π/fil should also be unchanged, and we can only consider changes in the lower q modes.

An actin network pushes the plasma membrane of the leading edge in migrating cells12,13, in which the step size for actin growth is Δzfil=2.7 nm17,18,25. The density of actin filaments varies among cell types and with the cell state12,13. For high and low densities of fil=10 nm and 100 nm, Δzfil/fil=0.27 and 0.027 are obtained, respectively. Thus, at a high density, the discreteness of the filament growth may play a significant role in membrane–filament interactions. A microtubule is a hollow cylinder with a diameter of 25 nm, typically consisting of 13 protofilaments, and the growth unit length is Δzfil=8 nm14,19. When the microtubules are closely packed, the average distance is fil7 nm, such that Δzfil/fil1 at the maximum.

Here, we consider the minimal model for filament growth, in which the membrane and filament tips have only a repulsive interaction and no interactions between filaments. In the previous studies, the contact energy between neighboring filaments20,21, attractive potentials between the membrane and filament tips22, acceleration of filament growth by the membrane contact27,28, and filament rigidity43 have been investigated. These interactions can be easily added to the present model to clarify their effects on the undulations. Actin filaments often bind to membranes via curvature-inducing proteins12. Under such conditions, the filament contact is accompanied by the local induction of a membrane spontaneous curvature. Propagation waves can be generated by the coupling of the curvature-inducing proteins with the actin and/or regulatory proteins4446. The effects of such a spontaneous curvature on membrane undulation are also an interesting topic for further studies. The model presented here could therefore be a versatile tool for the investigation of the interactions that may affect the membrane undulations in nonequilibrium conditions.

In previous studies811 on nonequilibrium membrane fluctuations, membrane undulations are always enhanced by active energy inputs. In the present case, the opposite effect (suppression) is also found. Indeed, filament growth can either increase or decrease the undulations depending on the conditions, and in particular, it can induce an excitation at a specific wave-number. Such undulations could stimulate characteristic length-scales or periodic structures that may give rise to filopodia and microspike coupling with the filament assembly.

Methods

The fluid membrane is modeled by a squared mesh of N vertices with periodic boundary condition, as described in Ref.33. The bending energy is given by Ubend=(κ/2)(C1+C2)2dA, where C1 and C2 represent the principal curvatures29,47,48. The Monge representation (z=h(x,y)) is employed, and the curvature is calculated as C1+C2=[(1+hx2)hyy+(1+hy2)hxx-2hxhyhxy]/(1+hx2+hy2)3/2, where the subscripts represent spatial derivatives, such as hx=h/x29. To control the membrane area, the intrinsic tension γit or the membrane area A is constrained by the addition of Uit or Uar to the Hamiltonian in case I or II, respectively. To remove the artificial entropy production by the membrane tilt, a correction potential Ucor=-kBTln(cosθi) is also added to the Hamiltonian, where θi is the angle between the normal vector of the i-th site and the z-axis33. Straight filaments are arranged in the squared lattice (xi,yi) which is shared by the vertices of the membrane (see Fig. 1). Membrane–filament excluded-volume interactions are implemented by forbidding moves leading to inter-penetration (zfil<zmb).

In the filament growth step, one of the filaments is randomly selected, and its tip moves upward with a probability pfil for a step of Δzfil. If the filament overlaps with the membrane vertex, the trial is rejected. The membrane vertex is moved by a vertical step taken from a uniform random number in [-Δzmb,Δzmb], and the motion is accepted or rejected by the Metropolis MC procedure. In each MC step, N trials are performed for both the filaments and membrane. In this study, Δzmb=0.2 and KA/A0=1 are used. For γ=0, 1, and 10, γit=0.44, 1.44, and 10.4 (A0/N=1.036, 1.022, and 1.01 for N=4096 in case II) are used, respectively. In our simulations, we have pmb0.5. More precisely, pmb=0.5226, 0.5128, and 0.4810 for γ=0,1, and 10 in case I and pmb=0.5228, 0.5130, and 0.4813 for γ=0,1, and 10 in case II, respectively. Error bars are calculated from three independent runs.

Supplementary Information

Acknowledgements

This work was supported by JSPS KAKENHI Grant Number JP17K05607. HN acknowledges the visiting professorship program of University of Lyon 1.

Author contributions

H.N. and O.P.-L. conceived the research. H.N. performed the simulation and analyzed data. H.N. wrote the manuscript, which was edited by O.P.-L.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-021-87073-6.

References

  • 1.Barabasi A-L, Stanley HE. Fractal Concepts in Surface Growth. Cambridge University Press; 1995. [Google Scholar]
  • 2.Family F, Vicsek T. Scaling of the active zone in the Eden process on percolation networks and the ballistic deposition model. J. Phys. A. 1985;18:L75–L81. doi: 10.1088/0305-4470/18/2/005. [DOI] [Google Scholar]
  • 3.Kardar M, Parisi G, Zhang Y-C. Dynamic scaling of growing interfaces. Phys. Rev. Lett. 1986;56:889–892. doi: 10.1103/PhysRevLett.56.889. [DOI] [PubMed] [Google Scholar]
  • 4.Sasamoto T, Spohn H. One-dimensional Kardar–Parisi–Zhang equation: An exact solution and its universality. Phys. Rev. Lett. 2010;104:230602. doi: 10.1103/PhysRevLett.104.230602. [DOI] [PubMed] [Google Scholar]
  • 5.Corwin I. The Kardar–Parisi–Zhang equation and universality class. Random Matrices Theory Appl. 2012;1:1130001. doi: 10.1142/S2010326311300014. [DOI] [Google Scholar]
  • 6.Halpin-Healy T, Takeuchi KA. A KPZ Cocktail-shaken, not stirred... J. Stat. Phys. 2015;160:794–814. doi: 10.1007/s10955-015-1282-1. [DOI] [Google Scholar]
  • 7.Cagnetta F, Evans MR, Marenduzzo D. Active growth and pattern formation in membrane–protein systems. Phys. Rev. Lett. 2018;120:258001. doi: 10.1103/PhysRevLett.120.258001. [DOI] [PubMed] [Google Scholar]
  • 8.Prost J, Bruinsma R. Shape fluctuations of active membranes. Europhys. Lett. 1996;33:321–326. doi: 10.1209/epl/i1996-00340-1. [DOI] [Google Scholar]
  • 9.Manneville J-B, Bassereau P, Ramaswamy S, Prost J. Active membrane fluctuations studied by micropipet aspiration. Phys. Rev. E. 2001;64:021908. doi: 10.1103/PhysRevE.64.021908. [DOI] [PubMed] [Google Scholar]
  • 10.Turlier H, et al. Equilibrium physics breakdown reveals the active nature of red blood cell flickering. Nat. Phys. 2016;12:513–519. doi: 10.1038/nphys3621. [DOI] [Google Scholar]
  • 11.Almendro-Vedia VG, et al. Nonequilibrium fluctuations of lipid membranes by the rotating motor protein F1F0-ATP synthase. Proc. Natl. Acad. Sci. USA. 2017;114:11291–11296. doi: 10.1073/pnas.1701207114. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Svitkina T. The actin cytoskeleton and actin-based motility. Cold Spring Harb. Perspect. Biol. 2018;10:a018267. doi: 10.1101/cshperspect.a018267. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Skruber K, et al. Arp2/3 and Mena/VASP require profilin 1 for actin network assembly at the leading edge. Curr. Biol. 2020;30:2651–2664. doi: 10.1016/j.cub.2020.04.085. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Wade RH. On and around microtubules: An overview. Mol. Biotechnol. 2009;43:177–191. doi: 10.1007/s12033-009-9193-5. [DOI] [PubMed] [Google Scholar]
  • 15.Dogterom M, Kerssemakers JWJ, Romet-Lemonne G, Janson ME. Force generation by dynamic microtubules. Curr. Opin. Cell Biol. 2005;17:67–74. doi: 10.1016/j.ceb.2004.12.011. [DOI] [PubMed] [Google Scholar]
  • 16.Howard J. Mechanics of Motor Proteins and the Cytoskeleton. Sinauer Associates; 2001. [Google Scholar]
  • 17.Peskin CS, Odell GM, Oster GF. Cellular motions and thermal fluctuations: The Brownian ratchet. Biophys. J. 1993;65:316–324. doi: 10.1016/S0006-3495(93)81035-X. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Mogilner A, Oster G. The physics of lamellipodial protrusion. Eur. Biophys. J. 1996;25:47–53. doi: 10.1007/s002490050016. [DOI] [Google Scholar]
  • 19.Mogilner A, Oster G. The polymerization ratchet model explains the force-velocity relation for growing microtubules. Eur. Biophys. J. 1999;28:235–242. doi: 10.1007/s002490050204. [DOI] [Google Scholar]
  • 20.Stukalin EB, Kolomeisky AB. Simple growth models of rigid multifilament biopolymers. J. Chem. Phys. 2004;121:1097–1104. doi: 10.1063/1.1759316. [DOI] [PubMed] [Google Scholar]
  • 21.Krawczyk J, Kierfeld J. Stall force of polymerizing microtubules and filament bundles. EPL. 2011;93:28006. doi: 10.1209/0295-5075/93/28006. [DOI] [Google Scholar]
  • 22.Motahari F, Carlsson AE. Thermodynamically consistent treatment of the growth of a biopolymer in the presence of a smooth obstacle interaction potential. Phys. Rev. E. 2019;100:042409. doi: 10.1103/PhysRevE.100.042409. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Whitehouse J, Blythe RA, Evans MR. Width scaling of an interface constrained by a membrane. Phys. Rev. Lett. 2018;121:058102. doi: 10.1103/PhysRevLett.121.058102. [DOI] [PubMed] [Google Scholar]
  • 24.Wood AJ, Blythe RA, Evans MR. Solvable model of a many-filament Brownian ratchet. Phys. Rev. E. 2019;100:042122. doi: 10.1103/PhysRevE.100.042122. [DOI] [PubMed] [Google Scholar]
  • 25.Lan Y, Papoian GA. The stochastic dynamics of filopodial growth. Biophys. J. 2008;94:3839–3852. doi: 10.1529/biophysj.107.123778. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Narasimhan SL, Baumgaertner A. Dynamics of a driven surface. J. Chem. Phys. 2010;133:034702. doi: 10.1063/1.3447384. [DOI] [PubMed] [Google Scholar]
  • 27.Sadhu RK, Chatterjee S. Actin filaments growing against a barrier with fluctuating shape. Phys. Rev. E. 2016;93:062414. doi: 10.1103/PhysRevE.93.062414. [DOI] [PubMed] [Google Scholar]
  • 28.Sadhu RK, Chatterjee S. Actin filaments growing against an elastic membrane: Effect of membrane tension. Phys. Rev. E. 2018;97:032408. doi: 10.1103/PhysRevE.97.032408. [DOI] [PubMed] [Google Scholar]
  • 29.Safran SA. Statistical Thermodynamics of Surfaces, Interfaces, and Membranes. Addison-Wesley; 1994. [Google Scholar]
  • 30.Helfrich W, Servuss R-M. Undulations, steric interaction and cohesion of fluid. Nuovo Cimento D. 1984;3:137–151. doi: 10.1007/BF02452208. [DOI] [Google Scholar]
  • 31.Goetz R, Gompper G, Lipowsky R. Mobility and elasticity of self-assembled membranes. Phys. Rev. Lett. 1999;82:221–224. doi: 10.1103/PhysRevLett.82.221. [DOI] [Google Scholar]
  • 32.Shiba H, Noguchi H. Estimation of the bending rigidity and spontaneous curvature of fluid membranes in simulations. Phys. Rev. E. 2011;84:031926. doi: 10.1103/PhysRevE.84.031926. [DOI] [PubMed] [Google Scholar]
  • 33.Shiba H, Noguchi H, Fournier J-B. Monte Carlo study of the frame, fluctuation and internal tensions of fluctuating membranes with fixed area. Soft Matter. 2016;12:2373–2380. doi: 10.1039/C5SM01900A. [DOI] [PubMed] [Google Scholar]
  • 34.David F, Leibler S. Vanishing tension of fluctuating membranes. J. Phys. 1991;II(1):959–976. [Google Scholar]
  • 35.Farago O, Pincus P. The effect of thermal fluctuations on Schulman area elasticity. Eur. Phys. J. E. 2003;11:399–408. doi: 10.1140/epje/i2003-10049-y. [DOI] [PubMed] [Google Scholar]
  • 36.Gueguen G, Destainville N, Manghi M. Fluctuation tension and shape transition of vesicles: Renormalisation calculations and Monte Carlo simulations. Soft Matter. 2017;13:6100–6117. doi: 10.1039/C7SM01272A. [DOI] [PubMed] [Google Scholar]
  • 37.Zilman AG, Granek R. Undulation instability of lamellar phases under shear: A mechanism for onion formation? Eur. Phys. J. B. 1999;11:593–608. doi: 10.1007/s100510051187. [DOI] [Google Scholar]
  • 38.den Otter WK. Area compressibility and buckling of amphiphilic bilayers in molecular dynamics simulations. J. Chem. Phys. 2005;123:214906. doi: 10.1063/1.2132287. [DOI] [PubMed] [Google Scholar]
  • 39.Noguchi H, Gompper G. Meshless membrane model based on the moving least-squares method. Phys. Rev. E. 2006;73:021903. doi: 10.1103/PhysRevE.73.021903. [DOI] [PubMed] [Google Scholar]
  • 40.Noguchi H. Anisotropic surface tension of buckled fluid membrane. Phys. Rev. E. 2011;83:061919. doi: 10.1103/PhysRevE.83.061919. [DOI] [PubMed] [Google Scholar]
  • 41.To TBT, Le Goff T, Pierre-Louis O. Adhesion dynamics of confined membranes. Soft Matter. 2018;14:8552–8569. doi: 10.1039/C8SM01567H. [DOI] [PubMed] [Google Scholar]
  • 42.Le Goff T, Politi P, Pierre-Louis O. Transition to coarsening for confined one-dimensional interfaces with bending rigidity. Phys. Rev. E. 2015;92:022918. doi: 10.1103/PhysRevE.92.022918. [DOI] [PubMed] [Google Scholar]
  • 43.Weichsel J, Geissler PL. The more the tubular: Dynamic bundling of actin filaments for membrane tube formation. PLoS Comput. Biol. 2016;12:e1004982. doi: 10.1371/journal.pcbi.1004982. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Peleg B, Disanza A, Scita G, Gov N. Propagating cell-membrane waves driven by curved activators of actin polymerization. PLoS ONE. 2011;6:e18635. doi: 10.1371/journal.pone.0018635. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Wu Z, Su N, Tong C, Wu M, Liu J. Membrane shape-mediated wave propagation of cortical protein dynamics. Nat. Commun. 2018;9:136. doi: 10.1038/s41467-017-02469-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Tamemoto N, Noguchi H. Pattern formation in reaction-diffusion system on membrane with mechanochemical feedback. Sci. Rep. 2020;10:19582. doi: 10.1038/s41598-020-76695-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Canham PB. The minimum energy of bending as a possible explanation of the biconcave shape of the human red blood cell. J. Theor. Biol. 1970;26:61–81. doi: 10.1016/S0022-5193(70)80032-7. [DOI] [PubMed] [Google Scholar]
  • 48.Helfrich W. Elastic properties of lipid bilayers: Theory and possible experiments. Z. Nat. 1973;28c:693–703. doi: 10.1515/znc-1973-11-1209. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data Availability Statement

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.


Articles from Scientific Reports are provided here courtesy of Nature Publishing Group

RESOURCES