M C F Pereira

C A Brackley

J S Lintuvuori

D Marenduzzo

E Orlandini

Entropic elasticity and dynamics of the bacterial chromosome: A simulation study

We study the compression and extension dynamics of a DNA-like polymer interacting with non-DNA binding and DNA-binding proteins, by means of computer simulations. The geometry we consider is inspired by recent experiments probing the compressional elasticity of the bacterial nucleoid (DNA plus associated proteins), where DNA is confined into a cylindrical container and subjected to the action of a "piston"-a spherical bead to which an external force is applied. We quantify the effect of steric interactions (excluded volume) on the force-extension curves as the polymer is compressed. We find that non-DNA-binding proteins, even at low densities, exert an osmotic force which can be a lot larger than the entropic force exerted by the compressed DNA. The trends we observe are qualitatively robust with respect to changes in protein sizes and are similar for neutral and charged proteins (and DNA). We also quantify the dynamics of DNA expansion following removal of the "piston": while the expansion is well fitted by power laws, the apparent exponent depends on protein concentration and protein-DNA interaction in a significant way. We further highlight an interesting kinetic process which we observe during the expansion of DNA interacting with DNA-binding proteins when the interaction strength is intermediate: the proteins bind while the DNA is packaged by the compression force, but they "pop-off" one-by-one as the force is removed, leading to a slow unzipping kinetics. Finally, we quantify the importance of supercoiling, which is an important feature of bacterial DNA in vivo.

I. INTRODUCTION

The genome of living organisms, from bacteria to humans, is under remarkable confinement in physiological conditions. [START_REF] Alberts | Molecular Biology of the Cell[END_REF][START_REF] Marenduzzo | [END_REF] For instance, the circular chromosome of E. coli would be over 1 mm long if stretched out, yet it needs to fit within the bacterial cell which is a 2 × 1 × 1 µm ellipsoid. Likewise, there is about 2 m of DNA in a single human nucleus, whose typical size is only about 10 µm. Another useful way to quantify the degree of confinement of the bacterial DNA is to estimate its gyration radius, R g , at equilibrium, which is about 5 µm; since this is larger than the bacterial cell, the bacterial genome is in the "semi-dilute" regime of polymer physics. [START_REF] Doi | The Theory of Polymer Dynamics[END_REF] There are at least four mechanisms through which the bacterial chromosome is compacted within the cell. [START_REF] Alberts | Molecular Biology of the Cell[END_REF][START_REF] Joyeux | [END_REF] First, and most obviously, it is confined within the cell wall; note however that the chromosome does not occupy the entire cell so it must be compacted further. Second, there is a depletion attraction between genome segments induced by the crowding of non-DNA-binding macromolecules. 5 Third, the genome is associated with a number of architectural or nucleoid associated proteins (NAPs) 6,7 which can bind the DNA at more than one point, creating effective DNA-DNA attractive interactions which help to reduce the space occupied by the chromosome. Fourth, the bacterial chromosome is a supercoiled loop, i.e., the helical pitch is different from the one favoured thermodynamically-10.5 base pairs (bps) for B-DNA. [START_REF] Alberts | Molecular Biology of the Cell[END_REF] In practice, bacterial DNA is negatively supercoiled, 8 so the helix is slightly underwound. The degree of negatively supercoiling is about 5%, which means that in a length of DNA, which would have 20 turns at thermodynamic equilibrium, there are only 19 turns. The twist deficit can be converted into negative writhe, which creates a local folding of the DNA, again favouring compaction.

As a first approximation, one may consider only the first of these compaction mechanisms and view the bacterial chromosome as a biopolymer under tight confinement. [START_REF] Jun | [END_REF] According to this model, there is a large decrease in entropy when the DNA is within the cell, and one expects this to create an entropic pressure, or force, on the confining walls. [10][11][12][13] This naturally explains why the bacterial chromosome tends to expand when the confining cell wall is removed. The entropic force exerted by the DNA was measured in an interesting experiment by Pelletier et al. 10 In that work, single bacterial cells were trapped in an array of cylindrical cavities, with the diameter just larger than the width of the bacteria and the height much larger than its length. After the cells were trapped, their walls were lysed so that the enclosed DNA was free to expand and to increase its conformational entropy. The DNA was found to reach a height about ten times larger than that of the bacterium. The entropic force exerted by the expanding chromosome on a colloidal bead placed in the channel on top of the DNA was measured by means of optical tweezers, and it was found that this force was much larger than expected on the basis of a simple theory (reviewed in Sec. III) for Gaussian, i.e., infinitely thin, polymers, with the known persistence length of DNA, 50 nm.

In this work, we present a simulation study of a situation closely related to the experiment discussed above. 10 With respect to previous theoretical work on the bacterial chromosome based on polymer physics, [11][12][13][14][15][16][17][18][19][20][21] the main novelty here is that we include in our simulations the effects of bacterial proteins, whether binding to DNA or not, and we quantify their effect on the compression elasticity of DNA and its dynamics.

More specifically, we analyse the entropic compression elasticity and expansion dynamics of a model bacterial DNA confined in a thin cylinder and subject to the action of a "piston," a colloidal bead under the influence of an external force. This setup allows us to measure the force experienced by the piston as a function of DNA compression. We find that steric effects and the presence of proteins strongly affect both the entropic elasticity of the DNA under compression and also the dynamics of its extension once the piston is removed. In particular, non-DNA-binding proteins exert an osmotic pressure which can be at least as large as the entropic force exerted by the DNA; the macromolecular crowding they introduce is also important in determining the polymer dynamics. DNAbinding proteins can further lead to interesting "popping-off" kinetics, when the thermodynamic interactions with the DNA are tuned such that it leads to stable binding in confinement but not in solution. Finally, we find that supercoiling and DNA topology have a relatively little effect on forceextension curves but can affect the compression dynamics significantly.

Our results complement the simulations of Refs. 19 and 20 which study the compaction of a confined bacterial DNA due to non-DNA-binding proteins and quantify how the polymer collapse depends on crowder size, concentration, and polydispersity-here we additionally quantify how crowding affects the out-of-equilibrium expansion dynamics. Other relevant, yet distinct, studies are those of Refs. 4, 5, and 21 which quantified the extent to which H-NS and other DNA-binding proteins compactifies DNA.

II. MATERIALS AND METHODS

A. Model: Setup and potentials used

The system we consider consists of a bacterial DNA molecule confined in a cylindrical pore that is compressed by a piston in the absence or presence of proteins. DNA is modeled as a linear (Secs. IV B-IV E) or circular (Sec. IV F) self-avoiding polymer composed of spherical beads with diameter σ. Our coarse grained model and force fields are similar to those used to study bacterial DNA or DNA-protein systems in Refs. 11, 19, 22, and 23. The interaction potential between monomers is defined by three contributions. First, two neighbouring monomers are bound via a finitely extensible non-linear elastic (FENE) spring given by the potential

U FENE (r i,i+1 ) = - K FENE r 2 0 2 ln       1 - r i,i+1 r 0 2      , (1)
where r i,i+1 is the distance between the ith bead and its nearest neighbour [the (i + 1)th] along the chain, r 0 = 1.6σ is the maximal extent of the bond, and K FENE = 30k B T/σ 2 is the bond energy. Second, there is a steric (excluded volume) interaction between all beads that is set by the Weeks-Chandler-Andersen (WCA) potential as follows:

U WCA (r ij ) = 4k B T       d ij r ij 12 - d ij r ij 6      + k B T , (2)
for r ij < 2 1/6 d ij , and U WCA (r ij ) = 0 otherwise. Here k B T is the thermal energy (k B is the Boltzmann constant and T is the temperature), r ij is the distance between the ith and jth beads, and d ij is the mean of the diameters of the two interacting beads, i.e., d ij = σ. (Note that under this choice of FENE and WCA potentials, the DNA bond length is approximately equal to σ.) Third, the bending rigidity of the polymer is introduced by a Kratky-Porod potential for every three adjacent monomers,

U BEND (θ) = K BEND (1 + cos(θ)), (3)
where θ is the angle between the three consecutive monomers and K BEND is the bending energy. K BEND sets the flexibility of the polymer, since it determines the persistence length l p (in units of σ): l p = K BEND /k B T. We use the well characterised persistence length for naked double-stranded DNA l p = 20σ = 50 nm.

In the simulations where supercoiling is included, a circular polymer has to be considered and three more interaction potentials are taken into account. DNA supercoiling results from over-or under-twisting the helical DNA (positive or negative supercoiling, respectively) relatively to its relaxed state. In a DNA loop, the number of times the double strands wrap around each other is fixed. So, in order to alleviate the torsional strain, caused by over-or under-winding, the DNA writhes up on itself. Supercoiling can be modeled by considering twisting rigidity potentials that can be defined by assigning an orientation to each one of the DNA polymer beads. One way of doing this is to decorate the surface of each bead with three patchy particles, as in the model of Ref. 24. The positions of the patches are such that they establish three orthogonal unit vectors, which define the bead's reference frame. With this it is possible to define three angles that give the orientation of a bead with respect to its neighbour: one angle ζ that gives the orientation of the bead with respect to the polymer backbone and two dihedral angles φ 1 and φ 2 that set the degree of twisting in the plane perpendicular to the backbone. Therefore, the orientation of the bead is kept aligned with the polymer backbone by defining the potential

U BB (ζ) = K BB (1 + cos(ζ)), (4)
and the twisting rigidity of the polymer is modeled by assigning to both dihedral angles φ 1,2 the energy

U TW (φ) = K TW (1 + cos(φ + φ 0 )), ( 5
)
where φ 0 is a phase related to the twist. More precisely, φ 0 determines the number of beads involved in half a turn of the ribbon-φ 0 = π + 2π p , where p is the pitch, i.e., the length scale corresponding to one full twist in equilibrium.

Proteins are also modeled, for simplicity, as spheres of diameter equal to σ so that all beads in the simulation have the same size; in Sec. V, we also present selected results where we have considered a protein diameter of 2σ which matches more closely the size of a typical bacterial protein. [START_REF] Phillips | Physical Biology of the Cell[END_REF] In the simulations, proteins interact sterically with each other via the WCA potential in Eq. (2). In this study, we consider two kinds of proteins: non-DNA-binding and DNA-binding. The interaction between non-DNA-binding proteins and the DNA is purely repulsive and, again, is described by the potential in Eq. ( 2). For the DNA-binding proteins, we assume that they are non-specifically DNA-binding and so can bind to any DNA bead. The attractive interaction is set by a Lennard-Jones (LJ) potential

U LJshift (r ij ) = U LJ (r ij ) -U LJ (r thr ), r ij < r thr , 0, otherwise, (6)
where

U LJ (r) = 4       d ij r 12 - d ij r 6      . ( 7
)
The parameter controls the magnitude of the protein-DNA interaction, d ij = σ, and r thr is the range of the interaction.

For the simulations where both kinds of proteins are present, r thr = 3.0σ and = 2.5k B T , leading to a weak-moderate attractive protein-DNA interaction. For the simulations where only DNA-binding proteins are present, r thr = 1.5σ and we choose values for in the range [2.0, 4.0]. Note that we do not explicitly consider electrostatic interactions between proteins and/or DNA beads, again for simplicity, although these are charged in reality. This approximation is motivated by the fact that for a physiological 150 mM concentration of a monovalent salt, the Debye length is around 1 nm, [START_REF] Ando | [END_REF] which is below the size of the proteins so that electrostatic interactions are heavily screened in practice. We performed some simulations where we did consider charged particles (see Appendix B) and the results confirm that one can rely on this simplification.

The piston is modeled as a rigid sphere whose diameter is slightly larger than the confining cylindrical pore's diameter, in order to prevent particles from escaping around the sides. The interaction between the piston and the confined particles is purely repulsive and again described by the WCA potential [Eq. (2)]. The piston moves under a uniform externally applied force, compressing the DNA and the proteins against the cylinder walls (see Fig. 1). We simulate the system by using Brownian dynamics (BD) via the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) code in the BD mode. In other words, we use a molecular dynamics (MD) algorithm with a stochastic thermostat, which models the thermal fluctuations and viscosity of an implicit solvent. The system's temperature is kept constant at a value T = 1.0 ≡ 300 K. Each particle (DNA beads, proteins, and piston) obeys the Langevin FIG. 1. Sketch of our model for a DNA molecule, confined in a cylindrical channel, being compressed by a piston subjected to an external force of 20k b T/σ. The DNA extension is measured as the largest distance between two DNA beads, along the confining cylinder's axis. To build our force-extension curves, we recorded the DNA extension after equilibration, for each value of the force (an example is given in the snapshot in the bottom picture). The two top pictures correspond to transient configurations during compression. equation

m d 2 r i dt 2 = -∇ i V -γ dr i dt + 2k B T γη i (t), (8)
where r i is the position of the centre of mass of the particle with mass m = 1, γ i is the friction due to the solvent (typically γ = 2), and η i (t) is a vector representing random uncorrelated noise, such that

η iα (t) = 0, η iα (t)η jβ (t ) = δ αβ δ(t -t )δ ij , (9)
where α and β indicate the Cartesian components, δ ij and δ αβ denote Kronecker's delta, and δ(tt ) denotes Dirac's delta function. Equation ( 8) is integrated with a constant time step ∆t = 0.01τ, where τ is the simulation time unit, for a total of 3 × 10 6 time steps or more.

B. Mapping between simulation and physical units

In our simulations, we use energy units of k B T (where T = 300 K), length units of σ = 2.5 nm (the size of a DNA bead that corresponds to the hydrated thickness of B-DNA), and mass units of m DNA , where the mass of a DNA bead m DNA = 1. With this choice, there is a natural simulation time unit τ = σ √ m/k B T . Therefore, in our coarse-grained model, each DNA monomer represents ∼7.4 bp of B-DNA (for which the distance between consecutive base pairs is 0.34 nm).

In our system, there are two further time scales with an intuitive physical meaning. First, there is an inertial time scale, τ in = m/γ, where m is the mass of a DNA or protein bead and γ is its friction. This quantity gives the time after which the velocity of the beads becomes uncorrelated. The second is the Brownian time, τ B = σ 2 /D diff , which gives the (order of magnitude of the) time taken for a bead to diffuse across its own diameter, σ. The diffusion coefficient D diff is set by the friction γ through Einstein's formula D diff = k B T /γ. We use γ = 2m/τ, which leads to a Brownian time scale τ B = 2τ = 4τ in (simulation units). Since we are interested in long time behaviours, i.e., of the order of several ms, and not in resolving fine details of the inertial collisions (which play no role in our overdamped system), we map the time from simulation to physical units by considering the Brownian time scale τ B . 27 For a sphere of diameter 2.5 nm in an aqueous fluid (viscosity 1 cP), τ B ≈ 35.6 ns (for completeness, we note that τ B corresponds to 200 elementary Brownian dynamics time steps in our simulations). Note that our choice of time scales means that we do not correctly describe the inertial dynamics before τ B , but this is not an issue for our purposes, as we are interested in time scales much exceeding this. At the same time, our choice of τ B = 2τ = 4τ in ensures that the dynamics in our simulations is overdamped, which is the physically relevant case for our system. We finally note that, with our choice of units,

τ B = √ τ in τ B .
By using the mapping given above for energy, length, and time, we can map all of the other physical quantities we measure. For instance, for the force, one simulation force unit is k B T/σ, which corresponds to 1.64 pN.

As detailed above, the system studied here is composed of three types of beads: DNA monomers, non-DNA-binding proteins (crowders), and DNA-binding proteins. The number of beads is chosen so that the ratio of the number of proteins to the number of DNA beads is that found in vivo: The chromosome of E. coli consists of ∼4.6 × 10 6 bp, which in our model corresponds to a polymer with ∼5 × 10 5 beads. In bacterial cells, the estimated total number of proteins is M ∼ 10 6 , from which ∼3% are DNA-binding. This corresponds to a protein-DNA bead ratio of 2 : 1. Since the computational cost associated with a 5 × 10 5 bead polymer is extremely high, we instead consider a smaller system with N = 1000 beads. We then explore the influence of proteins in the system, by varying the protein number up to M = 2000, which represents the true protein-DNA ratio. For simulations where both crowders and DNA-binding proteins are present, the number of DNA-binding proteins is 0.03M.

III. SUMMARY OF SCALING THEORY FOR CYLINDRICALLY CONFINED POLYMERS

A. A review of the entropic spring theory

According to the well-known blob scaling concept in polymer physics, a self-avoiding polymer confined in a cylindrical pore can be seen as a linear chain of blobs. 28 In Ref. 12, this blob-scaling approach is used to derive an expression for the force required to compress a polymer confined in a cylindrical nanopore, assuming that it behaves like an entropic spring. In the regime of weak deformations, the "renormalized" free energy F for a confined polymer of N beads is given by

βF(R, D) = A R 2 (N/g)D 2 + B D(N/g) 2 R , ( 10
)
where R is the extension of the polymer along the axis of the cylindrical pore (see Fig. 1), D is the confining cylinder's width, β = 1/k B T , A and B are constants, and g is the number of polymer beads inside a compression blob of diameter D, i.e., at equilibrium. Therefore N/g is the number of compression blobs of diameter D and DN/g = R 0 , the extension of the polymer at equilibrium (zero force). From this, a universal scaling relation for the (external) force-extension relation can be derived,

D βf = D ∂ ∂R ( βF) = 2A R R 0 -B R R 0 -2 . ( 11
)
The imposition of the equilibrium condition f (R = R 0 ) = 0 reveals the relation 2A = B. By redefining B ≡ k, Eq. ( 11) reduces to 10

f = k (k B T ) D       R R 0 - R R 0 -2      , ( 12
)
where k is the dimensionless polymer spring constant.

In the regime of strong compression (R/R 0 1), the above form of the free energy no longer holds. The correct form of free energy 12 leads to a new force-extension relation,

f ∼ - k B T D R R 0 -9/4 . (13)

B. A scaling theory for the polymer expansion dynamics

We now review a simple scaling theory which we use to guide the interpretation of our simulation results in what follows. The simplified theory we use follows the approach used in Ref. 18 and works in the adiabatic limit where the polymer goes through quasi-equilibrium states, i.e., for slow dynamics. This is sufficient for our purposes, as we focus on the dynamics of the polymer whilst it relaxes after the initial abrupt expansion. More sophisticated theories of polymer compression and expansion within nanochannels, which account for non-equilibrium effects, were recently presented in Refs. 29 and 30.

Taking F to be the confinement-deformation free energy, the polymer deformation force f is given by -∂F/∂R. The equation of motion for the polymer extension R is 18

f + 1 2 γ dR dt = 0, ( 14
)
where γ is the polymer friction coefficient. This equation can be solved given the initial condition R(t = 0)-the extension of the compressed polymer just before it starts expanding. An explicit form for f depends on the physical problem at hand. Following the approach in Ref. 12, we use the force-extension relations in Eqs. ( 12) and ( 13). Taking Eq. ( 12) for the weak compression regime and substituting it into Eq. ( 14), one gets

dR dt = - 2 γ k (k B T ) D      R R 0 - R R 0 -2     . ( 15
) By defining R = R/R 0 and A = (2k(k B T )) / (R 0 γD), this becomes d R dt = -A R -R-2 ⇔ R2 R3 -1 d R dt = -A. (16)
Taking into account that during the polymer expansion R < 1, the integral of this equation leads to the solution

ln(1 -R3 ) = -3At + c ⇔ R(t) = R(t) R 0 = 1 -ce -3At 1/3 , ( 17
)
where c = e c and c are constants that are defined by the initial offset R(t = 0

): c = 1 -R3 (t = 0). According to the solution R(t) = R 0 1 -ce -3At 1/3
, there is an initial offset R(t 0) corresponding to the extension of the polymer after being compressed by the piston. For intermediate times (corresponding in our simulations to 0.01 ms t 0.1 ms or 10 3 τ B t 10 4 τ B , see below), one can expand the solution about zero to get R(t) ≈ R 0 (1c + 3Act) 1/3 , which reveals the scaling R(t) ∼ t 1/3 . For late times, the polymer relaxes completely reaching a saturation regime where the extension corresponds to the value at equilibrium in the absence of a compression force: R(t → ∞) = R 0 .

In the strong compression regime [Eq. ( 13)], the extension-time scaling relation for intermediate times becomes

R ∼ t 4/13 .
These scaling relations are derived for a polymer confined in a cylindrical pore in the absence of other particles. However, the bacterial cellular environment is rich in proteins and so one can ask how the scaling would change in the presence of proteins either non-DNA-binding (crowders) or DNA-binding. Crowders have been shown to increase the viscosity of the medium, 31 which slows the polymer diffusion and hence the polymer expansion dynamics. Therefore one might expect a scaling relation for intermediate times R ∼ t α where the exponent α is smaller than that derived above in the absence of crowders. DNA-binding proteins with more than one binding site (such as the ones considered in the present study) bridge together DNA segments, reducing the polymer gyration radius and the extension of the confined polymer. This bridging effect increases the local polymer stiffness, reducing the effective polymer elasticity. Therefore, one may also expect a slow-down effect of the polymer expansion in the presence of such proteins and a further decrease of the exponent α.

IV. RESULTS

A. Setup of the simulation

First, we discuss our simulation geometry and setup (see Fig. 1). We considered two cases: DNA-only simulations (Fig. 1) and a DNA molecule interacting with proteins (both DNA-binding and non-DNA-binding, Figs. 2(a) and 2(b), respectively). Note that the two top pictures in Fig. 1 correspond to transient configurations during compression of the DNA molecule, depicting the time evolution of the simulation. The bottom picture corresponds to the fully equilibrated system.

To simulate DNA, we considered a model linear DNA molecule of N = 1000 beads, within cylinders of two different diameters (D = 40σ and D = 20σ, the latter case is shown in Fig. 1). Results for the two diameters are very similar, hence we show below only those for D = 40σ. For each configuration, we measure the DNA extension along the longitudinal direction of the cylinder, defined as the maximum distance between any two beads in the chain-we denote this quantity as R. We then take the average of R over 10 configurations. The radius of gyration of the unconfined DNA (in the absence of the cylinder) was estimated to be R g ∼ 62.2σ, while its extension within the cylinder in the absence of the piston was measured as R 0 ∼ 152.2σ. Therefore, we work in the semi-dilute regime which is realistic for the bacterial chromosome. Although in E. coli the DNA is circular, the scaling theory and previous work suggest that the main contribution is given by polymer confinement (entropy and free energy loss). Hence we expect the polymer topology to be not too important-the simulations with circular DNA described in Sec. IV F further support this view.

For the DNA-protein simulations, we considered two situations. In the first [Fig. 2(a)], we only included non-DNAbinding proteins [green spheres in Fig. 2(a)], which are the majority in vivo: for instance, in E. coli there are about 10 6 proteins, of which only 3% are estimated to be DNA-binding. As the bacterial cell volume of E. coli is about 6 µm 3 , and the average diameter of a bacterial protein is 5 nm, [START_REF] Phillips | Physical Biology of the Cell[END_REF] the volume fraction occupied by all bacterial proteins is a few %, which is larger than the volume occupied by DNA. In our simulations, we typically considered M = 2000 proteins, modeled as spheres (matching the ratio found in vivo between the number of proteins and the number of "DNA beads"). In the second situation, we included a fraction of DNA-binding proteins [magenta spheres in Fig. 2(b)]; we simulated nonspecific binding, as is the case, to a first approximation, for the histone-like bacterial H-NS protein, 32 or for the DNAbinding protein from starved cells (DPS). 33 While we include no direct protein-protein attractive interaction, DNA-binding proteins naturally cluster, through the so-called "bridginginduced attraction" described previously in Refs. 22 and 34. This attraction is associated with a simple positive feedback loop: proteins bind to the DNA in multiple places forming bridges, this increases the local concentration of DNA, which in turn recruits further proteins, etc., ultimately resulting, for the protein size considered here, in the formation of elongated protein clusters associated with a local tubular folding of DNA where the protein-associated segments are parallel to each other. Reference 22 shows that larger proteins lead to DNA wrapping around them. However this is not realistic for H-NS which is thought to form linear clusters, 22,32 as in Fig. 2.

Figures 1 and2 show examples of compression simulations, where different forces were applied to the piston, and the resulting DNA conformations were analysed. We have also performed further simulations, as follows. First, we considered compression simulations with modified protein density. Second, we simulated the dynamics of entropic expansion, by first compressing the DNA with a large force, allowing the system to reach equilibrium, and subsequently removing the piston. Third, we have varied the strength of the interaction between DNA-binding proteins and DNA. Finally, we have also considered the compression of supercoiled DNA loops (as opposed to the linear DNA shown in Figs. 1 and2).

B. Excluded volume effects lead to quantitative deviations from the predicted entropic spring response in DNA-only simulations

We begin by studying the entropic compression in DNAonly simulations [see Fig. 3(a)]. Plotted are the results from simulations (blue circles) and the entropic spring theory (full line). The latter is obtained by fitting Eq. ( 12) to the numerical results, through the spring constant k, in the weak compression regime. We find two regimes. First, for R/R 0 > 0.1, the entropic spring theory revised in Sec. III A (which is also the theory used in Ref. 10) works well, and we observe a very sensitive dependence of the extension on compression force: the extension reduces to 10% of its free value, R 0 , for a force of just ∼3 simulation units (corresponding to about 5 pN-see mapping in Sec. II B). Second, for R/R 0 < 0.1, we find a sharp deviation from the theory: our numerical estimate of the entropic force is more than an order of magnitude larger than that predicted. The origin of this deviation is in the assumption in the scaling theory of an infinitesimally thin polymer: clearly, this is not applicable to the tightly confined regime which we reach by the end of our simulation, where the DNA segments are forced into close contact so that steric interactions dominate. Figure 3(b) further suggests that close packing and many body interactions are the main contributions to this strong steric repulsion in the tightly confined regime, as it can be seen from the fact that the volume frac-tion reaches a large value (∼0.3) for the largest compression force.

This result suggests that steric repulsion is an important factor to consider when estimating the entropic force resisting compression. At the same time, we note that the effect found here (a 10-fold increase) is an overestimate of the correction needed for a real bacterial chromosome. This is due to the scaled down dimensions of the DNA we use: the same value of R/R 0 corresponds to a much denser volume fraction of DNA in our simulations with respect to the experiments in Ref. 10.

C. Non-DNA-binding proteins greatly increase the entropic force and pressure exerted on the piston

Next, we analyse the effect of bacterial proteins on the entropic elasticity of the system. The osmotic pressure of proteins in the cytosol can be estimated as Mk B T /V, where M is the number of proteins and V is the confinement volume; for the case of E. coli, this is ∼0.01 atm, which is larger than, or at least of the same order of, the pressures recorded in the experiment in Ref. 10.

We show in Fig. 4(a) how the presence of (non-DNAbinding) proteins (modeled as spheres which interact both with each other and with the DNA solely via excluded volume) affects the force-extension curves. We find that proteins make an important contribution, especially for moderate compression (relatively large values of R): in this regime, the force required for a given extension is orders of magnitude larger than in the case where the proteins are not included. More specifically, the force is approximately linear in the number of proteins M, as expected for an osmotic contribution [see Fig. 4 Figure 4(b) compares our numerical results for M = 2000 with the force resulting from an ideal gas of 2000 proteins (and no polymer, dashed line). There is overall a reasonable agreement, which quantitatively confirms that the compression curves are dominated by the osmotic pressure of proteins. The agreement improves when correcting for the finite volume of The protein osmotic contribution leads to a large increase in the compression force, sometimes of several orders of magnitude. For a given value of R, the protein contribution is linear in the number of proteins (inset 1)-fit: f = 0.0160M + 0.2933. For a given compression force, the DNA extension also increases linearly with the increasing number of proteins (inset 2)-fit: f = 0.0195M + 10.4113. (b) Comparison between numerical results (orange inverted triangles) and predictions from the theory for an ideal gas of 2000 non-DNA-binding proteins (dashed line), and from a theory accounting for the volume of the protein through the van der Waals equation of state (solid line). Pressures from the theory are converted into forces along the cylinder axis via multiplication by the cross section of the cylinder.

proteins by using a pressure given by the van der Waals equation of state (solid line). The remaining discrepancy in the high compression regime may be due to the presence of the polymer or excluded volume effects not captured correctly by the van der Waals formula.

We next address the role of DNA-binding proteins. These tend to compact the DNA and should lead to a change in the extensional elasticity. Mobile cross-links, or slip-links, have previously been shown to strongly modify the force-extension curves of polymers in stretching experiments; 35 for this reason, DNA-binding proteins were proposed in Ref. 10 as a possible explanation for the quantitative significant discrepancy between the experiments and the entropic spring theory in compression experiments. Therefore we ask to what extent the presence of DNA-associating proteins affect our results. Figure 5 shows that these effects are minor in the simulations. This is consistent with our previous finding that osmotic forces from non-DNA-binding proteins are more important quantitatively than DNA entropic forces: DNA-binding proteins only affect the polymer response and hence do not have much bearing on the overall curve. We should stress here that the DNA-binding proteins we consider form clusters, as could be the case for H-NS, 22,32 or DPS; 33 while the dynamic cross-links invoked in Ref. 10 will in practice interact entropically (see Ref. 36), their collective behaviour may be different.

In summary, we find that non-DNA-binding proteins exert a significant osmotic pressure on the piston, and this is much larger than the force exerted by the DNA. Furthermore, any reduction in the force exerted by the DNA due to DNAbinding proteins is dwarfed by the contribution of the non-DNA-binding proteins. Our estimates suggest that even in the experimental situation the presence of non-DNA-binding proteins could significantly affect the force measured via the setup used in Ref. 10; therefore, it would be of interest to compare in more detail those experiments with in vitro compression experiments with different size DNAs and different protein environments.

D. The expansion dynamics of DNA depends on macromolecular crowding and DNA-protein interactions

We next examine the dynamical behaviour of a DNA molecule (with or without proteins), where the DNA is first compressed under a strong force and then let free to expand after the piston is removed. Figure 6 shows how the extension R increases in time following the piston removal. We note that the curves for a single realisation are very noisy, underscoring the highly stochastic nature of the expansion dynamics. Therefore, in Fig. 6, each curve (or point) corresponds to an average over 10 independent runs.

First, for the DNA-only case, one may expect a scaling behaviour for R with t 4/13 (for self-avoiding chains, see Sec. III B and Ref. 18). Our results typically show a similar, but on average slightly smaller, exponent: the values are Second, we consider the case with proteins. The expansion dynamics is much slower [i.e., the apparent exponent is smaller, Fig. 6(b)] in the presence of non-DNA-binding proteins: this is because the proteins create a crowded environment which hampers DNA unfolding. At the same time, the DNA extension is larger at t = 0, for the same initial force, because of the osmotic pressure of the proteins which opposes the compression force from the piston; as a result, the value of R(t) is always larger when proteins are present in our simulations. For later times, the proteins diffuse away from the DNA and become dilute, so the effect of crowding diminishes. Hence for large t the apparent exponents for our curves with and without proteins become approximately equal.

DNA-binding proteins lead to even slower progress in the DNA expansion [Fig. 6(c)], especially at early times. Therefore, DNA-binding proteins have a much more detectable signature in the expansion dynamics, with respect to the minor contribution noted in the force-extension curves in Fig. 5 where DNA-binding proteins were absent. To characterise this effect more in detail, we performed simulations with only DNA-binding proteins [Fig. 6(d)]: the exponents found in this case should then be compared with those for DNA-only simulations [Fig. 6(a)]. These results confirm that when proteins bind to the DNA, they may reduce the apparent exponent; however, the effect is subtle, and there is a large stochastic element in the dynamical curves [as each of the values in Fig. 6(d) is computed by averaging 10 different expansion runs].

E. "Popping-off" dynamics with DNA-binding proteins

In Fig. 6 (bottom two panels), we analysed the effect of DNA-binding proteins on the dynamics of DNA expansion. 37 By examining the trajectories of the system as a function of DNA-protein interaction strength, we observed some further interesting phenomena in the kinetics of the system, which we now describe.

If the DNA-protein interaction is weak ( = 2k B T ), proteins only transiently bind to DNA. Since increased concentration favours the bound state (the entropy loss upon binding is smaller), more proteins bind upon compression; however, they detach immediately after the piston is released (see Fig. 7, left column, and Fig. 7, blue line in bottom panel). As a result, the DNA responds elastically as a protein-free polymer-this is consistent with our finding that the apparent exponent measured for a low DNA-protein interaction strength , in the simulations with only DNA-binding proteins, is comparable with the exponent measured for the DNA-only simulations (see Fig. 6).

Some interesting dynamics occur if we choose a larger DNA-protein interaction strength, so as to promote more FIG. 7. Protein popping-off during DNA free expansion, for simulations with DNA and 100 DNA-binding proteins only. The bottom plot shows the overall pair energy of the system as a function of time. This quantity is negative and approximately proportional to the number of proteins bound to the DNA at a given time. For low DNA-protein interaction strength ( = 2.0), the proteins do not stick to the DNA, as confirmed in the energy-time plot: the pair energy remains zero over time. For large DNA-protein interaction strength ( = 5.0), the proteins remain permanently bound to the DNA during expansion, and the pair energy remains approximately constant over time. For an intermediate interaction strength ( = 3.5), after removing the piston some proteins remain bound to the DNA, but eventually pop off. The pair energy first decreases linearly in time and then tends to zero asymptotically. long-lived binding (see Fig. 7, middle column). In this situation, the interaction strength is such that it favours longlived binding under compression-in this case, essentially all proteins are bound at all times, and they locally compact the DNA into a toroidal structure, resembling that of DNA within bacteriophages. [START_REF]bacterial DNA, proteins like H-NS would not bind the whole genome, so the 3D structure would likely be different upon compression[END_REF] When the piston is removed, the translational entropy of the proteins in the unbound state increases dramatically-as they could now occupy any region of the cylindrical domain; however, the bound state is still metastable, and it takes a relatively long time for the proteins to detach. Over time, proteins "pop-off," typically one-byone, from the collapsed DNA, and the total energy of the polymer-and-protein system (which is approximately proportional to the number of bound proteins) decreases in magnitude linearly with time. Therefore, the "popping-off" time should increase linearly with the number of DNA-binding proteins in the system and so could easily be observable in experiments with bacterial (recall that there are an estimated 3 × 10 4 DNA-binding proteins in the bacterial nucleoid). Intriguingly, while the protein kinetics are completely different, the popping-off leaves little detectable signature in the apparent exponents recorded in Fig. 6. Finally, the popping-off requires tuning of the interaction because if this becomes too large, proteins bind to the DNA permanently (at least within our simulation time), and the popping-off kinetics can no longer be observed-the energy now does not appreciably depend on time (see Fig. 7, bottom panel, green curve).

F. Compression curves and dynamics for supercoiled DNA

Thus far, as previously highlighted, we have described the results obtained for linear DNA. Within bacteria, DNA is circular and negatively supercoiled (see Sec. I); it is therefore of interest to ask what the effect of supercoiling is on our results.

In order to address this issue, we consider the model introduced in Ref. 24 and described in Sec. II A. We consider here the same bending persistence length used previously in this work-∼50 nm, appropriate for B-DNA. For the twisting rigidity, we consider ∼75 nm, which is in line with recent experimental and simulation values 39 (see Refs. 40 and 41). In DNA, there is a complete twist every 10.4 base pairs (corresponding to approximately 3.5 nm). To set these parameters, we chose a bead size σ = 3.4 nm, K BEND = 15.2 , K TW = 10 , and K BB = 90 . We considered two situations. In the first case, the dihedral phase φ 0 is equal to π giving a ribbon with no additional twist (p = ∞). In the second case, φ 0 = π + 2π 10 giving a ribbon that is undertwisted by a full turn every 20 beads, i.e., every 208 base pairs. This second case corresponds to supercoiled DNA, with a supercoiling density of 0.05 (note that in our model there is symmetry between negative and positive supercoilings because DNA denaturation is disallowed). In both cases, and as previously for the linear DNA, we considered a chain with N = 1000 backbone beads (each with its associated patches).

The chain was first relaxed within a cylinder of radius R = 20σ and length 400σ, verifying that the linking number of the circular ribbon was constant and approximately equal to 0 and 50 (in absolute value) for the case of torsionally relaxed and supercoiled DNA, respectively (data not shown).

In Fig. 8(b), we report the time evolution of the longitudinal extension of the DNA, R, relative to its value R 0 in the relaxed state-the compression was performed in the presence of M = 1000 non-binding proteins and by applying a constant force f = 60 (in simulation units). For this value of f, the DNA-protein system is already strongly compressed (R/R 0 ∼ 0.1) independently on the degree of twisting. While the final value of R is very similar for the torsionally relaxed and the supercoiled DNA, the dynamics is, quite intriguingly, rather different, as the supercoiled DNA shows a softer response to compression. For mild compression (f = 10) [see Fig. 8(a)], the situation is similar although, as expected, the final state is less compressed than the f = 60 case (R/R 0 > 0.3). These conclusions also hold in the absence of non-binding proteins [blue curves in Fig. 8(a)]; however, in that case, the final value of R is significantly lower (R/R 0 ∼ 0.07): as found previously with linear DNA, non-binding proteins contribute significantly to the force opposing compression by the piston.

By considering different values of the force and looking at the equilibrium compressed state, we further computed the compression force as a function of the ratio R/R 0 , for the torsionally relaxed and supercoiled DNA with M = 1000 nonbinding proteins. The results are reported Fig. 9: both curves are quite similar, and, remarkably, relatively close to the linear case as well.

Supercoiling or, in other words, the local writhing of the DNA polymer has an effect similar to that of DNAbinding proteins (described in Secs. IV C and IV D)-it leads to a change in the extensional elasticity and hence a softer response to compression. Like in the case of DNA-binding proteins, the effect of supercoiling in the force-compression curves is very small, being more detectable in the dynamics curves.

FIG. 9. Compression force as a function of the extension ratio R/R 0 for torsionally relaxed (green circles) and supercoiled (dark-green squares) DNA. The curves with hollow and full points refer to the case of linear and circular DNA, respectively. The compression has been performed in presence of M = 1000 non-binding proteins.

V. DISCUSSION AND CONCLUSIONS

In summary, in this work we have presented Brownian dynamics simulations of the compression and expansion dynamics of a DNA molecule, modeled as a self-avoiding linear or looped polymer, interacting with an ensemble of non-DNA-binding and DNA-binding proteins. Previous work (see, among other, Refs. 4, 5, 19, 20, and 22) had clearly demonstrated the importance of proteins on the thermodynamic conformations of bacterial DNA: non-DNA-binding proteins create macromolecular crowding which can promote global collapse of the bacterial chromosomes, while DNAbinding proteins such as H-NS provide local compaction. The novelty of our current work is that, by addressing a setup which is directly relevant to single molecule experiments probing the entropic elasticity of bacterial DNA, we establish that the inclusion of proteins further leads to important effects on the force-extension curves recorded upon compression and also on the expansion dynamics of the polymer.

First, we presented some DNA-only simulations, where there are no proteins as a reference case. We have shown that for weak to intermediate compression, our results confirm the entropic spring theory of Ref. 10, which predicts an abrupt decrease in elongation with compression. For large compression (R/R 0 < 0.1 in our simulations), however, excluded volume interaction create a strong deviation from the theory.

Second, we have found that the osmotic pressure of non-DNA-binding proteins can dwarf the entropic pressure of the spring-like polymer during compression. In our simulations, we have considered N = 1000 polymer "beads" and up to M = 2000 non-DNA-binding protein "beads"; although these numbers are comparable, the effect of proteins can be orders of magnitude larger in the compression curves. With respect to these thermodynamic curves, DNA-binding proteins affect the DNA elasticity, hence only have a minor effect overall.

It is interesting to ask whether osmotic pressure from unbound proteins can account for the high force (∼100 pN) observed experimentally in the compression curves in Ref. 10. As mentioned by the authors, while the curve overall can be fitted with the entropic spring theory, the numerical value which is expected of a self-avoiding polymer would be significantly smaller than observed: we note that even a small fraction of proteins could lead to an osmotic force which might account for this.

Third, we have explored the impact of proteins (again, non-DNA-binding and DNA-binding), on the expansion dynamics starting from a compressed DNA-and-protein system and following the removal of the force exerted by the piston. We found that the DNA-only simulations lead to a scaling behaviour for the extension R as a function of time which is consistent with previous work in the literature. 18 The crowding introduced by non-DNA-binding proteins leads to a much slower dynamics and to a much decreased apparent exponent. An interesting observation is that, when simulating DNA with DNA-binding-proteins only, tuning of the DNA-protein interaction leads to a popping-off kinetics during DNA expansion, where proteins are metastably bound to the DNA and detach one-by-one after the volume at their disposal increases.

Fourth, we have also performed more realistic simulations for bacterial DNA, where we considered circular polymers, with or without supercoiling. Quite remarkably, the torsionally relaxed and the supercoiled model DNA lead to a very similar force compression curve, which is also not far from the one we found for linear DNA. On the other hand, the dynamic response is significantly different and markedly dependent on supercoiling.

While the results presented in this work focus on the case of proteins which are not charged and are the same size as the DNA beads (a choice made for simplicity), we have also performed simulations taking into account the fact that the size of a typical bacterial protein is larger, and about twice the thickness of DNA [START_REF] Phillips | Physical Biology of the Cell[END_REF] 13-15: these correspond to Figs. 345. In all cases, the trends are qualitatively identical and confirm our conclusions above. There are quantitative differences for the size simulations when the volume fraction is high: this is expected as, under those situations, it is the volume fraction, rather than number density, which determines the compression pressure and force. It is interesting that the effective exponent for DNA expansion slightly decreases for larger proteins, due to the increased crowding.

Overall, our results provide a generic framework within which to analyse experiments such as those in Ref. 10; they also provide further testable predictions for future experiments, e.g., probing the dynamics of bacterial DNA or of supercoiled plasmids in vitro.

APPENDIX A: EFFECT OF PROTEIN SIZE

Here we show results corresponding to Figs. 4-6 in the main text but with a protein size of 2σ.

Figure 10 shows the compression force versus extension curve: comparison between these results and those of Fig. 4 shows a very similar trend. The quantitative values are very close together for large enough R and start to deviate significantly below R ∼ 60-70, where the volume fraction is non-negligible, and excluded volume effects are larger for the larger proteins.

The comparison between Figs. 11 and 5 leads to the same conclusion-the slightly larger gap between cases with and FIG. 10. Same as Fig. 4, but for simulations with proteins of diameter 2σ. Compression force as a function of the DNA extension for a varying number M of non-DNA-binding proteins: DNA-only simulations (blue circles), M = 500 (magenta squares), M = 1000 (green triangles), M = 1500 (yellow diamonds), and M = 2000 (orange inverted triangles). As for the simulations presented in Fig. 4, the protein osmotic contribution leads to a large increase in the compression force, sometimes of several orders of magnitude. For a given value of R, the protein contribution is linear in the number of proteins (inset 1)-fit: f = 0.0271M 5.8389. For a given compression force, the DNA extension also increases linearly with the increasing number of proteins (inset 2)-fit: f = 0.0290M + 9.8640. without DNA-binding proteins is due to the fact that larger DNA-binding proteins lead to multiple binding to the DNA; hence the DNA becomes more compact. Thus the observed effect might not just be due to protein size, as bacterial DNAbinding proteins such as H-NS only have two DNA-binding sites (they are better represented in our model by the smaller proteins considered in the main text).

The effect of the larger protein size on the expansion dynamics is analysed in Fig. 12, which shows a slower FIG. 11. Same as Fig. 5, but for simulations with proteins diameter 2σ. Compression force as a function of the DNA extension for simulations in the absence of proteins (blue circles), in the presence of non-DNA-binding proteins (green squares), and in the presence of both binding and non-binding proteins (orange triangles: 3% of the proteins are DNA-binding). Like in the simulations presented in Fig. 5, the DNA-binding proteins lead to the formation of DNA clusters, hence compacting the DNA, giving a decrease in the compression force. However, since here the proteins are larger, there is multiple binding to DNA, which leads to a more compact DNA, hence the slightly larger gap between the curves for the cases with and without DNA-binding proteins. FIG. 12. Same as Fig. 6, but for simulations with proteins of diameter 2σ. Measured dynamical exponents during DNA (in all cases, curves correspond to averages over 10 runs). (a) Comparison of the exponents measured for simulations without proteins (blue triangles) and with 1000 non-DNA-binding proteins (green circles). Like in the simulations presented in Fig. 6, the protein crowding leads to a lower exponent; however, here the exponent decrease is larger. This is due to the increased effective friction generated by larger proteins. (b) Comparison of the exponents measured for simulations in the presence of 1000 non-DNA-binding proteins (green circles) and in the presence of 970 non-DNA-binding and 30 DNA-binding proteins (magenta triangles). The exponent decreases in the presence of DNA-binding proteins, like in the case of Fig. 6; however, here large proteins lead to a more marked decrease of the exponent. This is due to the fact that larger proteins allow multiple binding to DNA, which further decreases the DNA extensional elasticity. All simulations started from an initial DNA configuration obtained for a compression force of 20. The range of times, for which the expansion curves were fit, were chosen to take into account the intermediate expansion regime (10 3 τ B -10 4 τ B or 0.01 ms -0.1 ms).

expansion dynamics. This is because the larger spheres create larger effective friction (as the friction or viscosity of a hard sphere suspension is proportional to its volume fraction). The effective exponent we find is accordingly smaller, although this should be seen as a measure of the speed of the dynamics rather than a true dynamical exponent, which would require the study of different chain lengths. The decrease in the effective exponent is more marked in the case with DNA-binding proteins: again, this is due to the fact that these proteins can form multiple contacts with DNA.

APPENDIX B: EFFECT OF CHARGE

Here we present the results corresponding to Figs. 345in the main text but for charged DNA beads and proteins.

The electrostatic interaction between particles is modeled by considering the Debye-Hückel potential, 42,43 in addition to the potentials already described in Sec. II A, U DH (r i,i+1 ) = C q i q j r e ka 1 + ka e -kr i,i+1 r i,i+1 , (B1)

where C = 1/4π 0 k B T , q i is the charge of particle i, r is the dimensionless dielectric constant (we consider r = r, water = 80), k is the inverse Debye length, and a is the radius of the particle.

We consider k 1 = 1 nm, which is the Debye length in the cell interior. More explicitly, k = 8πl B N A 10 3 c S , where l B = 0.71 is the Bjerrum length in water and c S ∼ 150 mM is the salt concentration inside cells. [START_REF] Ando | [END_REF] Regarding the charge of the particles, DNA is known to carry two negative charges (2e) per base-pair, due to the negatively charged phosphate group. As detailed in Sec. II B, 1 DNA bead corresponds to 7.4 bp in our model. Therefore, there are 7.4 phosphate groups per DNA bead. However, counterion (or salt) condensation leads to a neutralisation of 80%-100% of the phosphate groups. 44 So, in fact, each DNA bead will only carry a charge of 0.2 × -14.8e = -2.96e. The choice of the value of the proteins' charge is not as straight forward since it depends on the protein residues. We opted to consider the value of the charge of an average protein and use that value for negatively and positively charged proteins in our model. In Ref. 45, the average of protein charges in bacteria was measured to lie in the range [10e, +15e]. The inset shows the force axis in logarithmic scale. For f > 10, both models lead to similar force-extension relations (for very large forces, the case with electrostatic interactions is slightly smaller. However this only holds for a single realisation). For f < 10, the extension of the polymer in the presence of electrostatic interactions for a given force is larger than for the case without electrostatics, which was expected since there is an additional long-range repulsion between DNA beads.

For simplification, we consider the proteins' charge to be equal in magnitude to the DNA beads' charge-14.8e-which corresponds to an effective charge q prot = 2.96e due to neutralisation emerging from counterion condensation. Therefore, non-DNA-binding proteins are modeled as beads with charge q = 2.96e and DNA-binding proteins with charge q = +2.96e.

Figure 13 shows the compression force versus extension in the absence of proteins. Figure 13(a) shows that, like in Fig. 3, the entropic spring theory of Ref. 10 agrees with the model for R/R 0 > 0.1 but breaks down for R/R 0 < 0.1. Figure 13(b) shows that, for f > 10, the DNA beads' charge does not play a significant role in the elastic response of the DNA polymer but that for weak compression forces (f < 10) the effect of long-range electrostatic interactions is more noticeable: for a given force, the DNA extension for the charged polymer is larger than for the neutral polymer.

Figure 14 for charged particles and Fig. 4 for neutral particles are remarkably similar. The comparison between inset 2 in both figures shows, however, that the presence of electrostatic interactions leads to a slightly higher DNA extension for a moderate force, as seen before.

Again, Figs. 15 and 5 lead to the same conclusions. In both cases, the DNA-binding proteins lead to the formation of DNA clusters, hence compacting the DNA, giving a decrease in the compression force. The striking similarity between results arising from the charged and neutral models further suggests that the electrostatic interactions do not play a significant role in the overall elastic response of DNA. 4 shows that the presence of electrostatic interactions leads to a slightly higher DNA extension for a moderate force, as seen in Fig. 13. FIG. 15. Same as 5, but for simulations with charged proteins and polymer beads. Compression force as a function of the DNA extension for simulations in the absence of proteins (blue circles), in the presence of non-DNA-binding proteins (green squares), and in the presence of both binding and non-binding proteins (orange triangles: 3% of the proteins are DNA-binding). Like in the simulations presented in Fig. 5, the DNA-binding proteins lead to the formation of DNA clusters, hence compacting the DNA, giving a decrease in the compression force. Again we point out the striking similarity between these curves and the ones in Fig. 5. This suggests that, indeed, the electrostatic interactions do not play a significant role in the overall elastic response of DNA.

FIG. 2 .

 2 FIG. 2. Sketch of our model for a DNA molecule in the presence of (a) non-DNA-binding proteins (green spheres) and (b) DNA-binding proteins (magenta spheres). The DNA-binding protein cluster, inducing a local tubular folding of DNA (see the inset).

  (a), inset 1]. Interestingly, for intermediate forces, we find that the extension recorded at a given force also depends linearly on M [Fig. 4(a), inset 2].

FIG. 3 .

 3 FIG. 3. (a) Comparison of the results for the compression force as a function of the normalized DNA extension in the absence of proteins, from the entropic spring theory of Ref. 10 (full line) and our numerical model (blue circles). Parameters were as specified in Sec. II, apart from D = 40σ. For R/R 0 > 0.1, the theory agrees with the model in that there is a strong dependence of the DNA extension on the compression force. For R/R 0 < 0.1, the theory deviates from our numerical estimate of the entropic force, suggesting that, in the high compression regime, the assumption done in the theory of an infinitesimally thin polymer, where excluded volume effects can be disregarded, breaks down. The inset shows the results for the "Force" axis in logarithmic scale. Error bars are shown for three different points. (b) Compression force as a function of the DNA volume fraction. As the DNA volume fraction increases, an increasing force is needed to further compress the DNA. The fact that the volume fraction reaches almost 0.3 for the tightest compression suggests that close packing and many body interactions are the most relevant contributions to the divergence of our numerical estimates from the theory in the high compression regime observed in plot (a).

FIG. 4 .

 4 FIG. 4. (a) Quantification of the effect of non-DNA binding proteins on the numerical results for the compression force as a function of the DNA extension. Blue circles correspond to DNA only (the solid line corresponds to the entropic spring theory for DNA only), while other symbols corresponds to system with a variable number M of non-DNA binding proteins; we examined systems with M = 500 (magenta squares), M = 1000 (green triangles), M = 1500 (yellow diamonds), and M = 2000 (orange inverted triangles).The protein osmotic contribution leads to a large increase in the compression force, sometimes of several orders of magnitude. For a given value of R, the protein contribution is linear in the number of proteins (inset 1)-fit: f = 0.0160M + 0.2933. For a given compression force, the DNA extension also increases linearly with the increasing number of proteins (inset 2)-fit: f = 0.0195M + 10.4113. (b) Comparison between numerical results (orange inverted triangles) and predictions from the theory for an ideal gas of 2000 non-DNA-binding proteins (dashed line), and from a theory accounting for the volume of the protein through the van der Waals equation of state (solid line). Pressures from the theory are converted into forces along the cylinder axis via multiplication by the cross section of the cylinder.

FIG. 5 .

 5 FIG. 5. Compression force as function of the DNA extension for DNA-only(blue circles), simulations in the presence of non-DNA-binding proteins (green squares), and simulations in the presence of both types of proteins (orange triangles): 3% of the proteins are DNA-binding. The DNAbinding proteins lead to the formation of DNA clusters, hence compacting the DNA, which yields a decrease in the compression force. The effect in the osmotic pressure due to DNA-binding proteins is, however, small. 7

FIG. 6 .

 6 FIG. 6. Measured dynamical exponents during DNA expansion (in all cases, curves correspond to averages over 10 runs). (a) Comparison of the exponents measured for DNA-only simulations for different initial compression forces: f = 20 /σ (blue triangles) and f = 40 /σ (orange circles). The bigger the compression force, and hence the more compressed the initial DNA the bigger the apparent value of the exponent. (b) Comparison of the exponents measured for DNA-only simulations (blue triangles) and simulations in the presence of 1000 non-DNA-binding proteins (green circles). The protein crowding leads to a decrease in the exponent. (c) Comparison of the exponents measured for simulations in the presence of 1000 non-DNA-binding proteins (green circles) and in the presence of 970 non-DNA-binding and 30 DNA-binding proteins (magenta triangles). The exponent decreases in the presence of DNA-binding proteins, suggesting that the formation of protein-induced DNA clusters slows down the DNA expansion. (d) Measured exponents for simulations with DNA and 100 DNA-binding proteins, but no non-DNA-binding proteins, as a function of the DNA-protein binding strength. The exponents show a weak tendency to decrease with increasing interaction strength, supporting the observation that DNA clustering slows down DNA expansion. All simulations started from an initial DNA configuration obtained for a compression force of 20, except for the case in the top-left plot. The ranges of times, for which the expansion curves were fit, were chosen to take into account the intermediate expansion regime (10 3 τ B -10 4 τ B or 0.01 ms-0.1 ms), which corresponds to a power law behaviour for all forces we considered.

FIG. 8 .

 8 FIG. 8. Compression dynamics under a force (a) f = 10 and (b) f = 60. The ratio R/R 0 is reported as a function of time for both the torsionally relaxed (full line) and the supercoiled (dashed line) DNA. Green and blue curves refer to the case in which M = 1000 non-binding proteins are, respectively, present or absent in the system.

  (Appendix A), and that proteins may have a non-negligible charge (Appendix B). The size results are shown in Appendix A, Figs. 10-12: these correspond to Figs. 4-6 and were obtained with a protein size of 2σ. The charge results are shown in Appendix B, Figs.

FIG. 13 .

 13 FIG.13. Same as Fig.3, but for simulations with charged proteins and polymer beads. Compression force as a function of the normalized DNA extension in the absence of proteins. (a) Comparison of the force-extension curve obtained from the entropic spring theory of Ref. 10 (full line) and our numerical model (blue circles). Like in the simulations presented in Fig.3, the theory of an infinitesimally thin polymer agrees with the model for R/R 0 > 0.1 but breaks down for R/R 0 < 0.1, where excluded volume effects become significant. The inset shows the results for the "Force" axis in logarithmic scale. Error bars are shown for three different points. (b) Comparison of the force-extension curves obtained from the model with (blue circles) and without (black dots) electrostatic interactions. The inset shows the force axis in logarithmic scale. For f > 10, both models lead to similar force-extension relations (for very large forces, the case with electrostatic interactions is slightly smaller. However this only holds for a single realisation). For f < 10, the extension of the polymer in the presence of electrostatic interactions for a given force is larger than for the case without electrostatics, which was expected since there is an additional long-range repulsion between DNA beads.

FIG. 14 .

 14 FIG. 14. Same as Fig. 4, but for simulations with charged proteins and polymer beads. Compression force as a function of the DNA extension for a varying number M of non-DNA-binding proteins: DNA-only simulations (blue circles), M = 500 (magenta squares), M = 1000 (green triangles), M = 1500 (yellow diamonds), and M = 2000 (orange inverted triangles). The forceextension curves are remarkably similar to the ones in Fig. 4. The linear relations force-M (inset 1-fit: f = 0.0156M + 1.4513) and DNA extension-M (inset 2-fit: f = 0.0212M 9.3741) are also recovered. The comparison between inset 2 and the one in Fig.4shows that the presence of electrostatic interactions leads to a slightly higher DNA extension for a moderate force, as seen in Fig.13.

ACKNOWLEDGMENTS

This work was partially funded by the European Research Council (ERC Consolidator Grant, THREED-CELLPHYSICS, Reference No. 648050). M.C.P. acknowledges studentship funding from EPSRC under Grant No. EP/L015110/1. J.L. was supported by EU intra-European Fellowship No. 623637 DyCoCoS FP7-PEOPLE-2013-IEF.


Amélie Rochet-Capellan
email: amelie.rochet-capellan@icp.inpg.fr

Jean-Luc Schwartz
email: jean-luc.schwartz@icp.inpg.fr

Rafael Laboissière
email: laboissiere@cbs.mpg.de

Arturo Galván
email: galvan@cbs.mpg.de

Finger-jaw coordination during a deictic gesture with CVCV utterances: the effect of stress position

 on speech-hand coordination in deictic expressions and regard to Abry et al.'s (2004) "Vocalize to Localize" framework.

Introduction

General framework and objectives

The deictic gesture has the particularity to require the synchronization between speech and hand. Moreover, for many theorists, it constitutes the primary indexical sign in both phylogeny and ontogeny [START_REF] Haviland | Pointing, gesture spaces, and mental maps[END_REF]. According to the "Vocalize to Localize" framework (Abry et al., 2004), the association of voice and hand in deictic tasks is a key step of both phylogenetic and ontogenetic developments of oral communication. Their hypothesis is that the deictic function is first associated with hand movements and is later connected to the speech. This leads to the close association between the pointing gesture and the "foot", the coordinative unit that gives the basis of the speech rhythm. Two consequences could be derived from this hypothesis. First, hand-voice coordination might occur around the foot stress unit. Second, this coordination might be constrained by the physical and motor properties of the speech and the arm-hand-finger systems. In addition, motor control studies [START_REF] Nelson | Mandibule movements during increasingly rapid articulations of single syllables : Preliminary observations[END_REF] and speech production theories [START_REF] Macneilage | The frame/content theory of evolution of speech production[END_REF] suggest that the jaw plays a basic role in the control of the speech rhythm. Therefore, there should be a close coordination between jaw and hand in deictic tasks. [START_REF] Levelt | Pointing and voicing in deictic expressions[END_REF] investigated the processes underlying the synchronization between voicing and pointing in deictic expressions. The focus was on Gesture plus Speech (GS) task, associating a hand pointing gesture with a verbal response ("dit lampje" vs. "dat lampje", i.e. "this lamp" vs. "that lamp"). Hand and speech performances in the GS task were respectively compared with hand performances in a gesture only (G) task and speech performances in a speech only (S) task. Their main results are: (1) The presence of speech does not affect the time of the pointing apex and has a small delay effect on the initiation time of pointing. (2) On the contrary, the presence of the hand gesture delays the voice onset in a way that tends to synchronize the voice onset with the pointing apex. (3) A mechanical perturbation applied to the hand gesture affects the voice onset only if it is applied during the first stage of motion. According to the authors, these results show that the interaction between the two systems is limited to the planning stage as well as to the initial portion of the execution stage. Moreover, as [START_REF] Holender | Interference between a vocal and a manual response to the samestimuls[END_REF]Feyreisen (1997), they consider this interaction as a competition for resources access.

Investigation of synchronization processes in deictic expressions

Investigation of synchronization sites

The precedent studies did not explore the possible sites of synchronization between the orofacial and the hand gestures. The Levelt et al.'s study provides a first answer to this question: voice onset tends to be synchronized with hand pointing apex. However, Levelt et al. did not vary the position of the speech deictic site, always at the beginning of the utterance ("dat" vs. "dit"). The explanation of their results in the "Vocalize to Localize" framework would be that the speech indexical site tends to be synchronized with the hand pointing apex. Consequently, the speech-hand relationship would go beyond the competition for the common resources during the planning phase. Moreover, in [START_REF] Levelt | Pointing and voicing in deictic expressions[END_REF] speech timing is measured only by the voice onset. One could argue that mandible motion measurements would be more appropriate for studying speech-hand coordination. In this paper, we propose to study how the variation of the stress position in CVCV words affects the coordination between the jaw and the hand in a GS pointing task. Our main hypothesis is that the hand pointing apex would be synchronized with the apex of the jaw opening gesture for the stressed vowel, that is the first vowel in the case of 'CVCV utterances and the second vowel in the case of CV'CV utterances. This alignment could be reached either only by jaw adaptation, the hand motion remaining the same for both 'CVCV and CV'CV sequences, or by a mutual adaptation, involving a modification of both jaw and hand motions from 'CVCV to CV'CV.

Method

Speakers. Twenty native Brazilian Portuguese speakers (4 men and 16 women) participated in the experiment. They were all right-handed, with no speech or hearing pathology record and unaware of the purpose of the experiment. The Brazilian Portuguese language was chosen because it is possible to find pairs of words in this language that differ only by the position of the stress.

Figure 1. Experimental setup

Procedure. As in the princeps study by [START_REF] Levelt | Pointing and voicing in deictic expressions[END_REF], our experiment involved a hand-pointing task associated with a verbal response. The main factor was the stress position in the CVCV sequence (stress on the first vs. the second vowel), which were either /papa/ or /tata/. The vowel /a/ was selected since its realization requires a large jaw opening gesture. Two spatial targets were used for the pointing gesture (near vs. far). Speakers were seated at a table. The targets to point and the item to pronounce were projected on a white board in front of the speaker using a beamer. A black square pasted on the midline of the table indicated the rest position of the finger. Speakers were informed that a word and a red smiley  sign (the target) would appear on the board. The target appeared in the right visual field, either at the near or at the far position (see Fig. 1). In order to make the joint gesture/pronunciation task natural, the subjects were instructed that the displayed word was the name of the person represented by the smiley-target. As soon as the sign color changed from red to green, the subject should point to the smiley and name it at the same time. To become used with the task, subjects were briefly trained prior to the experiment start to point to objects in the room while naming them. Also, the subjects trained to read the CVCV sequences aloud in order to ensure that they understood the stress instruction. Finger and jaw movement were recorder using an Optotrak. Two Optotrak markers were pasted at the tip of index finger, such that at least one of them was always visible by the cameras during the course of the pointing movement. The jaw position was tracked by a third marker attached to the chin. Three other fixed sensors were pasted on the table to provide a referential for the moving sensors. Head motion was measured by three markers attached to a plastic triangle, which was fixed by a strap around the subject's head. Jaw positions were computed in relation to the head moving reference frame. The experiment was divided into four blocks. A block contained 4 practice trials and 40 experimental trials, 10 for each [stress position]*[target position] experimental condition with equal number of /papa/ and /tata/. The order of the trials was randomized for each block and each speaker. The blocks were separated by a 30 seconds pause. The presentation of the red target lasted for 3.5 s plus a Gaussian variation with zero mean and 0.15 s standard deviation. Then, the target became green (the go signal) and lasted 1 s on the board. Data recording and processing. The Optotrak markers positions were sampled against time at 99.474 Hz and the sound was recorded at 16 kHz with a microphone. In order to reduce the amount of data to process, we conducted a Principal Component Analysis (PCA) on the Optotrack data. The first PC explained most of the variance for all subjects: 98.8% (std = 0.7%) and 98.3% (std = 1.2%) for the two finger markers and 95.6% (std = 2.2%) for the jaw marker. Thus, the first PC appears as a good representation of finger and jaw movements. These signals were lowpass filtered at 15 Hz with a Butterworth filter. The temporal measurements are indicated in Fig. 2. Motion onset (initiation) and apex times correspond to 10% of the peak velocity at the beginning and the end of the movement, respectively. P I and P A are the initiation and apex times for the forward movement of the finger, while P R is the onset of the return movement. For the jaw, J I1 , J A1 and J I2 , J A2 are the initiation and the apex times of the first and second opening gestures, respectively. Labeling errors were manually corrected.

Results

Jaw-finger synchronization

The main results are shown in Fig. 3. P A is close to J A1 in the first-stress condition whereas it is more or less at the mid-point between J A1 and J A2 in the second-stress condition. On the contrary, P R is close to J A2 in the second-stress condition and about at the mid-point between J A1 and J A2 in the first-stress condition. We computed the relative finger/jaw synchronization indices for the apex and return instants of the finger as, respectively: P A/J = (P A -J A1 )/(J A2 -J A1 ) and P R/J = (P R -J A1 )/(J A2 -J A1 ) Values of 0 and 1 for these indices indicate synchronization with the first and the second jaw apex, respectively. Within-subject ANOVA with stress and target position as factors on P A/J and P R/J confirms the previous pattern (see Fig. 4). Indeed, the P A/J mean is close to 0 (0.05) in the first-stress condition and to 0.5 (0.47) in the second-stress one (F(1,19) = 65.2, p < .0001), while the P R/J mean is close to 0.5 (0.52) in the first-stress condition and to 1 (1.04) in the second-stress one (F(1,19) = 100.9, p < .0001). The target position has no effect on P A/J (near: 0.25, far: 0.27, F(1,19) = 1.3, p = 0.3) while it affects P R/J (near: 0.73, far: 0.82, F(1,19) = 11.9, p < .003). The stress*target interaction is not significant for both P A/J and P R/J (P A/J : F(1,19) = 4.1, p = .058; P R/J : F(1,19) = 4.1, p = .055).

Unidirectional or bi-directional adaptation?

Initiation times. From Fig. 3, we see that J I1 occurs after P I . The mean (J I1 -P I ) value is greater in the first-stress condition (223 ms) than in the second-stress one (149 ms, F(1,19) = 13.7, p < .002). Yet, the difference between P I values across stress conditions is not significant (F(1,19) = 2.2, p = .08). On the contrary, J I1 occurs 62 ms earlier in the second-stress (475 ms) condition than in the first-stress one (537 ms, F(1,19) = 12.1, p < .0008). In addition, the (J I1 -P I ) mean is higher in the far-target condition (195 ms) than in the near-target one (176 ms, F(1,19) = 10.1, p < .005).

Arrival times. The increase of target distance induces a significant16 ms delay for P A (near: 733 ms, far: 749 ms, F(1,19) = 9.2, p < .007) and a significant 12 ms delay for both J A1 (near: 664 ms, far: 676 ms, F(1,19) = 5.5, p < .03) and J A2 (near: 946 ms, far: 958 ms, F(1,19) = 6.6, p < .02). The difference between P A values in the first-(732 ms) and second-stress (749 ms) conditions is not significant (F(1,19) = 3.0, p = .10). On the contrary, J A1 and J A2 happen earlier in the second-stress condition than in the first-stress one (from 619 to 720 ms for J A1 , F(1,19) = 58.4, p < .0001 and from 899 to 1005 ms for J A2 , F(1,19) = 59.7, p < .0001).

Durations and amplitudes. Fig. 3 suggests that the elapsed time from P I to P A (D P ) increases with target distance (near: 406 ms, far: 431 ms, F(1,19) = 26.3, p < .0001). This comes together with an increase of the distance moved by the finger (A P ) (near: 275 mm, far: 423 mm, F(1,19) = 2220.9, p < .0001). On the contrary, stress position does not affect D P (F(1,19) = 1.4, p = .3) while A P tends to be larger in the secondstress (350 mm) condition as compared to the first-stress one (348 mm, F = 8.3, p < .01). The elapsed time from P A to P R (pointing plateau) is longer in the far-target condition (152 ms) than in the near one (132 ms, F(1,19) = 13.8, p < .002) and in the second-stress condition (157 ms) than in the first-stress one (127 ms, F(1,19) = 14.0, p < .001). Finally, duration (D J1 ) and amplitude (A J1 ) of the first jaw opening motion is higher in the first-stress condition than in the second one (table 1, +41 ms, F(1,19) = 275, p < .0001 and +2.9 mm, F(1,19) = 20.5, p < .0002). The reverse pattern characterizes the second gesture with greater D J1 and A J1 values in the second-stress condition than in the first one (+48 ms, F(1,19) = 40.9, p < .0001 and +3.8 mm, F(1,19) = 40.1, p < .0001). Target position affects neither A J1 and A J2 nor D J1 and D J2 .

Stress

First Second

Target Near Far Near Far

Pointing forward 273 422 276 424

First jaw opening motion 12.6 12.4 9.6 9.6

Second jaw opening motion 3.1 3.3 7.5 7.5

Table 1. Means of amplitude (mm) of finger forward and jaw opening motions according to stress and target position.

Discussion and conclusion

In agreement with our initial hypothesis, stress position affects jaw-finger coordination.

In the first-stress condition, the finger apex P A is synchronized with the first-syllable jaw apex J A1 . In the second-stress condition, the finger return instant P R is synchronized with the second-syllable jaw apex J A2 . It seems then that the remarkable points of the finger trajectory tend to be anchored to the jaw apices of the stressed syllable. These results hold for both near and far target positions. The fact that the finger pointing movement is quite similar across the stress conditions indicates that the jaw movement adapts itself to the hand movement in order to achieve the synchronizations described above. This main result is in agreement with Levelt et al.'s (1985) results.

The position of the target, either near or far, affects both finger and jaw motions. For the finger, the duration of the movement is larger for the farther target position whereas the motion initiation time is unchanged. In addition, the duration of the pointing plateau is about 20 ms longer in the far-target condition in respect to in the near-target one. As regards the jaw movement, target position does not affect amplitude and duration of opening strokes but induces delays on the two apices values (increase of 12 ms for both J A1 and J A2 ). Again, these results suggest that speech adapts to pointing gestures in deictic expressions.

As in Levelt et al.'s study, in which the stress is always placed in the first syllable of the utterance, we found that finger apex is synchronized with the jaw apex of the first syllable in the first-stress condition. However, the pattern of synchronization for the second-stress condition is puzzling. Indeed, when the second syllable is stressed, finger apex happens at about the mid-point between the two jaw apices (about 130 ms after J A1 and 150 ms before J A2 ). The pointing apex is then phased neither with voice onset nor with the jaw apex for the stressed vowel. This pattern may result from two types of jaw adaptation: (1) adaptation to the phonological goal, with a reduction of the first opening motion when the first vowel is unstressed; (2) adaptation for speechpointing coordination, with a lead of jaw initiation time in the second-stress condition as compared to the first-stress one. In addition, we observed that the finger waits for the second jaw apex before starting the return movement.

Results coming from speech development studies can shed some light on why the synchronization pattern is different across stress conditions. Observations of speech and gestures produced by infants [START_REF] Ducey-Kaufmann | When the Speech Frame meets the Sign Frame in a developmental framework[END_REF] showed that the natural frequency of the jaw system is twice that of the hand system. This 2:1 ratio of natural frequencies may be rooted on biomechanical constraints of both the upper limb and the orofacial systems. In order to take this into account, the Central Nervous System would try to accommodate two opening/closing movements of the jaw into one forward/backward movement of the finger. For this aim, one possible strategy would be to always synchronize the finger apex with the jaw apex of the first syllable, regardless of the stress placement. This is what happens in the first-stress condition, but not in the second-stress one. A possible explanation would be that, on the top of this 2:1 timing ratio restriction, there is a communication constraint related to the vocal site of the deictic expression. This second constraint would force the finger apex to synchronize with the stressed vowel.

In this context, the observed pattern of synchronization can be interpreted as satisfying the required alignment of the verbal deictic site with the pointing gesture while maintaining a large range for hand-jaw coordination. Indeed, while coordination is rather strict for the first-stress condition, in order to remove any ambiguity in terms of verbal deictic site, it is in fact sufficient in the second-stress one that the apex arrives somewhere inside the second jaw opening gesture, even if it is not on the very apex. The coordination of the return point with the jaw apex would just indicate that the deictic task is now achieved, and the return movement can now be operated. In this scenario, it would be interesting to test what happens with three or more syllables, to see whether the coordination of apex with jaw apex is respected for all syllables except the last one, for which coordination would be relaxed into a coordination with the return gesture.

Figure 2 .

 2 Figure 2. Example of record and labeling for a /papa/ trial.

Figure 3 .

 3 Figure 3. Mean durations from the GO-signal to various jaw and finger temporal events, as a function of stress and target position.

Figure 4 .

 4 Figure 4. Means of P A and P R values relatively to (J A2 -J A1 ) according to stress and target position.

Acknowledgments

This work is part of the "Patipapa" project funded by the French Ministry of Research (Action Concertée Incitative "Systèmes Complexes en Sciences Humaines et Sociales"). We also wish to thank Christian Abry for his comments on this work.