Papers

In the present work, we study the two-component model of microtubules, the basic components of the eukaryotic cytoskeleton. We introduce a couple of estimations, which tremendously simplified the model. The paper is devoted to tangential oscillations of dimers, but we explain that the model can explain the radial oscillations as well. Finally, we study the stability of all solutions of differential equations, describing the dynamics of the microtubules.

Critical systems sit near boundaries between qualitatively distinct behaviors. When inferring models of neural activity, this proximity to criticality is thought to require the precise tuning of parameters. Here, we show that as the number of neurons increases, criticality can emerge naturally without fine-tuning. When computing observable statistics from parameters (the forward problem), some small regions in parameter space map to large regions in statistics space. These special parameters are precisely those near criticality. Thus, when inferring parameters from experimental measurements (the inverse problem), models concentrate near critical points, and this concentration becomes stronger as the system grows. We illustrate this flow toward criticality across many large-scale recordings in the mouse brain. In the Curie-Weiss model of Ising spins, we find that all of the recordings collapse to a first-order phase transition, despite substantial differences in the underlying systems. Together, these results suggest a resolution to the tension between criticality and fine-tuning in models of neural activity.

Biological tissues grow by continuously producing, transporting, and reorganizing multiple interacting constituents. These processes are intrinsically coupled to finite deformation and residual stress. Existing models typically capture either finite growth kinematics or multi-constituent transport, but rarely both within a thermodynamically consistent setting. In particular, existing approaches do not consistently link the volume created by finite growth to the mass produced for each individual constituent. In this work, we develop a general continuum framework that unifies finite growth kinematics and multiphase mixture theory for fully saturated multi-constituent mixtures containing an arbitrary number of dilute dissolved solutes. Formulated in a solid-skeleton-based description, the framework rests on constituent-wise balance laws and a free-energy dissipation principle, from which thermodynamically admissible constitutive closures are derived for all mass-exchange, transport, reaction, and growth processes. The central novelty of the framework is a coupling between growth-induced volume creation and constituent mass production, expressed through volume accumulation fractions that distribute the newly created volume among the constituents while preserving saturation. We cast the resulting model in a total Lagrangian mixed weak form and specialize the general theory to a four-constituent, two-solute model of avascular tumor growth that couples nutrient transport, waste production, phenotype transitions between proliferative, hypoxic, and necrotic cells, volume growth, elastic deformation, and growth-induced residual stress. The model is implemented within a finite element setting and its capabilities are demonstrated on representative benchmark problems.

In aquatic environments, gradients in flow velocity and turbulence define a constantly shifting landscape that sets the physical constraints on propulsion and energy expenditure in fish. Successfully navigating these complex flows depends on the ability to sense and respond to subtle hydrodynamic cues in an energy-efficient manner. Revealing these mechanisms is central to understanding how fish identify and exploit favorable flow conditions. To this end, we designed a hydrodynamic maze with spatially varying mean flow and turbulence, creating a controlled heterogeneous flow landscape for understanding fish movement strategies. We found that fish escape energetically unfavorable regions by yawing their bodies relative to the flow. These yawed orientations appear to facilitate lateral migration through lift forces and may improve sensitivity to local flow variations. Notably, fish maintain body orientations near, but below, predicted stall conditions to exploit lift without a significant increase in drag. These findings provide insight into how fish navigate heterogeneous hydrodynamic environments, with potential relevance for both natural and engineered flow systems.

Stochastic models of gene expression typically begin with a microscopic model of transcription and propagate its statistics to the RNA distribution. Here we develop a doubly stochastic framework in which RNA production is Poisson conditional on a time-varying transcription rate $\lambda(t)$. For RNA lifetimes drawn independently from an arbitrary finite-mean distribution, we derive the RNA Fano factor in terms of the lifetime survival function and the autocovariance of $\lambda(t)$. For a Poisson degradation process with rate $\mu$, the survival kernel provides an exponential temporal filter, and the result depends only on the mean, variance, and normalized autocorrelation of $\lambda(t)$. These quantities may be calculated from an explicit rate model or, under ergodicity and adequate sampling, estimated from a sufficiently long rate trajectory. We validate the data-driven estimator using simulated transcription-rate trajectories, without supplying the known autocorrelation to the estimator. We also obtain exact analytical results for transcription-rate dynamics modeled by a discrete M/M/1 process and by drift-diffusion with reflecting, periodic, or first-passage-reset boundaries, and verify each result by direct simulation of the coupled transcription-rate and RNA copy-number processes.

Many systems in soft and living matter bind through a limited number of bonds per particle: proteins associate via discrete surface patches, nucleic acids form one-to-one contacts, and the phase behaviour of multivalent biomolecules is governed by the number of binding sites they carry. In simulations, valency limits are typically enforced with patchy particles, whose anisotropic potentials require integration of rotational degrees of freedom and combine hard cores with narrow patches, which forces small timesteps and commits the model to a fixed binding-site geometry that is often unknown, flexible or mobile. We introduce the valency-bounding correction (VBC), a many-body modification of generic short-range pairwise potentials that smoothly suppresses attraction once the neighbour count of either interacting particle exceeds a prescribed valency. The correction carries no angular degrees of freedom, applies on top of soft repulsive cores and evaluates in two passes over the neighbour list at the cost of a standard pairwise potential. The VBC drives the coordination number to the prescribed valency with low error, while its cluster statistics depart from Wertheim and Flory-Stockmayer predictions through unrestricted ring formation. A tuned variant exchanges bonded partners through ordinary molecular dynamics, reducing bond lifetimes at high saturation by an order of magnitude. On GPUs the cost of the correction is nearly independent of valency, reaching an almost tenfold advantage over a patchy-particle reference. We illustrate large-scale applications by reproducing the reentrant aggregation of repeat-expanded RNA, and show that the VBC also remedies the Fisher-Ruelle thermodynamic instability of soft-core potentials with attraction. The VBC is available as an open-source GPU plugin for HOOMD-blue.

A general scheme of evolution from autocatalytic processes to socio-technological system is discussed from the perspective of maximum energy dissipation principle. The scheme treats the emergence of biological systems as an initial stage in the acceleration of global free energy dissipation. The sequential emergence of qualitatively new levels of organisation (biological cell, multicellular organism, social system) has been proposed, the levels are suggested as the results of cooperation (symbiosis) of the dissipative systems at the previous levels. The cooperation/symbiosis provides sufficient complexity for development of the next, essentially a new level of energy dissipation, leading to a new form of free energy utilization and a new form of information mapping. From a thermodynamic perspective, every qualitatively new level of biological organisation provides an additional step to increase the rate of energy dissipation from qualitatively new sources, essentially widening the number of these sources involved in the utilization.

A common formalization of homeostasis is the free energy principle, a framework that defines a set of desired observation values, or critical states, that the agent should reach or remain close to. Under the free energy principle, an agent should act to maximize the probability of receiving the desired observations. Here we revisit the common approach of solving the problem of maximizing the log probability of the desired observations by maximizing a variational lower bound, the so-called negative free energy. We show that, instead, an approach directly maximizing that probability under the agent's policy is better suited to, and provides a better solution for, the original homeostatic control problem. This is done using hidden Markov model (HMM) control by allowing the policy to act over hidden states or noisy versions thereof while trying to maximize the probability of repeatedly having the desired observations. HMM control largely improves performance over the variational, or free energy, approach. We also show that the optimal policy is strictly deterministic, while the variational approach leads to a stochastic policy approximation. We finally provide a homeostatic reinterpretation of the maximum occupancy principle -a principle proposing that agents ought to maximize the occupancy of action-state path space -by defining homeostatic states as any states that do not immediately entail the termination or death of the agent.

The maximum energy dissipation principle can be successfully applied to describe a range of basic nonlinear models. The application of the general variational framework (Moroz, 2008, 2009, 2010) has been illustrated for basic linear schemes. The study presented in the paper shows how the kinetic processes at this level of the biomolecular and biophysical phenomena can be effectively described in terms of the maximum energy dissipation principle and its variational formulation. On the basis of this approach, a range of Lagrangians was proposed for basic nonlinear dissipative kinetic models. The results of this study show that the framework is in agreement with nonlinear equilibrium thermodynamics.

Condensates can accelerate reactions by concentrating reactants, yet product accumulation can eliminate this advantage. We show that product-induced suppression of condensation generates rotating, deforming, and propagating condensate domains through spontaneous symmetry breaking. Their reorganization shifts reaction sites away from accumulated products while retaining reactant enrichment, yielding rates above those of static condensates and homogeneous states. Reaction flows can thus self-organize condensates into dynamic reactors, with implications for biological and synthetic systems.

Active quantum matter has very recently emerged at the intersection between bio- and quantum many-body physics, combining the self-organization of living systems with the coherence of the quantum world. Active quantum systems have been shown to exhibit flocking - a collective phenomenon with no precedent in equilibrium quantum physics. In this work we uncover an unexpected layer of quantum order in active quantum flocks: they can become superfluid. We show that, in addition to the symmetry breaking associated with their directed motion, these flocks can also break an additional U(1) symmetry, giving rise to off-diagonal long-range order. For a microscopic model of active hard-core bosons governed by Lindblad dynamics, we derive an effective long-wavelength description of the single-particle density matrix and demonstrate that the flocking phase develops an instability toward off-diagonal long-range order characteristic of superfluid behavior. Our findings reveal active quantum matter as a promising research direction for discovering exotic nonequilibrium phases of quantum matter.

Motile microorganisms inhabit oscillatory flows encountered in physiological and engineering systems, yet the influence of flow unsteadiness on their shear-induced preferential concentration remains less understood. We investigate transverse transport of elongated microswimmers in oscillatory pressure-driven channel flow using complementary Langevin simulations and two- and one-dimensional Fokker-Planck models. Agreement between particle-based and continuum descriptions across governing parameters establishes the fidelity of the Fokker-Planck formulation for swimmer transport in oscillatory shear flows. Our analysis demonstrates that oscillatory forcing fundamentally modifies classical steady-flow shear-trapping. We find that increasing Womersley number $Wo$ reduces centerline depletion by confining oscillatory shear to thinner near-wall regions. In contrast, increasing frequency ratio $\beta$, of swimmer rotational diffusion rate and flow oscillation frequency, promotes sustained orientational anisotropy, and leads to a saturating increase in centerline depletion. In the weak-swimming limit, we derive a hierarchy of coupled orientational moments, enabling analytical solutions at arbitrary temporal harmonic order. The asymptotic solutions establish a universal transfer law showing that, for a given swimmer shape, the normalized leading-order orientational response depends solely on $\beta$. Reconstruction of the orientational distribution yields closed-form expressions for the swimmer concentration profile and depletion index, $I_D$. Our asymptotic solution recovers the steady-flow weak-shear scaling of $I_D$ with flow Peclet number, $Pe_f$, of $I_D \propto Pe_f^2$, and reveals that oscillatory forcing attenuates this response by a factor $16\beta^2/(1+16\beta^2)$. These results provide a theoretical framework to predict transverse transport of microswimmers in oscillatory channel flows.

It is demonstrated experimentally that the effective separation of bi-dispersed microspheres (dust particles) in the underdamped and strongly-coupled regime is realized using a designed dusty plasma ratchet. Experimental findings reveal that these dust particles can undergo directional transport at varying speeds, even moving in opposite directions depending on the discharge conditions, enabling successful particle separation. Numerical simulations of the plasma environment surrounding the dust particles are performed using fluid simulations of the capacitively coupled discharge of Argon. The simulation results indicate that the bi-dispersed dust particles are suspended at different balance heights within the plasma sheath and experience distinct ratchet potentials that govern their directional transport, resulting in varied flow velocities. The discovery of height-dependent transport of dust particles here provides insights of transport fundamental of underdamped strongly-coupled particles in dusty plasma ratchets.

Optical tweezers are a versatile tool in the domain of cytology, enabling trapping and manipulation of individual cells. However, the incidence of laser light causes photodamage to biological matter, even when operating at low optical powers and over short periods of time. Here, we demonstrate stable trapping of single living Saccharomyces cerevisiae yeast cells in vitro using a dark optical trap operating in the repulsive regime of light-matter interactions. In contrast to standard tweezers, our dark optical trap confines cells for hours with negligible disruption to their morphology and reproduction cycle, opening up new possibilities for long duration experiments with living organisms such as the observation of cell reproduction under laser trapping.

Elastic buckling has explained shape formation in growing tissues, yet the role of tissue fluidity remains elusive. We derive a minimal fluidized growth-elasticity model as a nonlinear analogue of Maxwell rheology. Analysis of a growing strip reveals a different picture of growth-induced morphogenesis: rather than emerging at a critical stress, symmetry breaking develops continuously during growth. Fluidity regulates stress evolution, the rate of shape-symmetry breaking, and flow patterns, establishing it as an active regulator of morphogenesis beyond its intuitive role in stress relaxation.

Interstitial flow plays a key role in drug delivery, angiogenesis, cancer, and edema. Recent studies have identified connected interstitial pathways that can transport material over long distances. However, the spatial scale of physiological interstitial flow remains unclear: despite the existence of connected pathways, flow may be dominated by nearby vascular filtration or extend over longer distances. Here, we identify organ-scale interstitial flow patterns in the chick yolk sac and elucidate the mechanisms that determine their spatial scale. The yolk sac contains an organ-scale interstitial region that enables large-scale flow patterns to be identified without truncation. Our approach combines experimental imaging with computational modeling of coupled blood and interstitial flow in the whole yolk sac. Scaling analysis identifies a hydraulic conductivity ratio that controls the transition from small-scale to organ-scale flow. In organ-scale patterns, we find interstitial flow speed to be substantially greater than the transvascular flow speed. Mechanistically, the organ-scale patterns arise from the spatial segregation of the leakage and drainage of interstitial fluid from the vessels. These findings have important implications for biological transport by interstitial flow, suggesting that flow-mediated cues may be sensed locally but generated nonlocally.

Massive jellyfish swarms observed at sea can extend over tens of kilometres and contain millions of individuals, yet the mechanisms governing their formation and large-scale dynamics remain poorly understood. Agent-based models provide a framework for describing this dynamics based on jellyfish responses to ocean currents and environmental cues, but become computationally prohibitive when extended to large populations and spatial scales relevant to ocean circulation. Here we derive a continuous kinetic theory from an active-particle model of jellyfish motion. The resulting Fokker-Planck framework incorporates transport by prescribed currents, stochastic reorientation, direct interactions and stimulated steering, allowing chemical signalling to be represented through a coupled field. We further derive a hydrodynamic closure for large swarms by exploiting the separation between fast orientational and slow spatial dynamics, yielding a reduced density equation suitable for implementation in ocean-current models. This framework provides a route from individual behavioural mechanisms to continuum descriptions of jellyfish populations and establishes a basis for constraining model parameters using observations and in-situ measurements. This approach offers a theoretical foundation for future numerical prediction of large jellyfish swarm formation and evolution in realistic ocean flows.

We develop a method to extract the shear viscosity of a lipid membrane from equilibrium molecular dynamics simulations. The method characterizes the rheology of general interfacial systems embedded in three-dimensional media; we term it molecular interfacial rheology. In our simulations the planar bilayer and surrounding water are confined between solid, parallel walls. Following Onsager's regression hypothesis, membrane and water fluctuations are assumed to relax according to the coupled continuum-mechanical equations governing the confined system—which predict that the membrane transverse velocity autocorrelation function (TVACF) decays exponentially, at a rate set by the membrane and water viscosities. The measured TVACF, however, exhibits damped oscillations followed by a slowly decaying tail. We reconcile these behaviors using the Mori–Zwanzig formalism, and extract the wavevector-dependent membrane viscosity from the time-integral of the TVACF. Results from theory and simulations agree over a decade of wavevectors, and extrapolating to long wavelengths yields shear viscosities ranging from 0.064 to 0.18 pN*us/nm across two representative single-component, fluid-phase bilayers. Our results are corroborated by nonequilibrium simulations where a spatially varying in-plane body force is applied to lipid molecules, thus validating the framework of molecular interfacial rheology.

Many semiflexible filaments exhibit structural asymmetries that lead to a curved ground state (curvature in the absence of thermal fluctuations, external forces, or torques). This property is known as spontaneous curvature. Many semiflexible polymers can reversibly develop spontaneous curvature through internal conformational transitions or through transient interactions with their molecular environment. In this article, we show that such reversible spontaneous curvature, even in the absence of transitions in the value of the bending stiffness, provides a minimal mechanism for ensemble-dependent elasticity. We formulate an analytically tractable two-state modified wormlike-chain model in two dimensions, in which an uncurved state (without spontaneous curvature) competes with a curved state (possessing spontaneous curvature). The transition is controlled by an activation energy. Within the weak-bending approximation, we obtain the Gibbs and Helmholtz partition functions for a stretched filament with reversible constant (uniform along the backbone) or sinusoidal spontaneous curvature and for a grafted bistable filament driven either by an end torque or a bending force. Stretching or bending induces a crossover from spontaneous-curvature-dominated to entropic elasticity. We show how the elastic response differs in the two ensembles (Gibbs vs Helmholtz). In addition, we consider the case where the curved state is also characterized by a higher value of the bending stiffness. In that case, a reentrant transition is possible as we stretch the bistable filament.

Transferable coarse-grained (CG) force fields compress chemical space: by aggregating atoms into a reduced set of interaction beads, models such as MARTINI reduce the number of distinguishable compounds by roughly three orders of magnitude, making high-throughput screening of thermodynamic properties tractable across soft matter, with drug–membrane permeability as a well-developed example. The compression is lossy and, so far, one-way: a screen returns a combination of beads, with no established route back to the compounds it stands for. Recovering those compounds–compositional backmapping–is a one-to-many inverse map, distinct from the better-studied conformational problem of rebuilding atomic coordinates from a known mapping. Here we formulate compositional backmapping as conditional graph generation by introducing juniper, a discrete denoising diffusion model over molecular graphs conditioned on the octanol–water partition free energy $\Delta G_{\mathrm{W} \mapsto \mathrm{O}}$, the principal driver of MARTINI bead type assignment and hence a proxy for bead identity. Trained on molecules of up to 9 heavy atoms mapped onto one or two beads, juniper generates molecules that are 93% valid and 92% unique for two-bead targets, and whose $\Delta G_{\mathrm{W} \mapsto \mathrm{O}}$ distributions track the target $\Delta G^{\mathrm{CG}}_{\mathrm{W} \mapsto \mathrm{O}}$ linearly ($r^{2} \geq 0.96$), departing only in the hydrophobic and hydrophilic tails. Although the model receives no chemical information beyond a single scalar, the functional groups shift systematically with the imposed free energy, from branched hydrocarbons at the apolar end to amides, imides, and isocyanates at the polar end. A bead combination flagged by a CG screen can therefore be turned into candidate molecules for atomistic study or synthesis.

Connecting the autonomous kinematics of active matter to its emergent macroscopic transport is constrained by the requirement for high-resolution Eulerian velocity fields. Here, we use confined bacterial suspensions as a model active fluid, mapping the sparse Lagrangian trajectories of fluorescent spy cells into (2+1)-dimensional geometric braids that directly encode the spatiotemporal entanglement of the flow. We use the finite-time braiding exponent (FTBE) as a proxy to quantify the topological entropy and chaotic mixing of confined bacterial suspensions. In moderately confined wet systems, we find that hydrodynamic coupling drives a structural transition from a dilute active gas to coherent vortices, and ultimately to active turbulence, revealing three distinct density-dependent regimes of topological mixing. Conversely, truncating the hydrodynamic screening length via extreme confinement drives the system toward a dry active matter limit. In this limit, dense in-plane steric collisions suppress irreducible entanglement and substantially reduce the FTBE at high particle densities. By evaluating the topological complexity generated per encounter, we reveal a transition from a discrete geometric encounter regime to an areal escape mechanism. Finally, we establish a square-root scaling between the FTBE and the effective diffusivity, placing the self-sustained mixing of active fluids into the pathline braiding universality class.

Deng et al. discovered that Drt3b synthesizes protein-primed poly(AC) DNA without a nucleic-acid template. The structures reveal a finite protein architecture and product-associated contacts, but not the renewable dynamical rule that converts a bounded pocket into a long period-2 sequence. We propose that Drt3b is a regenerating protein-substrate free-energy register. The framework specifies a sampled quantum mechanics/molecular mechanics (QM/MM) potential of mean force and a preregistered, within-domain first-order electronic-descriptor map for edge-specific activation barriers; physical curvature is admitted only through a training-frozen Taylor-remainder bound that widens uncertainty but never corrects a held-out mean. Conditional hazards derived from those barriers are averaged over unresolved conformational, hydration, protonation, and metal-coordination trajectories to yield a semi-Markov event kernel. Pointwise ratios of competing hazards are therefore fixed by the same barrier differences and preregistered prefactor rules that govern exit timing, so nucleotide choice and dwell statistics are co-generated rather than separately fitted. A regime gate separates stationary or slowly driven experiments, analyzed by a joint sequence-time cycle determinant, from genuinely nonstationary protocols, analyzed by the full age-structured tilted propagator. The decisive test is one locked parameterization: without condition-specific refitting, it must jointly predict nucleotide choice, waiting-time laws, product-length tails, and the rank, minors, null spaces, and rescue structure of perturbation responses. An independent 2026 DRT3 study supplies a stringent cross-construct transport test after sequence and structural alignment are frozen. The proposal requires neither coherent quantum computation nor coupling to an external field.

Enzymatic degradation of biopolymers underpins critical processes in biotechnological applications and natural processes, such as food digestion and cancer development, yet the interplay between enzyme propagation in a dense substrate and substrate degradation remains unresolved. It remains especially unclear whether degradation of a dense matrix facilitates or impedes enzyme propagation. We simultaneously track with spatiotemporal resolution unidirectional enzyme diffusion and biopolymer degradation in a model system. We demonstrate that enzyme diffusion is decoupled from catalytic activity, while the degradation front progression is dictated by enzyme diffusion, reaction kinetics and slow enzyme deactivation. These findings establish a quantitative framework to optimize enzymatic processes for applications in biomedicine, biomass valorization, and nanotechnology.

The vertex model is widely used to describe the mechanics of epithelial tissues, but its conventional formulation assumes that all cells remain tightly packed and always share edges with their neighbors, making it difficult to represent local detachment or gap formation. Here, we propose a minimal extension of the vertex model that enables cell detachment by introducing a new topological transformation, T2-inverse, which acts as the inverse of the classical T2 transition. When the imbalance of forces acting on a vertex, quantified by a tension metric $T$, exceeds a threshold, the T2-inverse splits the vertex into multiple vertices and creates a closed polygon that is incorporated as a pseudo-cell. This operation allows the model to represent the emergence and propagation of local detachment events. Using this framework, we simulate the stretching of a cell sheet and show that force-induced local detachments can accumulate to produce macroscopic tissue rupture. These results demonstrate that the proposed model extends the capability of the vertex model to describe tissue-level breakdown processes, including detachment and tearing, and provides a foundation for studying a broader class of epithelial mechanical phenomena.

Nematic order of confined cell populations plays an important role in determining cell alignment and stable configurations of topological defects, which are related to various biomechanical phenomena. Topological charges (or winding numbers) of topological defects strictly depend on the Euler characteristic of the confining domain, which has typically been non-negative in studies focused on domains without internal obstacles. However, biological tissues often surround two or more internal obstacles or holes, which inherently generate defects with negative charges. To understand the mechanical interaction between cellular tissue and obstacles, it is necessary to elucidate the geometrical effects of obstacles on cell alignment and defects with negative charges. Here, we investigate how cell populations achieve stable defect configurations of two -1/2 defects in a triply connected domain. First, we present experimental observations of C2C12 myoblasts confined by two circular obstacles of varying diameter, demonstrating that two $-1/2$ defects are the most frequent configuration when the obstacles are sufficiently large. Second, to theoretically validate these experimental observations, we perform systematic stability analyses of defect configurations using an explicit expression of cell alignment and numerical minimization of the Frank elastic energy. Our numerical calculations reveal that the most stable configuration shifts continuously from a horizontal, through off-axis, to a vertical configuration as the obstacle size increases. In addition, the experimentally observed defect positions agreed with these theoretical predictions to within 60 $\mu$m. These findings suggest that obstacle sizes control the symmetry of cell alignment, providing insights into how geometric and topological constraints can generate complex force patterns during morphogenesis or organ movements.

Biological molecular machines are mesoscopic systems that act repeatedly and periodically to perform important cellular tasks while contending with strong fluctuations and operating in an overdamped regime. Optimal control theory is a tool that can be used to understand the design principles behind efficient operation of these machines; however, most studies on optimal control of classical mesoscopic systems have focused on control problems that do not repeat periodically. Here, we automatically differentiate Fokker-Planck simulations to design efficient nonequilibrium control strategies for simple models of periodic molecular machines with and without explicit changes in the machine's chemical state. The designed protocols and theoretical analysis provide insight into the design principles governing efficient driving in these nonequilibrium systems. Designed control protocols should seek to reduce mechanical heat by rotating the entire angular probability distribution at a constant speed without changing its shape, and should reduce chemical heat by reducing the proportion of chemical transitions with large heat.

The structure of the genotype-phenotype map plays a central role in determining how populations move through fitness landscapes, yet less is known about how this structure interacts with environmental constraints and fitness trade-offs. Here, we study evolution on a tunable multilayer genotype-phenotype map in which binary genotypes are transformed through successive feed-forward layers before being evaluated by phenotype-level fitness functions. We explicitly model the phenotype to fitness transformation in order to compare an emergent trade-off fitness with a no-trade-off control. We explore different map architecture as models of more and less complex genotype-phenotype relationships by tuning the genotype length and number of layers. We find that ruggedness and navigability have a complex, architecture-dependent relationship. Landscapes with few local maxima can still have low navigability for wide and shallow maps, and navigability is high even at mean values of ruggedness for wide and deep maps. Trade-offs increase navigability in wide and shallow maps, while their effect is muted in other architectures. The effect of trade-offs on epistasis depends on evolutionary history. While trade-offs change which mutational neighborhoods are sampled by evolution, the background degree of epistatic interactions is set by map architecture. Finally, we use our framework to investigate micro-evolution of high-fitness populations and show that temporal covariance in allele-frequency changes can arise from the internal structure of a rugged genotype-phenotype map, producing signatures that resemble short-term alternating selection.

Ultrasound is increasingly used to control biological cells, but its multiple physical effects, including radiation force, streaming, heating, and cavitation, require theoretical frameworks that can quantify their relative strengths. Here we develop a semi-analytical model to predict flow patterns and energy distributions of ultrasound-induced intracellular streaming. Cells are modeled as viscoelastic droplets immersed in a fluid with different viscoelastic properties. For this geometry, the momentum equation with an Oldroyd-B constitutive law is solved using a perturbation expansion commonly applied in acoustofluidics. The resulting equations for the different orders of the expansion are solved using partial wave expansion and explicit integration. We investigate our model for a range of parameters which is relevant both for synthetic polymer and for protein solutions. We find a series of flow reversals that we can connect to the distribution of energy into the different modes. We also consider density and compressibility contrasts, which shift these transitions and introduce new ones. Finally, we discuss the potential biological relevance of our mathematical results for cellular sensing and signaling.

The finitely extensible nonlinear elastic (FENE) potential is widely used as a bonded interaction in coarse-grained simulations of polymers, soft matter, colloids, and biological systems. In the classical Kremer–Grest framework, FENE bonding is combined with a short-range Weeks–Chandler–Andersen (WCA) interaction to provide finite bond extensibility together with excluded-volume repulsion. Although this combination is highly successful, it intrinsically couples bonded elasticity to the nonbonded interaction, limiting the ability to independently control these two contributions. Here, we introduce a standalone FENE bond potential in HOOMD-blue in which finite bond extensibility is implemented independently of the choice of nonbonded interaction. This formulation allows the same FENE bond potential to be combined with WCA, Lennard–Jones, or other pair interactions without modifying the bonded interaction itself. We demonstrate the utility of the standalone formulation in coarse-grained polymer chains, colloidal networks, and mesh-based biological membrane models. Across these systems, Pure FENE bonding without short-range excluded-volume stabilization produces pronounced structural contraction, whereas the addition of WCA repulsion suppresses this collapse and preserves finite, spatially extended structures. These results demonstrate that separating finite bond extensibility from steric interactions provides independent control over local bond mechanics and collective structural organization. The standalone FENE formulation therefore provides a modular framework for coarse-grained simulations in which molecular connectivity and nonbonded interactions represent distinct physical mechanisms.

The extracellular matrix (ECM) is a fibrous and dynamic network that plays a critical role in development, homeostasis, and disease. Cells both respond to and remodel the ECM, engaging in a mechanical reciprocity that shapes tissues. To study these interactions, computational models have been developed that simulate either ECM mechanics or cell behavior. The Cellular Potts Model (CPM) is a flexible cell-based framework that has been extended to many biological processes, including coupling to a discrete deformable fiber network. So far, however, this extension has only been applied in two dimensions, even though a three-dimensional setting is biologically more relevant. Here, we present a 3D hybrid framework that couples the CPM with a discrete and deformable representation of the ECM. This model enables explicit simulation of cell-induced ECM remodeling, including fiber reorientation and matrix densification. By incorporating contractile forces through static adhesion points, the model captures how ECM elasticity and fiber stiffness influences cell shape. The simulations show that ECM stiffness, controlled by fiber crosslinking, resists contraction and reaches equilibrium, while crosslink density modulates fiber alignment and local matrix accumulation. This framework provides a versatile platform for studying cell-ECM mechanics and supports future studies of multicellular behavior in realistic 3D environments.