Virtually every biological rate changes with temperature, but the mechanisms underlying these responses differ between different processes. Here, we bring together the main theoretical approaches used to describe temperature-rate relationships, ranging from empirical curve shapes to reaction-level kinetics and network-based dynamical frameworks. These models highlight how temperature influences not only the speed of elementary reactions, but also the behavior that emerges when many reactions interact through regulation, feedback, or stochastic transitions. By outlining the assumptions and implications of each perspective, we aim to clarify how different modeling strategies connect molecular processes to physiological temperature response curves and to point toward integrative frameworks that can better explain the diversity of biological thermal responses.

The importance of molecular-scale forces in sculpting biological form and function has been acknowledged for more than a century. Accounting for forces in biology is a problem that lies at the intersection of soft condensed matter physics, statistical mechanics, computer simulations and novel experimental methodologies, all adapted to a cellular context. This review surveys how forces arise within the cell. We provide a summary of the relevant background in basic biophysics, of soft-matter systems in and out of thermodynamic equilibrium, and of various force measurement methods in biology. We then show how these ideas can be incorporated into a description of cell-scale processes where forces are involved. Our examples include polymerization forces, motion of molecular motors, the properties of the actomyosin cortex, the mechanics of cell division, and shape changes in tissues. We show how new conceptual frameworks are required for understanding the consequences of cell-scale forces for biological function. We emphasize active matter descriptions, methodological tools that provide ways of incorporating non-equilibrium effects in a systematic manner into conceptual as well as quantitative descriptions. Understanding the functions of cells will necessarily require integrating the role of physical forces with the assimilation and processing of information. This integration is likely to have been a significant driver of evolutionary change.

We present a fully analytical integration of the Maxwell stress tensor and derive exact relations for interparticle forces in systems of multiple dielectric spheres immersed in a polarizable ionic solvent, within the framework of the linearized Poisson--Boltzmann theory. Building upon the screening-ranged (in ascending orders of Debye screening) expansions of the potentials developed and rigorously analyzed in the accompanying works \cite{supplem_pre,supplem_pre_math,supplem_prl}, we construct exact screening-ranged many-body expansions for electrostatic forces in explicit analytical form. These results establish a rigorous foundation for evaluating screened electrostatic interactions in complex particle systems and provide direct analytical connections to, and systematic improvements upon, various earlier approximate or limited-case formulations available in the literature, both at zero and finite ionic strength.

DEAD-box RNA helicases (DDXs) are essential RNA metabolism regulators that typically unwind dsRNA in an ATP-dependent manner. However, recent studies show some DDXs can also unwind dsRNA without ATP, a phenomenon that remains poorly understood. Here, we developed HelixTriad coarse-grained RNA model, incorporating Watson-Crick base pairing, base stacking, and electrostatics within a three-bead-per-nucleotide scheme to accurately reproduce experimental RNA melting curves. Molecular dynamics simulations showed that weak, specific DDX3X-dsRNA interactions drive stochastic strand separation without ATP. Free energy analysis revealed that successful unwinding via high-entropy, stand-displacing intermediates. Furthermore, we introduced Entropy-Unet, a deep learning framework for entropy prediction, which corroborated theoretical estimates and uncovered a hierarchical pattern of entropy contributions. Together, our findings suggest that ATP-independent dsRNA unwinding by DDXs is predominantly entropy-driven, offering new mechanistic insights into RNA helicases versatility.

Phase separation in confined environments is a fundamental process underlying geological flows, porous filtration, emulsions, and intracellular organization. Yet, how confinement and activity jointly govern coarsening kinetics and interfacial morphology remains poorly understood. Here, we use large-scale molecular dynamics simulations to investigate vapor-liquid phase separation of passive and active fluids embedded in complex porous media. By generating porous host structures via a freeze-quench protocol, we systematically control the average pore size and demonstrate that confinement induces a crossover from the Lifshitz-Slyozov power-law growth to logarithmically slowed coarsening, ultimately arresting domain evolution. Analysis of correlation functions and structure factors reveals that confined passive systems exhibit fractal interfaces, violating Porod's law and indicating rough morphological arrest. In contrast, introducing self-propulsion dramatically changes the coarsening pathway: activity restores smooth interfaces, breaks the confinement-induced scaling laws, and drives a transition from logarithmic to ballistic domain growth at high activity levels. Our findings reveal an activity-controlled mechanism to overcome geometric restrictions and unlock coarsening in structurally heterogeneous environments. These insights establish a unifying framework for nonequilibrium phase transitions in porous settings, with broad relevance to active colloids, catalytic media, and biologically crowded systems, where living matter routinely reorganizes within geometric constraints to sustain function.

We present an analytical many-body formalism for systems of spherical particles carrying arbitrary free charge distributions and interacting in a polarizable electrolyte solution, that we model within the linearized Poisson--Boltzmann framework. Building on the detailed spectral analysis of the associated nonstandard Neumann--Poincaré-type operators developed in our companion study~\cite{supplem_pre_math}, we construct exact explicit expansions of the electrostatic potential and energy in ascending orders of Debye screening thereby obtaining systematic "screening-ranged" series for potentials and energies. These screening-ranged expansions provide a unified and tractable description of many-body electrostatics. We demonstrate the versatility of the approach by showing how it generalizes and improves upon both classical and modern methods, enabling rigorous treatment of heterogeneously charged systems (such as Janus particles) and accurate modeling of higher-order phenomena (such as asymmetric dielectric screening, opposite-charge repulsion, like-charge attraction) as well as yielding many-body generalizations to analytical explicit results previously known only in the two-body setting.

We analyse the stability of viscoelastic Dean flow (flow of an elastic fluid through a curved two-dimensional channel, driven by an azimuthal pressure gradient) in the absence of fluid inertia. This configuration is well known to exhibit a hoop-stress-driven `purely elastic' instability (referred to henceforth as the hoop-stress mode -- `HSM') on account of the base-flow streamline curvature. The objective of this study is to demonstrate the existence and importance of a distinct elastic instability in this flow configuration, which is not driven by hoop-stresses, but instead is a continuation of a novel `centre-mode' (CM) instability recently identified in rectilinear shear flows. We use both the Oldroyd-B and FENE-P models to map out parameter regimes in the $W\!i$--$\epsilon$--$\beta$ space where the aforementioned instabilities are present. Here, $W\!i$ is a suitably defined Weissenberg number that characterizes fluid elasticity, $\beta$ is the ratio of solvent to total solution viscosity, and $\epsilon$ is the ratio of the gap (channel) width to the radius of curvature. For FENE-P model, decreasing the finite extensibility parameter $L$ has opposing effects on the HSM and CM instabilities -- stabilising the former, but destabilising the latter. In the dilute solution regime ($\beta > 0.95$), and for realistic values of $L \sim O(100)$, corresponding to polymer molecular weights of $O(10^{5-6})$g/mol, the CM remains the most unstable mode for $\epsilon \leq 0.25$, rendering it potentially relevant to the onset of elastic turbulence in the flow of such polymer solutions through curved channels.

The paper describes the coupling of the MercuryDPM discrete element method (DEM) code and the implementation of the kernel-independent fast multipole method (KIFMM). The combined simulation framework allows addressing the large class of multiscale problems, including both the mechanical interactions of particulates at the fine scale and the long-range interactions of various natures at the coarse scale. Among these are electrostatic interactions in powders, clays, and particulates, magnetic interactions in ferromagnetic granulates, and gravitational interactions in asteroid clouds. The formalism of rigid clumps is successfully combined with KIFMM, enabling addressing problems involving complex long-large interactions between non-spherical particles with arbitrary charge distributions. The capabilities of our technique are demonstrated in several application examples.

We introduce a high-throughput platform that enables simultaneous, parallel testing of six bistable beams via programmable motion of a rotating disk. By prescribing harmonic angular dynamics, the platform explores the phase space of angular velocity and acceleration $(\Omega,\,\dot{\Omega})$, producing continuously varying centrifugal and Euler force fields that act as tunable body forces in our specimens. Image processing extracts beam kinematics with sub-pixel accuracy, enabling precise identification of snap-through events. By testing six beams in parallel, the platform allows systematic variation of beam thickness, pre-compression, tilt angle, and clamp orientations across 65 distinct configurations, generating 23,400 individual experiments. We construct stability boundaries and quantitatively parameterize them as parabolic functions, characterized by a vertical offset and a curvature parameter. Tilt angle provides the most robust mechanism for tuning the curvature parameter, while beam thickness and pre-compression modulate vertical offset. Modal decomposition analysis reveals that antisymmetric clamp configurations can trigger mode switching, in which competing geometric and inertial effects drive transitions through different deformation pathways. Our work establishes a scalable experimental framework for high-throughput characterization of dynamic nonlinear instabilities in mechanics. The complete experimental dataset is made publicly available to support data-driven design and machine learning models for nonlinear mechanics with applications to bistability-based metamaterials, mechanical memory, and electronics-free sensing systems.

The interaction of particles in an electrolytic medium can be calculated by solving the Poisson equation inside the solutes and the linearized Poisson--Boltzmann equation in the solvent, with suitable boundary conditions at the interfaces. Analytical approaches often expand the potentials in spherical harmonics, relating interior and exterior coefficients and eliminating some coefficients in favor of others, but a rigorous spectral analysis of the corresponding formulations is still lacking. Here, we introduce composite many-body Neumann--Poincaré-type operators and prove that they are compact with spectral radii strictly less than one. These results provide the foundation for systematic screening-ranged expansions, in powers of the Debye screening parameters, of electrostatic potentials, interaction energies, and forces, and establish the analytical framework for the accompanying works~\cite{supplem_prl,supplem_pre,supplem_pre_force}.

The paper presents a two-phase hydrodynamic model for the numerical simulation of collective motion in a thin layer of active colloids containing spherical microswimmers. The model accounts for three fundamental mechanisms governing the dynamics of the active colloid: the random motion of the microswimmers, their mutual collisions, and their interaction with the surrounding fluid phase. The accurate resolution of the characteristic time scales associated with each mechanism is crucial for reproducing the different dynamic modes. The model reproduces two primary modes of motion: Brownian and collective, as well as the transition between them. It is demonstrated that hydrodynamic interactions begin to play a significant role when the microswimmer velocity exceeds a critical threshold. At this point, the kinetic energy transferred to the fluid phase is sufficient to generate a noticeable feedback effect on the swimmers' motion. Conversely, a further increase in microswimmers' velocity enhances the role of collisions, causing the system to revert from a collective mode back to a Brownian-like state. A similar transition occurs at higher volume fractions of microswimmers within the colloid.

Machine learning (ML) has become a versatile tool for analyzing anomalous diffusion trajectories, yet most existing pipelines are trained on large collections of simulated data. In contrast, experimental trajectories, such as those from single-particle tracking (SPT), are typically scarce and may differ substantially from the idealized models used for simulation, leading to degradation or even breakdown of performance when ML methods are applied to real data. To address this mismatch, we introduce a wavelet-based representation of anomalous diffusion that enables data-efficient learning directly from experimental recordings. This representation is constructed by applying six complementary wavelet families to each trajectory and combining the resulting wavelet modulus scalograms. We first evaluate the wavelet representation on simulated trajectories from the andi-datasets benchmark, where it clearly outperforms both feature-based and trajectory-based methods with as few as 1000 training trajectories and still retains an advantage on large training sets. We then use this representation to learn directly from experimental SPT trajectories of fluorescent beads diffusing in F-actin networks, where the wavelet representation remains superior to existing alternatives for both diffusion-exponent regression and mesh-size classification. In particular, when predicting the diffusion exponents of experimental trajectories, a model trained on 1200 experimental tracks using the wavelet representation achieves significantly lower errors than state-of-the-art deep learning models trained purely on $10^6$ simulated trajectories. We associate this data efficiency with the emergence of distinct scale fingerprints disentangling underlying diffusion mechanisms in the wavelet spectra.

We present an exact many-body framework for electrostatic interactions among $N$ arbitrarily charged spheres in an electrolyte, modeled by the linearized Poisson--Boltzmann equation. Building on a spectral analysis of nonstandard Neumann--Poincaré-type operators introduced in a companion mathematical work~\cite{supplem_pre_math}, we construct convergent screening-ranged series for the potential, interaction energy, and forces, where each term is associated with a well-defined Debye--Hückel screening order and can be obtained evaluating an analytical expression rather than numerically solving an infinitely dimensional linear system. This formulation unifies and extends classical and recent approaches, providing a rigorous basis for electrostatic interactions among heterogeneously charged particles (including Janus colloids) and yielding many-body generalizations of analytical closed-form results previously available only for two-body systems. The framework captures and clarifies complex effects such as asymmetric dielectric screening, opposite-charge repulsion, and like-charge attraction, which remain largely analytically elusive in existing treatments. Beyond its fundamental significance, the method leads to numerically efficient schemes, offering a versatile tool for modeling colloids and soft/biological matter in electrolytic solution.

A superfluid flows without friction below a critical velocity, exhibiting zero drag force on impurities. Above this threshold, superfluidity breaks down, and the internal energy is redistributed into incoherent excitations such as vortices. We demonstrate that a finite-mass, mobile impurity immersed in a flowing two-dimensional paraxial superfluid of light can \textit{swim} against the superfluid current when this critical velocity is exceeded. This self-propulsion is achieved by the periodic emission of quantized vortex-antivortex pairs downstream, which impart an upstream recoil momentum that results in a net propulsive force. Analogous to biological systems that minimize effort by exploiting wake turbulence, the impurity harnesses this vortex backreaction as a passive mechanism of locomotion. Reducing the impurity dynamics to the motion of its center of mass and using a point-vortex model, we quantitatively describe how this mechanism depends on the impurity geometry and the surrounding flow velocity. Our findings establish a fundamental link between internal-energy dissipation in quantum fluids and concepts of self-propulsion in active-matter systems, and opens new possibilities for exploiting vortices for controlled quantum transport at the microscale.

Self-propelled particles accumulate on repulsive barriers in so-called active wetting, but the relationship between this process and equilibrium wetting remains unclear. Using an exact (noiseless) hydrodynamic framework for an active lattice gas, we show, using a slit geometry with periodic boundary conditions, that active matter exhibits both fully- and partially-wet states, with a critical wetting transition between them. Furthermore, we demonstrate the existence of a spontaneous-symmetry-breaking ratchet current in the partially wet state, leading to departure of the bulk densities from their binodal values and the emergence of a novel dynamical pathway for the full-to-partial wetting transition. We elucidate this modified dynamical pathway using a minimal model. The results, while establishing a direct connection between active and equilibrium wetting, also identify the nonequilibrium consequences of activity.

The association and dissociation of ion pairs in water are fundamental to physical chemistry, yet their reaction coordinates are complex, involving not only interionic distance but also solvent-mediated hydration structures. These processes are often represented by free-energy landscapes constructed from collective variables (CVs), such as interionic distance and water bridging structures; however, it remains uncertain whether such representations reliably capture the transition pathways between the two associated and dissociated states. In this study, we employ deep learning to identify reaction coordinates for NaCl ion pair association and dissociation in water, using the committor as a quantitative measure of progress along the transition pathway through the transition state. The solvent environment surrounding the ions is encoded through descriptors based on atom-centered symmetry functions (ACSFs), which serve as input variables for the neural network. In addition, an explainable artificial intelligence technique is applied to identify ACSFs that contribute to the reaction coordinate. A comparative analysis of their correlation with CVs representing water bridging structures, such as interionic water density and the number of water molecules coordinating both ions, further provides a molecular-level interpretation of the ion association-dissociation mechanism in water.

In simulation studies of crystallisation, the size of the largest crystalline nucleus is often used as a reaction coordinate to monitor the progress of the nucleation process. Here, we investigate, for the case of homogeneous ice nucleation, whether the nucleus size exhibits Markovian dynamics, as assumed in classical nucleation theory. Using 300 independent nucleation trajectories generated by molecular dynamics, we evaluate the mean recurrence time required to reach selected values of the largest nucleus size. Early recurrences consistently take longer than later ones, revealing a clear history dependence and thus non-Markovian dynamics. To identify the slow modes underlying this behaviour, we analyse several structural descriptors of the nucleus, observing subtle but systematic differences between nuclei at early and late recurrences. By training a neural network on 2,700 short trajectories to learn the committor, we identify relevant collective variables. Based on these features, symbolic regression provides a compact approximation of the committor, i.e., an improved reaction coordinate, which we subsequently test for Markovianity.

Nematic elastomers are a particular class of liquid crystal elastomers (LCEs) that exhibit both liquid-crystalline order and rubber (entropic) elasticity. This combination makes them stimuli-responsive soft materials with a number of unusual thermo-mechanical properties. They have been proposed for various applications, including soft robotics, enhanced adhesion, and impact resistance. This paper presents a new experimental setup and a comprehensive dataset characterizing the soft behavior of nematic elastomers over a range of temperatures and strain rates. We also fit the results to a recently developed model of nematic elastomers.

We revisit the Fokker-Planck based theory of driven polymer translocation through a narrow nanopore. A bead-spring model of a uniformly charged polyelectrolyte chain translocating through a semi-implicit model of a nanopore embedded in a membrane are used to gain insights into the underlying free energy landscape and kinetics of translocation. The free energy landscape is predicted using metadynamics simulation, an enhanced sampling method. A direct comparison with the theoretical free energy formulation proposed in the literature allows us to introduce a modification related to the entropic contribution in the theory. Additional classical Langevin dynamics simulation runs are performed to obtain the translocation time distribution for polymers of lengths $N$ driven by voltages $V$ through nanopores of radii $r_p$. In agreement with earlier reports, a scaling of the mean translocation time $\langle \tau_\text{LD} \rangle \sim N^\alpha/V$ is observed, with $\alpha \sim 1.40 - 1.48$ depending on the nanopore size. Fitting the mean first passage time given by the Fokker-Planck theory, $\langle \tau_\text{FP}\rangle$,to simulation results helps gain insights into the diffusivity $k_\text{FP}$ used in the theory. We report a scaling of $k_\text{FP}\sim N^\beta$. The $r_p-$dependent values of the exponent $\beta$ significantly deviate from the Rouse theory prediction of $\beta = -1$ for center-of-mass diffusivity of a polymer chain.

Chirality organize living and active matter systems into striking collective states, yet the principles that govern chiral ordering in open systems, where elements are continuously added or removed, remain unclear. A mutant strain of Dictyostelium discoideum deficient in chemotaxis (KI cells) forms centimeter-scale, clockwise multi-armed spirals. Each arm is a traveling band produced by short-range alignment interactions and guided by a polar-ordered rotating core that encircles the cell source. A subtle clockwise bias in the single-cell migration is amplified by collective ordering into tissue-scale chirality. To uncover the minimal ingredients, we developed an open chiral Vicsek model and identified intrinsic chirality and continuous cell supply as the key factors. Our study establishes a general route by which weak microscopic chirality and sustained material flux generate macroscopic chiral order, offering new insight into chiral patterning in multicellular behaviors.