基于erwin frey发表的所有文献进行综述

A simple system for studying self-organization in biology comprises driven actin filaments, thought to interact primarily via binary collisions. Angle-resolved statistics suggest that the transition to polar order is driven by multi-filament events. From the self-organization of the cytoskeleton to the synchronous motion of bird flocks, living matter has the extraordinary ability to behave in a concerted manner1,2,3,4. The Boltzmann equation for self-propelled particles is frequently used in silico to link a system’s meso- or macroscopic behaviour to the microscopic dynamics of its constituents5,6,7,8,9,10. But so far such studies have relied on an assumption of simplified binary collisions owing to a lack of experimental data suggesting otherwise. We report here experimentally determined binary-collision statistics by studying a recently introduced molecular system, the high-density actomyosin motility assay11,12,13. We demonstrate that the alignment induced by binary collisions is too weak to account for the observed ordering transition. The transition density for polar pattern formation decreases quadratically with filament length, indicating that multi-filament collisions drive the observed ordering phenomenon and that a gas-like picture cannot explain the transition of the system to polar order. Our findings demonstrate that the unique properties of biological active-matter systems require a description that goes well beyond that developed in the framework of kinetic theories.

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Significance Propelled agents that align their orientations upon collisions can give rise to macroscopic order. Depending on the details of the agents’ collisions, this ordering process can lead to spatiotemporal patterns, including traveling polar waves and nematic lanes. Recent experiments suggest that such patterns can also coexist and dynamically interconvert. Here we propose a general mechanism for such a phenomenology, which is based on patterned-induced local symmetry breaking. We show that macroscopic order in active-matter systems is linked to spatiotemporal density patterns, which in turn can lead to local symmetry breaking if the agent density exceeds the relevant threshold values. Our study sheds light on the principles of pattern formation and the emergence of symmetry in biological active-matter systems. The emergence of macroscopic order and patterns is a central paradigm in systems of (self-)propelled agents and a key component in the structuring of many biological systems. The relationships between the ordering process and the underlying microscopic interactions have been extensively explored both experimentally and theoretically. While emerging patterns often show one specific symmetry (e.g., nematic lane patterns or polarized traveling flocks), depending on the symmetry of the alignment interactions patterns with different symmetries can apparently coexist. Indeed, recent experiments with an actomysin motility assay suggest that polar and nematic patterns of actin filaments can interact and dynamically transform into each other. However, theoretical understanding of the mechanism responsible remains elusive. Here, we present a kinetic approach complemented by a hydrodynamic theory for agents with mixed alignment symmetries, which captures the experimentally observed phenomenology and provides a theoretical explanation for the coexistence and interaction of patterns with different symmetries. We show that local, pattern-induced symmetry breaking can account for dynamically coexisting patterns with different symmetries. Specifically, in a regime with moderate densities and a weak polar bias in the alignment interaction, nematic bands show a local symmetry-breaking instability within their high-density core region, which induces the formation of polar waves along the bands. These instabilities eventually result in a self-organized system of nematic bands and polar waves that dynamically transform into each other. Our study reveals a mutual feedback mechanism between pattern formation and local symmetry breaking in active matter that has interesting consequences for structure formation in biological systems.

Significance Biological processes operate in a spatially and temporally ordered manner to reliably fulfill their function. This is achieved by pattern formation, which generally involves many different spatial and temporal scales. The resulting multiscale patterns exhibit complex dynamics for which it is difficult to find a simplified description at large scales while preserving information about the patterns at small scales. Here, we introduce an approach for mass-conserving reaction–diffusion systems that is based on a linear theory and therefore conceptually simple to apply. We investigate multiscale patterns of the Min protein system and show that our approach enables us to explain and predict the intricate dynamics from the large-scale mass redistribution of the total protein densities.

The formation of protein patterns inside cells is generically described by reaction-diffusion models. The study of such systems goes back to Turing, who showed how patterns can emerge from a homogenous steady state when two reactive components have different diffusivities (e.g., membrane-bound and cytosolic states). However, in nature, systems typically develop in a heterogeneous environment, where upstream protein patterns affect the formation of protein patterns downstream. Examples for this are the polarization of Cdc42 adjacent to the previous bud site in budding yeast and the formation of an actin-recruiter ring that forms around a PIP3 domain in macropinocytosis. This suggests that previously established protein patterns can serve as a template for downstream proteins and that these downstream proteins can "sense" the edge of the template. A mechanism for how this edge sensing may work remains elusive. Here we demonstrate and analyze a generic and robust edge-sensing mechanism, based on a two-component mass-conserving reaction-diffusion (McRD) model. Our analysis is rooted in a recently developed theoretical framework for McRD systems, termed local equilibria theory. We extend this framework to capture the spatially heterogeneous reaction kinetics due to the template. This enables us to graphically construct the stationary patterns in the phase space of the reaction kinetics. Furthermore, we show that the protein template can trigger a regional mass-redistribution instability near the template edge, leading to the accumulation of protein mass, which eventually results in a stationary peak at the template edge. We show that simple geometric criteria on the reactive nullcline's shape predict when this edge-sensing mechanism is operational. Thus, our results provide guidance for future studies of biological systems and for the design of synthetic pattern forming systems.

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Intracellular protein patterns regulate many vital cellular functions, such as the processing of spatiotemporal information or the control of shape deformations. To do so, pattern-forming systems can be sensitive to the cell geometry by means of coupling the protein dynamics on the cell membrane to dynamics in the cytosol. Recent studies demonstrated that modeling the cytosolic dynamics in terms of an averaged protein pool disregards possibly crucial aspects of the pattern formation, most importantly concentration gradients normal to the membrane. At the same time, the coupling of two domains (surface and volume) with different dimensions renders many standard tools for the numerical analysis of self-organizing systems inefficient. Here, we present a generic framework for projecting the cytosolic dynamics onto the lower-dimensional surface that respects the influence of cytosolic concentration gradients in static and evolving geometries. This method uses a priori physical information about the system to approximate the cytosolic dynamics by a small number of dominant characteristic concentration profiles (basis), akin to basis transformations of finite element methods. As a proof of concept, we apply our framework to a toy model for volume-dependent interrupted coarsening, evaluate the accuracy of the results for various basis choices, and discuss the optimal basis choice for biologically relevant systems. Our analysis presents an efficient yet accurate method for analyzing pattern formation with surface-volume coupling in evolving geometries.

Recent in vitro experiments with FtsZ polymers show self-organization into different dynamic patterns, including structures reminiscent of the bacterial Z ring. We model FtsZ polymers as active particles moving along chiral, circular paths by Brownian dynamics simulations and a Boltzmann approach. Our two conceptually different methods point to a generic phase behavior. At intermediate particle densities, we find self-organization into vortex structures including closed rings. Moreover, we show that the dynamics at the onset of pattern formation is described by a generalized complex Ginzburg-Landau equation.

Wavelength selection in reaction-diffusion systems can be understood as a coarsening process that is interrupted by counteracting processes at certain wavelengths. We first show that coarsening in mass-conserving systems is driven by self-amplifying mass transport between neighboring high-density domains. We derive a general coarsening criterion and show that coarsening is generically uninterrupted in two-component systems that conserve mass. The theory is then generalized to study interrupted coarsening and anticoarsening due to weakly broken mass conservation, providing a general path to analyze wavelength selection in pattern formation far from equilibrium.

Much like passive materials, active systems can be affected by the presence of imperfections in their microscopic order, called defects, that influence macroscopic properties. This suggests the possibility to steer collective patterns by introducing and controlling defects in an active system. Here we show that a self-assembled, passive nematic is ideally suited to control the pattern formation process of an active fluid. To this end, we force microtubules to glide inside a passive nematic material made from actin filaments. The actin nematic features self-assembled half-integer defects that steer the active microtubules and lead to the formation of macroscopic polar patterns. Moreover, by confining the nematic in circular geometries, chiral loops form. We find that the exact positioning of nematic defects in the passive material deterministically controls the formation and the polarity of the active flow, opening the possibility of efficiently shaping an active material using passive defects. Defects of a passive nematic liquid crystal made from actin filaments pattern the collective behaviour of active microtubules, creating macroscopic polar patterns and chiral loops.

Meindlhumer et al. report a combined theoretical/experimental study of how the propagation direction of Min protein patterns can be altered by a bulk flow of solution. The Min proteins constitute the best-studied model system for pattern formation in cell biology. We theoretically predict and experimentally show that the propagation direction of in vitro Min protein patterns can be controlled by a hydrodynamic flow of the bulk solution. We find downstream propagation of Min wave patterns for low MinE:MinD concentration ratios, upstream propagation for large ratios, but multistability of both propagation directions in between. Whereas downstream propagation can be described by a minimal model that disregards MinE conformational switching, upstream propagation can be reproduced by a reduced switch model, where increased MinD bulk concentrations on the upstream side promote protein attachment. Our study demonstrates that a differential flow, where bulk flow advects protein concentrations in the bulk, but not on the surface, can control surface-pattern propagation. This suggests that flow can be used to probe molecular features and to constrain mathematical models for pattern-forming systems.

Even simple active systems can show a plethora of intriguing phenomena and often we find complexity where we would have expected simplicity. One striking example is the occurrence of a quiescent or absorbing state with frozen fluctuations that at first sight seems to be impossible for active matter driven by the incessant input of energy. While such states were reported for externally driven systems through macroscopic shear or agitation, the investigation of frozen active states in inherently active systems like cytoskeletal suspensions or active gels is still at large. Using high-density motility assay experiments, we demonstrate that frozen steady states can arise in active systems if active transport is coupled to growth processes. The experiments are complemented by agent-based simulations which identify the coupling between self-organization, growth, and mechanical properties to be responsible for the pattern formation process.

Some of the key proteins essential for important cellular processes are capable of recruiting other proteins from the cytosol to phospholipid membranes. The physical basis for this cooperativity of binding is, surprisingly, still unclear. Here, we suggest a general feedback mechanism that explains cooperativity through mechanochemical coupling mediated by the mechanical properties of phospholipid membranes. Our theory predicts that protein recruitment, and therefore also protein pattern formation, involves membrane deformation and is strongly affected by membrane composition.

Important cellular processes, such as cell motility and cell division, are coordinated by cell polarity, which is determined by the non-uniform distribution of certain proteins. Such protein patterns form via an interplay of protein reactions and protein transport. Since Turing’s seminal work, the formation of protein patterns resulting from the interplay between reactions and diffusive transport has been widely studied. Over the last few years, increasing evidence shows that also advective transport, resulting from cytosolic and cortical flows, is present in many cells. However, it remains unclear how and whether these flows contribute to protein-pattern formation. To address this question, we use a minimal model that conserves the total protein mass to characterize the effects of cytosolic flow on pattern formation. Combining a linear stability analysis with numerical simulations, we find that membrane-bound protein patterns propagate against the direction of cytoplasmic flow with a speed that is maximal for intermediate flow speed. We show that the mechanism underlying this pattern propagation relies on a higher protein influx on the upstream side of the pattern compared to the downstream side. Furthermore, we find that cytosolic flow can change the membrane pattern qualitatively from a peak pattern to a mesa pattern. Finally, our study shows that a non-uniform flow profile can induce pattern formation by triggering a regional lateral instability.

We investigate a three-component system involving the Belousov–Zhabotinsky reaction in water-in-oil microemulsions. Our goal is to investigate the connection between homoclinic snaking and semi-strength interaction in a three-variable reaction–diffusion system. A two-parameter bifurcation diagram of homogeneous, periodic and localized patterns is obtained numerically, and a natural asymptotic scaling for semi-strong interaction theory is found where an activator source term a=O(δ1) and b=O(δ1), with δ1≪1 being the diffusion ratio. Under this regime, singular perturbation techniques are used to construct localized steady-state patterns, and new types of non-local eigenvalue problems (NLEP) are derived to determine the stability of these patterns to O(1) time-scale instabilities. We extend the scope of the NLEP by considering a general scenario where both time-scaling parameters are non-zero. All analytical results are found to agree with numerics. Further numerical results are presented on the location of various types of breathing Hopf instability for localized patterns.

Significance Flows of particulate suspensions are ubiquitous in advanced technological applications, including coating of colloidal inks and paints, manufacturing of pharmaceuticals, and oil production. When particles aggregate due to attractive forces, flow can induce giant anisotropic concentration fluctuations. Surprisingly, shear flow between parallel plates organizes these fluctuations into periodically spaced, particle-rich stripes that are aligned perpendicularly to the flow direction. We use experiments and complementary simulations to build a universal stability criterion, demonstrating that hydrodynamic interactions alone drive this process of pattern formation independent of particle size, shape, and chemical composition. Such flow-induced patterning has potential applications in the production of a broad range of anisotropic structures for use in technologies such as flexible electronics and nanocomposites, 3D printing, and flow batteries. Dilute suspensions of repulsive particles exhibit a Newtonian response to flow that can be accurately predicted by the particle volume fraction and the viscosity of the suspending fluid. However, such a description fails when the particles are weakly attractive. In a simple shear flow, suspensions of attractive particles exhibit complex, anisotropic microstructures and flow instabilities that are poorly understood and plague industrial processes. One such phenomenon, the formation of log-rolling flocs, which is ubiquitously observed in suspensions of attractive particles that are sheared while confined between parallel plates, is an exemplar of this phenomenology. Combining experiments and discrete element simulations, we demonstrate that this shear-induced structuring is driven by hydrodynamic coupling between the flocs and the confining boundaries. Clusters of particles trigger the formation of viscous eddies that are spaced periodically and whose centers act as stable regions where particles aggregate to form flocs spanning the vorticity direction. Simulation results for the wavelength of the periodic pattern of stripes formed by the logs and for the log diameter are in quantitative agreement with experimental observations on both colloidal and noncolloidal suspensions. Numerical and experimental results are successfully combined by means of rescaling in terms of a Mason number that describes the strength of the shear flow relative to the rupture force between contacting particles in the flocs. The introduction of this dimensionless group leads to a universal stability diagram for the log-rolling structures and allows for application of shear-induced structuring as a tool for assembling and patterning suspensions of attractive particles.

We study the behavior of interacting self-propelled particles, whose self-propulsion speed decreases with their local density. By combining direct simulations of the microscopic model with an analysis of the hydrodynamic equations obtained by explicitly coarse graining the model, we show that interactions lead generically to the formation of a host of patterns, including moving clumps, active lanes, and asters. This general mechanism could explain many of the patterns seen in recent experiments and simulations.

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This paper studies the fractional reaction-diffusion Brusselator model, which incorporates fractional-time derivatives to describe memory effects and anomalous diffusion in pattern formation. A fully discrete numerical scheme is developed using an L1 approximation for the fractional derivative and a finite difference method for spatial discretization. Theoretical analysis proves the uniqueness, asymptotic stability, and convergence of the scheme. Numerical simulations demonstrate the emergence of stationary Turing patterns under appropriate conditions, validating the model’s ability to capture complex spatiotemporal dynamics. The work provides a reliable computational framework for exploring fractional reaction-diffusion systems in two dimensions.

The self-organization of proteins into enriched compartments and the formation of complex patterns are crucial processes for life on the cellular level. Liquid-liquid phase separation is one mechanism for forming such enriched compartments. When phase-separating proteins are membrane-bound and locally disturb it, the mechanical response of the membrane mediates interactions between these proteins. How these membrane-mediated interactions influence the steady state of the protein density distribution is thus an important question to investigate in order to understand the rich diversity of protein and membrane-shape patterns present at the cellular level. This work starts with a widely used model for membrane-bound phase-separating proteins. We numerically solve our system to map out its phase space and perform a careful, systematic expansion of the model equations to characterize the phase transitions through linear stability analysis and free energy arguments. We observe that the membrane-mediated interactions, due to their long-range nature, are capable of qualitatively altering the equilibrium state of the proteins. This leads to arrested coarsening and length-scale selection instead of simple demixing and complete coarsening. In this study, we unambiguously show that long-range membrane-mediated interactions lead to pattern formation in a system that otherwise would not do so. This work provides a basis for further systematic study of membrane-bound pattern-forming systems.

Active matter systems evade the constraints of thermal equilibrium, leading to the emergence of intriguing collective behavior. A paradigmatic example is given by motor-filament mixtures, where the motion of motor proteins drives alignment and sliding interactions between filaments and their self-organization into macroscopic structures. After defining a microscopic model for these systems, we derive continuum equations, exhibiting the formation of active supramolecular assemblies such as micelles, bilayers, and foams. The transition between these structures is driven by a branching instability, which destabilizes the orientational order within the micelles, leading to the growth of bilayers at high microtubule densities. Additionally, we identify a fingering instability, modulating the shape of the micelle interface at high motor densities. We study the role of various mechanisms in these two instabilities, such as contractility, active splay, and anchoring, allowing for generalization beyond the system considered here. Published by the American Physical Society 2024

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Understanding the spatial organization of membrane proteins is crucial for unraveling key principles in cell biology. The reaction-diffusion model is commonly used to understand biochemical patterning; however, applying reaction-diffusion models to subcellular phenomena is challenging because of the difficulty in measuring protein diffusivity and interaction kinetics in the living cell. In this work, we investigated the self-organization of the plasmalemma vesicle-associated protein (PLVAP), which creates regular arrangements of fenestrated ultrastructures, using single-molecule tracking. We demonstrated that the spatial organization of the ultrastructures is associated with a decrease in the association rate by actin destabilization. We also constructed a reaction-diffusion model that accurately generates a hexagonal array with the same 130 nm spacing as the actual scale and informs the stoichiometry of the ultrastructure, which can be discerned only through electron microscopy. Through this study, we integrated single-molecule experiments and reaction-diffusion modeling to surpass the limitations of static imaging tools and proposed emergent properties of the PLVAP ultrastructure.

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In the rod-shaped bacterium Escherichia coli, the center is selected by the Min-proteins as the site of cell division. To this end, the proteins periodically translocate between the two cell poles, where they suppress assembly of the cell division machinery. Ample evidence notably obtained from in vitro reconstitution experiments suggests that the oscillatory pattern results from self-organization of the proteins MinD and MinE in presence of a membrane. A mechanism built on cooperative membrane attachment of MinD and persistent MinD removal from the membrane induced by MinE has been shown to be able to reproduce the observed Min-protein patterns in rod-shaped E. coli and on flat supported lipid bilayers. Here, we report our results of a numerical investigation of patterns generated by this mechanism in various geoemtries. Notably, we consider the dynamics on membrane patches of different forms, on topographically structured lipid bilayers, and in closed geometries of various shapes. We find that all previously described patterns can be reproduced by the mechanism. However, it requires different parameter sets for reproducing the patterns in closed and in open geometries.

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Self-organization of proteins in space and time is of crucial importance for the functioning of cellular processes. Often, this organization takes place in the presence of strong random fluctuations due to the small number of molecules involved. We report on stochastic switching of the Min-protein distributions between the two cell halves in short Escherichia coli cells. A computational model provides strong evidence that the macroscopic switching is rooted in microscopic noise on the molecular scale. In longer bacteria, the switching turns into regular oscillations that are required for positioning of the division plane. As the pattern becomes more regular, cell-to-cell variability also lessens, indicating cell length-dependent regulation of Min-protein activity.

We use the oscillating Min proteins of Escherichia coli as a prototype system to illustrate the current state and potential of modeling protein dynamics in space and time. We demonstrate how a theoretical approach has led to striking new insights into the mechanisms of self-organization in bacterial cells and indicate how these ideas may be applicable to more complex structure formation in eukaryotic cells.

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Reaction-diffusion (RD) waves, which are dynamic self-organization structures generated by nanosize molecules, are a fundamental mechanism from patterning in nano- and micromaterials to spatiotemporal regulations in living cells, such as cell division and motility. Although the periods of RD waves are the critical element for these functions, the development of a system to control their period is challenging because RD waves result from nonlinear physical dynamics under far-from-equilibrium conditions. Here, we developed an artificial cell system with tunable period of an RD-driven wave (Min protein wave), which determines a cell division site plane in living bacterial cells. The developed system is based on our finding that Min waves are generated by energy consumption of either ATP or dATP, and the period of the wave is different between these two energy suppliers. We showed that the Min-wave period was modulated linearly by the mixing ratio of ATP and dATP and that it was also possible to estimate the mixing ratio of ATP and dATP from the period. Our findings illuminated a previously unidentified principle to control the dissipative dynamics of biomolecules and, simultaneously, built an important framework to construct molecular robots with spatiotemporal units.

Significance To gain insight into cellular metabolism, it is important to understand how proteins travel and self-organize within the cell. A characteristic feature of many important proteins is that they are oligomeric complexes, that is, they are composed of a few smaller subunits that can reversibly associate and dissociate. Here, we study the impact of dissociation on protein transport and self-organization. We find that dissociation can help proteins find and react with a target more rapidly, and that dissociating proteins spontaneously accumulate in regions in which they are most stable. Many functional units in biology, such as enzymes or molecular motors, are composed of several subunits that can reversibly assemble and disassemble. This includes oligomeric proteins composed of several smaller monomers, as well as protein complexes assembled from a few proteins. By studying the generic spatial transport properties of such proteins, we investigate here whether their ability to reversibly associate and dissociate may confer on them a functional advantage with respect to nondissociating proteins. In uniform environments with position-independent association–dissociation, we find that enhanced diffusion in the monomeric state coupled to reassociation into the functional oligomeric form leads to enhanced reactivity with localized targets. In nonuniform environments with position-dependent association–dissociation, caused by, for example, spatial gradients of an inhibiting chemical, we find that dissociating proteins generically tend to accumulate in regions where they are most stable, a process that we term “stabilitaxis.”

Amyloid fibrils play a crucial role in many human diseases and are found to function in a range of physiological processes from bacteria to human. They have also been gaining importance in nanotechnology applications. Understanding the mechanisms behind amyloid formation can help develop strategies towards the prevention of fibrillation processes or create new technological applications. It is thus essential to observe the structures of amyloids and their self-assembly processes at the nanometer-scale resolution under physiological conditions. In this work, we used highly force-sensitive frequency-modulation atomic force microscopy (FM-AFM) to characterize the fibril structures formed by the N-terminal domain of a bacterial division protein MinE in solution. The approach enables us to investigate the fibril morphology and protofibril organization over time progression and in response to changes in ionic strength, molecular crowding, and upon association with different substrate surfaces. In addition to comparison of the fibril structure and behavior of MinE1-31 under varying conditions, the study also broadens our understanding of the versatile behavior of amyloid-substrate surface interactions.

The dynamics of protein self-assembly on the inorganic surface and the resultant geometric patterns are visualized using high-speed atomic force microscopy. The time dynamics of the classical macroscopic descriptors such as 2D fast Fourier transforms, correlation, and pair distribution functions are explored using the unsupervised linear unmixing, demonstrating the presence of static ordered and dynamic disordered phases and establishing their time dynamics. The deep learning (DL)-based workflow is developed to analyze detailed particle dynamics and explore the evolution of local geometries. Finally, we use a combination of DL feature extraction and mixture modeling to define particle neighborhoods free of physics constraints, allowing for a separation of possible classes of particle behavior and identification of the associated transitions. Overall, this work establishes the workflow for the analysis of the self-organization processes in complex systems from observational data and provides insight into the fundamental mechanisms.

In many physiological situations, BAR proteins reshape membranes with pre-existing curvature (templates), contributing to essential cellular processes. However, the mechanism and the biological implications of this reshaping process remain unclear. Here we show, both experimentally and through modelling, that BAR proteins reshape low curvature membrane templates through a mechanochemical phase transition. This phenomenon depends on initial template shape and involves the co-existence and progressive transition between distinct local states in terms of molecular organization (protein arrangement and density) and membrane shape (template size and spherical versus cylindrical curvature). Further, we demonstrate in cells that this phenomenon enables a mechanotransduction mode, in which cellular stretch leads to the mechanical formation of membrane templates, which are then reshaped into tubules by BAR proteins. Our results demonstrate the interplay between membrane mechanics and BAR protein molecular organization, integrating curvature sensing and generation in a comprehensive framework with implications for cell mechanical responses. Amphiphysin BAR proteins reshape membranes, but the dynamics of the process remained unexplored. Here, the authors show through experiment and modelling that reshaping depends on the initial template shape, occurs even at low initial curvature, and involves the coexistence of isotropic and nematic states.

Living systems employ a common set of building blocks that can self-organize into a multitude of different structures. Now, a model system based on programmed non-reciprocal interactions, which generically emerge in non-equilibrium systems with chemical activity, exhibits self-assembly pathways that feature temporal structures in the form of cyclic motifs. A hallmark of living systems is the ability to employ a common set of building blocks that can self-organize into a multitude of different structures. This capability can only be afforded in non-equilibrium conditions, as evident from the energy-consuming nature of the plethora of such dynamical processes. To achieve automated dynamical control of such self-assembled structures and transitions between them, we need to identify the fundamental aspects of non-equilibrium dynamics that can enable such processes. Here we identify programmable non-reciprocal interactions as a tool to achieve such functionalities. The design rule is composed of reciprocal interactions that lead to the equilibrium assembly of the different structures, through a process denoted as multifarious self-assembly, and non-reciprocal interactions that give rise to non-equilibrium dynamical transitions between the structures. The design of such self-organized shape-shifting structures can be implemented at different scales, from nucleic acids and peptides to proteins and colloids.

Self-assembly and self-organization are mechanisms by which ordered structures are formed spontaneously in nature. Traditionally, these phenomena have been distinguished thermodynamically: self-assembly occurs via free energy minimization toward equilibrium, whereas self-organization occurs in open systems maintained far from equilibrium via continuous energy dissipation. Despite this contrast, both processes involve dynamic pathways governed by entropy production during structure formation. Recent findings have shown that the entropy production rate is important in determining the selection rule for the resultant structure. Herein, we first summarize the differences and similarities between self-assembly and self-organization, along with representative examples, from micelles and crystals to convective flows and chemical oscillations. Then, we focus on the entropy production rate as a principle governing structure selection during non-equilibrium processes in both self-assembly and self-organization regimes. Our recent experimental findings reveal how flux conditions influence structure selection in reaction–diffusion systems (Liesegang phenomenon) and protein self-assembly. This perspective suggests that nature, including biological systems, may selectively harness self-assembly or self-organization depending on the interplay between energy flux and the kinetics of the involved reactions. These insights highlight the potential of an entropy-based analysis to enhance our understanding of complex pattern formation and guide the rational design of self-assembly and self-organization.

Microtubules self-organize to form part of the cellular cytoskeleton. They give cells their shape and play a crucial role in cell division and intracellular transport. Strikingly, microtubules driven by motor proteins reorganize into stable mitotic/meiotic spindles with high spatial and temporal precision during successive cell division cycles. Although the topic has been extensively studied, the question remains: What defines such microtubule networks' spatial order and robustness? Here, we aim to approach this problem by analyzing a simplified computational model of radial microtubule self-organization driven by a single type of motor protein -- dyneins. We establish that the spatial order of the steady-state pattern is likely associated with the dynein-driven microtubule motility. At the same time, the structure of the microtubule network is likely linked to its connectivity at the beginning of self-organization. Using the continuous variation of dynein concentration, we reveal hysteresis in microtubule self-organization, ensuring the stability of radial filament structures.

Self-organisation of Min proteins is responsible for the spatial control of cell division in Escherichia coli, and has been studied both in vivo and in vitro. Intriguingly, the protein patterns observed in these settings differ qualitatively and quantitatively. This puzzling dichotomy has not been resolved to date. Using reconstituted proteins in laterally wide microchambers with a well-controlled height, we experimentally show that the Min protein dynamics on the membrane crucially depend on the micro chamber height due to bulk concentration gradients orthogonal to the membrane. A theoretical analysis shows that in vitro patterns at low microchamber height are driven by the same lateral oscillation mode as pole-to-pole oscillations in vivo. At larger microchamber height, additional vertical oscillation modes set in, marking the transition to a qualitatively different in vitro regime. Our work reveals the qualitatively different mechanisms of mass transport that govern Min protein-patterns for different bulk heights and thus shows that Min patterns in cells are governed by a different mechanism than those in vitro. Self-organisation of Min protein patterns observed in vivo and in vitro differ qualitatively and quantitatively. Here the authors reconstituted Min proteins in laterally wide microchambers with a well-controlled height and show that the Min protein dynamics on the membrane crucially depend on the micro chamber height.

Force generation due to actin assembly is a fundamental aspect of membrane sculpting for many essential processes. In this work, we use a multiscale computational model constrained by experimental measurements to show that a minimal branched actin network is sufficient to internalize endocytic pits against physiological membrane tension. A parameter sweep identified the number of Arp2/3 complexes as particularly important for robust internalization, which prompted the development of a molecule-counting method in live mammalian cells. Using this method, we found that ~200 Arp2/3 complexes assemble at sites of clathrin-mediated endocytosis in human cells. Our simulations also revealed that actin networks self-organize in a radial branched array with barbed filament ends oriented to grow toward the base of the pit, and that the distribution of linker proteins around the endocytic pit is critical for this organization. Surprisingly, our model predicted that long actin filaments bend from their attachment sites in the coat to the base of the pit and store elastic energy that can be harnessed to drive endocytosis. This prediction was validated using cryo-electron tomography on cells, which revealed the presence of bent actin filaments along the endocytic site. Furthermore, we predict that under elevated membrane tension, the self-organized actin network directs more growing filaments toward the base of the pit, increasing actin nucleation and bending for increased force production. Thus, our study reveals that spatially constrained actin filament assembly utilizes an adaptive mechanism that enables endocytosis under varying physical constraints.

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The generation of two non-identical membrane compartments via exchange of vesicles is considered to require two types of vesicles specified by distinct cytosolic coats that selectively recruit cargo, and two membrane-bound SNARE pairs that specify fusion and differ in their affinities for each type of vesicles. The mammalian Golgi complex is composed of 6–8 non-identical cisternae that undergo gradual maturation and replacement yet features only two SNARE pairs. We present a model that explains how distinct composition of Golgi cisternae can be generated with two and even a single SNARE pair and one vesicle coat. A decay of active SNARE concentration in aging cisternae provides the seed for a cis trans SNARE gradient that generates the predominantly retrograde vesicle flux which further enhances the gradient. This flux in turn yields the observed inhomogeneous steady-state distribution of Golgi enzymes, which compete with each other and with the SNAREs for incorporation into transport vesicles. We show analytically that the steady state SNARE concentration decays exponentially with the cisterna number. Numerical solutions of rate equations reproduce the experimentally observed SNARE gradients, overlapping enzyme peaks in cis, medial and trans and the reported change in vesicle nature across the Golgi: Vesicles originating from younger cisternae mostly contain Golgi enzymes and SNAREs enriched in these cisternae and extensively recycle through the Endoplasmic Reticulum (ER), while the other subpopulation of vesicles contains Golgi proteins prevalent in older cisternae and hardly reaches the ER.

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Turing patterns are stationary, wave-like structures that emerge from the nonequilibrium assembly of reactive and diffusive components. While they are foundational in biophysics, their classical formulation relies on a single characteristic length scale that balances reaction and diffusion, making them overly simplistic for describing biological patterns, which often exhibit multi-scale structures, grain-like textures, and inherent imperfections. Here, we integrate diffusiophoretically-assisted assembly of finite-sized cells, driven by a background chemical gradient in a Turing pattern, while also incorporating intercellular interactions. This framework introduces key control parameters, such as the P\'{e}clet number, cell size distribution, and intercellular interactions, enabling us to reproduce strikingly similar structural features observed in natural patterns. We report imperfections, including spatial variations in pattern thickness, packing limits, and pattern breakups. Our model not only deepens our understanding but also opens a new line of inquiry into imperfect Turing patterns that deviate from the classical formulation in significant ways.

Spontaneous pattern formation is a fundamental scientific problem that has received much attention since the seminal theoretical work of Turing on reaction-diffusion systems. In molecular biophysics, this phenomenon often takes place under the influence of large fluctuations. It is then natural to inquire about the precision of such pattern. In particular, spontaneous pattern formation is a nonequilibrium phenomenon, and the relation between the precision of a pattern and the thermodynamic cost associated with it remains largely unexplored. Here, we analyze this relation with a paradigmatic stochastic reaction-diffusion model, i.e., the Brusselator in one spatial dimension. We find that the precision of the pattern is maximized for an intermediate thermodynamic cost, i.e., increasing the thermodynamic cost beyond this value makes the pattern less precise. Even though fluctuations get less pronounced with an increase in thermodynamic cost, we argue that larger fluctuations can also have a positive effect on the precision of the pattern.

We develop a framework describing the dynamics and thermodynamics of open non-ideal reaction-diffusion systems, which embodies Flory-Huggins theories of mixtures and chemical reaction network theories. Our theory elucidates the mechanisms underpinning the emergence of self-organized dissipative structures in these systems. It evaluates the dissipation needed to sustain and control them, discriminating the contributions from each reaction and diffusion process with spatial resolution. It also reveals the role of the reaction network in powering and shaping these structures. We identify particular classes of networks in which diffusion processes always equilibrate within the structures, while dissipation occurs solely due to chemical reactions. The spatial configurations resulting from these processes can be derived by minimizing a kinetic potential, contrasting with the minimization of the thermodynamic free energy in passive systems. This framework opens the way to investigating the energetic cost of phenomena, such as liquid-liquid phase separation, coacervation, and the formation of biomolecular condensates.

Understanding of the effect of coupling interaction is at the heart of nonlinear science since some nonequilibrium systems are composed of different layers or units. In this paper, we demonstrate various spatio-temporal patterns in a nonlinearly coupled two-layer Turing system with nonidentical reaction kinetics. Both the type of Turing mode and coupling form play an important role in the pattern formation and pattern selection. Two kinds of Turing mode interactions, namely supercritical-subcritical and supercritical-supercritical Turing mode interaction, have been investigated. Stationary resonant superlattice patterns arise spontaneously in both cases, while dynamic patterns can also be formed in the latter case. The destabilization of spike solutions induced by spatial heterogeneity may be responsible for these dynamic patterns. In contrast to linear coupling, the nonlinear coupling not only increases the complexity of spatio-temporal patterns, but also reduces the requirements of spatial resonance conditions. The simulation results are in good agreement with the experimental observations in dielectric barrier discharge systems.

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We establish universal relations between pattern formation and dissipation with a geometric approach to nonequilibrium thermodynamics of deterministic reaction-diffusion systems. We first provide a way to systematically decompose the entropy production rate based on the orthogonality of thermodynamic forces, thereby identifying the amount of dissipation caused by each factor. This enables us to extract the excess entropy production rate that genuinely contributes to the time evolution of patterns. We also show that a similar geometric method further decomposes the entropy production rate into detailed contributions, e.g., the dissipation from each point in real or wavenumber space. Second, we relate the excess entropy production rate to the details of the change in patterns through two types of thermodynamic trade-off relations for reaction-diffusion systems: thermodynamic speed limits and thermodynamic uncertainty relations. The former relates dissipation and the speed of pattern formation, and the latter bounds the excess entropy production rate with partial information on patterns, such as specific Fourier components of concentration distributions. In connection with the derivation of the thermodynamic speed limits, we also extend optimal transport theory to reaction-diffusion systems, which enables us to measure the speed of the time evolution. This extension of optimal transport also solves the minimization problem of the dissipation associated with the transition between two patterns, and constructs energetically efficient protocols for pattern formation. We numerically demonstrate our results using chemical traveling waves in the Fisher–Kolmogorov–Petrovsky–Piskunov equation and changes in symmetry in the Brusselator model. Our results apply to general reaction-diffusion systems and contribute to understanding the relations between pattern formation and unavoidable dissipation. Published by the American Physical Society 2025

Couplings between biochemical and mechanical processes have a profound impact on embryonic development. However, studies capable of quantifying these interactions have remained elusive. Here, we investigate a synthetic system where a DNA reaction-diffusion (RD) front is advected by a turbulent flow generated by active matter (AM) flows in a quasi-one-dimensional geometry. Whereas the dynamics of simple RD fronts solely depend on the reaction and diffusion rates, we show that RD-AM front propagation is also influenced by the confinement geometry. We first experimentally dissected the different components of the reaction-diffusion-advection process by knocking out reaction or advection and observe how RD-AM allows for faster transport over large distances, avoiding dilution. We then show how confinement impacts active matter flow: While changes in instantaneous flow velocities are small, correlation times are dramatically increased with decreasing confinement. As a result, RD-AM front speed increases up to eightfold compared to an RD one, in quantitative agreement with a conveyor-belt reaction-diffusion-advection theoretical model. The RD-AM experimental system described here provides a framework for the rational engineering of complex spatiotemporal processes observed in living systems. It will reinforce our understanding of how macro-scale patterns and structures emerge from microscopic components in nonequilibrium systems. Published by the American Physical Society 2025

We extend the energetic variational approach so it can be applied to a chemical reaction system with general mass action kinetics. Our approach starts with an energy-dissipation law. We show that the chemical equilibrium is determined by the choice of the free energy and the dynamics of the chemical reaction is determined by the choice of the dissipation. This approach enables us to couple chemical reactions with other effects, such as diffusion and drift in an electric field. As an illustration, we apply our approach to a nonequilibrium reaction-diffusion system in a specific but canonical setup. We show by numerical simulations that the input-output relation of such a system depends on the choice of the dissipation.

Shapes of biological membranes are dynamically regulated in living cells. Although membrane shape deformation by proteins at thermal equilibrium has been extensively studied, nonequilibrium dynamics have been much less explored. Recently, chemical reaction propagation has been experimentally observed in plasma membranes. Thus, it is important to understand how the reaction–diffusion dynamics are modified on deformable curved membranes. Here, we investigated nonequilibrium pattern formation on vesicles induced by mechanochemical feedback between membrane deformation and chemical reactions, using dynamically triangulated membrane simulations combined with the Brusselator model. We found that membrane deformation changes stable patterns relative to those that occur on a non-deformable curved surface, as determined by linear stability analysis. We further found that budding and multi-spindle shapes are induced by Turing patterns, and we also observed the transition from oscillation patterns to stable spot patterns. Our results demonstrate the importance of mechanochemical feedback in pattern formation on deforming membranes.

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We show both experimentally and numerically that the time scales separation introduced by long range activation can induce oscillations and excitability in nonequilibrium reaction-diffusion systems that would otherwise only exhibit bistability. Namely, we show that in the chlorite-tetrathionate reaction, where the autocatalytic species H+ diffuses faster than the substrates, the spatial bistability domain in the nonequilibrium phase diagram is extended with oscillatory and excitability domains. A simple model and a more realistic model qualitatively account for the observed dynamical behavior. The latter model provides quantitative agreement with the experiments.

The reaction-diffusion equations provide a powerful framework for modeling nonequilibrium, cell-scale dynamics over the long time scales that are inaccessible by traditional molecular modeling approaches. Single-particle reaction-diffusion offers the highest resolution technique for tracking such dynamics, but it has not been applied to the study of protein self-assembly due to its treatment of reactive species as single-point particles. Here, we develop a relatively simple but accurate approach for building rigid structure and rotation into single-particle reaction-diffusion methods, providing a rate-based method for studying protein self-assembly. Our simplifying assumption is that reactive collisions can be evaluated purely on the basis of the separations between the sites, and not their orientations. The challenge of evaluating reaction probabilities can then be performed using well-known equations based on translational diffusion in both 3D and 2D, by employing an effective diffusion constant we derive here. We show how our approach reproduces both the kinetics of association, which is altered by rotational diffusion, and the equilibrium of reversible association, which is not. Importantly, the macroscopic kinetics of association can be predicted on the basis of the microscopic parameters of our structurally resolved model, allowing for critical comparisons with theory and other rate-based simulations. We demonstrate this method for efficient, rate-based simulations of self-assembly of clathrin trimers, highlighting how formation of regular lattices impacts the kinetics of association.

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We explore traveling waves with oscillatory tails in a bistable piecewise linear reaction-diffusion system of the FitzHugh-Nagumo type with linear cross diffusion. These waves differ fundamentally from the standard simple fronts of the kink type. In contrast to kinks, the waves studied here have a complex shape profile with a front-back-front (a pulse-front) pattern. The characteristic feature of such pulse-front waves is a hybrid type of the speed diagram, which on the one hand reflects the typical dynamical behavior of the fronts in the FitzHugh-Nagumo model, related to the nonequilibrium Ising-Bloch bifurcation, and on the other hand exhibits also the solitary pulse scenario where several waves appear simultaneously with different speeds of propagation. We describe analytically the wave profiles and heteroclinic trajectories in the phase plane and discuss their morphology and transformation. The phenomena of wave formation and propagation are also studied by numerical simulations of the model partial differential equations. These simulations support the view that the pulse-front waves are constructed of fronts and pulses.

Complex anatomical form is regulated in part by endogenous physiological communication between cells; however, the dynamics by which gap junctional (GJ) states across tissues regulate morphology are still poorly understood. We employed a biophysical modeling approach combining different signaling molecules (morphogens) to qualitatively describe the anteroposterior and lateral morphology changes in model multicellular systems due to intercellular GJ blockade. The model is based on two assumptions for blocking-induced patterning: (i) the local concentrations of two small antagonistic morphogens diffusing through the GJs along the axial direction, together with that of an independent, uncoupled morphogen concentration along an orthogonal direction, constitute the instructive patterns that modulate the morphological outcomes, and (ii) the addition of an external agent partially blocks the intercellular GJs between neighboring cells and modifies thus the establishment of these patterns. As an illustrative example, we study how the different connectivity and morphogen patterns obtained in presence of a GJ blocker can give rise to novel head morphologies in regenerating planaria. We note that the ability of GJs to regulate the permeability of morphogens post-translationally suggests a mechanism by which different anatomies can be produced from the same genome without the modification of gene-regulatory networks. Conceptually, our model biosystem constitutes a reaction-diffusion information processing mechanism that allows reprogramming of biological morphologies through the external manipulation of the intercellular GJs and the resulting changes in instructive biochemical signals.

Biological cells have the ability to switch internal states depending on the density of other cells in their local environment, referred to as "quorum sensing." The latter can be utilized to control collective structuring, such as in biofilm formation. In this work, we study a simple quorum sensing model of ideal (noninteracting) colloids with a switchable internal degree of freedom in the presence of external potentials. The colloids have two possible discrete states, in which they are affected differently by the external field, and switch with rates dependent on the local density in their environment. We study this model with reactive Brownian dynamics simulations, as well as with an appropriate reaction-diffusion theory. We find remarkable structuring in the system controlled by the density-mediated interactions between the ideal colloids. We report results of different functional forms for the rate dependence and quantify the influence of their parameters, in particular, discuss the role of the spatiotemporal sensing range, i.e., how the resulting structures depend on how the environmental information is "measured" by the colloids. Especially in the case of a rate function with sigmoidal dependence on local density, i.e., requiring a threshold density for switching, we observe significant correlation effects in the density profiles which are tuneable by the sensing ranges but also sensitive to noise and fluctuations. Hence, our model gives some basic insights into the nonequilibrium structuring mediated by simple quorum sensing protocols.

We derive and study two different formalisms used for nonequilibrium processes: the coherent-state path integral, and an effective, coarse-grained stochastic equation of motion. We first study the coherent-state path integral and the corresponding field theory, using the annihilation process A+A→A as an example. The field theory contains counterintuitive quartic vertices. We show how they can be interpreted in terms of a first-passage problem. Reformulating the coherent-state path integral as a stochastic equation of motion, the noise generically becomes imaginary. This renders it not only difficult to interpret, but leads to convergence problems at finite times. We then show how alternatively an effective coarse-grained stochastic equation of motion with real noise can be constructed. The procedure is similar in spirit to the derivation of the mean-field approximation for the Ising model, and the ensuing construction of its effective field theory. We finally apply our findings to stochastic Manna sandpiles. We show that the coherent-state path integral is inappropriate, or at least inconvenient. As an alternative, we derive and solve its mean-field approximation, which we then use to construct a coarse-grained stochastic equation of motion with real noise.

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Smoluchowski-type models for diffusion-influenced reactions (A + B → C) can be formulated within two frameworks: the probabilistic-based approach for a pair A, B of reacting particles and the concentration-based approach for systems in contact with a bath that generates a concentration gradient of B particles that interact with A. Although these two approaches are mathematically similar, it is not straightforward to establish a precise mathematical relationship between them. Determining this relationship is essential to derive particle-based numerical methods that are quantitatively consistent with bulk concentration dynamics. In this work, we determine the relationship between the two approaches by introducing the grand canonical Smoluchowski master equation (GC-SME), which consists of a continuous-time Markov chain that models an arbitrary number of B particles, each one of them following Smoluchowski's probabilistic dynamics. We show that the GC-SME recovers the concentration-based approach by taking either the hydrodynamic or the large copy number limit. In addition, we show that the GC-SME provides a clear statistical mechanical interpretation of the concentration-based approach and yields an emergent chemical potential for nonequilibrium spatially inhomogeneous reaction processes. We further exploit the GC-SME robust framework to accurately derive multiscale/hybrid numerical methods that couple particle-based reaction-diffusion simulations with bulk concentration descriptions, as described in detail through two computational implementations.

Diffusive motion of regulatory enzymes on biopolymers with eventual capture at a reaction site is a common feature in cell biology. Using a lattice gas model we study the impact of diffusion and capture for a microtubule polymerase and a depolymerase. Our results show that the capture mechanism localizes the proteins and creates large-scale spatial correlations. We develop an analytic approximation that globally accounts for relevant correlations and yields results that are in excellent agreement with experimental data. Our results show that diffusion and capture operates most efficiently at cellular enzyme concentrations which points to in vivo relevance.

The non-Hermitian skin effect refers to the accumulation of eigenstates near the boundary in open boundary lattice models, which can be systematically characterized using the non-Bloch band theory. Here, we apply the non-Bloch band theory to investigate the stochastic reaction-diffusion process by mapping it to a non-Hermitian Kitaev chain. We exactly obtain the open boundary spectrum and the generalized Brillouin zone, and identify a robust zero mode arising from the non-Bloch topology. Notably, distinct from its Hermitian counterpart in the quantum context, the zero mode supports anomalous dynamical crossover in the Markov process. We quantitatively demonstrate the intriguing dynamical effects through the spectral decomposition of the Hamiltonian on the non-Bloch eigenstates, and confirm our findings by conducting stochastic simulations with high accuracy. Our study highlights the significant and general role of non-Bloch topology in non-equilibrium dynamics.

An emerging mechanism for intracellular organization is liquid-liquid phase separation (LLPS). Found in both the nucleus and the cytoplasm, liquidlike droplets condense to create compartments that are thought to promote and inhibit specific biochemistry. In this work, a multiphase, Cahn-Hilliard diffuse interface model is used to examine RNA-protein interactions driving LLPS. We create a bivalent system that allows for two different species of protein-RNA complexes and model the competition that arises for a shared binding partner, free protein. With this system we demonstrate that the binding and unbinding of distinct RNA-protein complexes leads to diverse spatial pattern formation and dynamics within droplets. Both the initial formation and transient behavior of spatial patterning are subject to the exchange of free proteins between RNA-protein complexes. This study illustrates that spatiotemporal heterogeneity can emerge within phase-separated biological condensates with simple binding reactions and competition. Intradroplet patterning may influence droplet composition and, subsequently, cellular organization on a larger scale.

Intracellular protein patterns regulate a variety of vital cellular processes such as cell division and motility, which often involve dynamic cell-shape changes. These changes in cell shape may in turn affect the dynamics of pattern-forming proteins, hence leading to an intricate feedback loop between cell shape and chemical dynamics. While several computational studies have examined the rich resulting dynamics, the underlying mechanisms are not yet fully understood. To elucidate some of these mechanisms, we explore a conceptual model for cell polarity on a dynamic one-dimensional manifold. Using concepts from differential geometry, we derive the equations governing mass-conserving reaction-diffusion systems on time-evolving manifolds. Analyzing these equations mathematically, we show that dynamic shape changes of the membrane can induce pattern-forming instabilities in parts of the membrane, which we refer to as regional instabilities. Deformations of the local membrane geometry can also (regionally) suppress pattern formation and spatially shift already existing patterns. We explain our findings by applying and generalizing the local equilibria theory of mass-conserving reaction-diffusion systems. This allows us to determine a simple onset criterion for geometry-induced pattern-forming instabilities, which is linked to the phase-space structure of the reaction-diffusion system. The feedback loop between membrane shape deformations and reaction-diffusion dynamics then leads to a surprisingly rich phenomenology of patterns, including oscillations, traveling waves, and standing waves, even if these patterns do not occur in systems with a fixed membrane shape. Our paper reveals that the local conformation of the membrane geometry acts as an important dynamical control parameter for pattern formation in mass-conserving reaction-diffusion systems.

Spatial patterns formed by biomacromolecules such as proteins are widely present in biological systems and are closely related to fundamental cellular processes. A classic example is the spatial patterning of Min proteins in bacteria, where pole-to-pole oscillations of these patterns guide symmetric cell division. To uncover the underlying mechanisms behind the formation and transition of spatial patterns in the Min protein system, we applied nonequilibrium landscape-flux theory combined with the mode expansion method. By quantifying and visualizing the potential landscape in mode space, we identified distinct stable spatial patterns as potential wells, providing a global perspective on the system's stability. Moreover, we revealed that nonequilibrium flux acts as the driving force for spatial pattern switching with increasing cell length or molecular detachment rates. Peaks in the average flux and entropy production rate near phase boundaries highlight significant changes in dynamical nature and thermodynamic cost during critical transitions, offering deeper insights into the physical mechanisms underlying spatial pattern transitions. These findings not only underscore how spatial landscape topography and flux dynamics collectively govern the formation, stability, and switching of protein patterns but also establish a powerful framework for linking nonequilibrium physical mechanisms to biological functions. Furthermore, this framework holds potential applications, such as the detection of early warning signals for cell division.

Significance The spontaneous formation of patterns by multiprotein systems is essential for orchestrating fundamental biological processes like cell division and embryogenesis. Although various membrane proteins can generate patterns on continuous substrates, the mechanisms governing their self-organization on discontinuous, dispersed, and motile membranes, typical of intracellular environments, remain elusive. Here, we demonstrate that MinDE proteins, a prototypical pattern-forming system from Escherichia coli, can form robust 3D patterns when interacting with a suspension of nanoscale liposomes, rather than a continuous 2D lipid membrane. Through a combination of modeling and in vitro experiments, we reveal that membrane fragmentation controls pattern formation by effectively rescaling biochemical and transport rates. Our results provide critical insights into intracellular self-organization and the design of tunable, out-of-equilibrium biomimetic systems.

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Reaction-diffusion systems have been widely used to study spatio-temporal phenomena in cell biology, such as cell polarization. Coupled bulk-surface models naturally include compartmentalization of cytosolic and membrane-bound polarity molecules. Here we study the distribution of the polarity protein Cdc42 in a mass-conserved membrane-bulk model, and explore the effects of diffusion and spatial dimensionality on spatio-temporal pattern formation. We first analyze a one-dimensional (1-D) model for Cdc42 oscillations in fission yeast, consisting of two diffusion equations in the bulk domain coupled to nonlinear ODEs for binding kinetics at each end of the cell. In 1-D, our analysis reveals the existence of symmetric and asymmetric steady states, as well as anti-phase relaxation oscillations typical of slow-fast systems. We then extend our analysis to a two-dimensional (2-D) model with circular bulk geometry, for which species can either diffuse inside the cell or become bound to the membrane and undergo a nonlinear reaction-diffusion process. We also consider a nonlocal system of PDEs approximating the dynamics of the 2-D membrane-bulk model in the limit of fast bulk diffusion. In all three model variants we find that mass conservation selects perturbations of spatial modes that simply redistribute mass. In 1-D, only anti-phase oscillations between the two ends of the cell can occur, and in-phase oscillations are excluded. In higher dimensions, no radially symmetric oscillations are observed. Instead, the only instabilities are symmetry-breaking, either corresponding to stationary Turing instabilities, leading to the formation of stationary patterns, or to oscillatory Turing instabilities, leading to traveling and standing waves. Codimension-two Bogdanov-Takens bifurcations occur when the two distinct instabilities coincide, causing traveling waves to slow down and to eventually become stationary patterns. Our work clarifies the effect of geometry and dimensionality on behaviors observed in mass-conserved cell polarity models.

Since the onset of intracellular voltage recording techniques, additional methods have been developed and improved upon, such as using voltage-activated dyes, sodium indicators, fluorescent proteins (namely Green fluorescent protein (GFP)), synthetic and genetically encoded indicators, in conjunction with calcium imaging Gasparini & Palmer (2016). These techniques have shown that dendrites are not just simple transmission lines, but are sophisticated cellular systems with nonlinear multiscale dynamics that evolve over different timescales and are involved in neural signaling, information processing, along with any underlying computations. Calcium imaging has been important in this regard, having highlighted how reaction-diffusion processes between calcium, buffers and other proteins shape neuronal activity, through dynamical interaction and synaptic plasticity, over different timescales compared to the evolution of electrical signals. To this end, experiments have shown the involvement of calcium and calcium dependent buffers in the response dynamics of neurons. A novel participant during morphological studies, using electron microscopy, fluorescence and immunostaining have illustrated that the Endoplasmic Reticulum (ER) (present in the soma and extends into the distal dendrites) is also a calcium store that can release calcium as puffs through the activation ryanodine receptors into the cytosol of neuronal dendrites. This is called Ca2+ -induced Ca2+ release (CICR), which have been implicated in a number of processes, including the occurrence of calcium waves in the presence of a unsaturated buffer. In this situation, one can observe local changes to the Ca2+ and buffer concentrations in response to some stimuli, such as the presentation of orientated stationary or moving bars or gratings, in a selective fashion through the manifestation of a bias in the resulting calcium concentration in space along the dendrite, that underpins some computation. Studies have shown that Ca2+ plays many important roles in neuronal function and information processing. To better understand the role of Ca2+, we constructed a computational model of a dendrite with a mechanism that describes CICR in the presence of an unsaturated buffer and study the conditions permitting the occurrence calcium waves and the underlying requirements of timed inputs from CICR. Modeling the heterogeneity of CICR from the endoplasmic reticulum by using a formulation that permits essential dynamics to be analyzed. Using a two-pool model calcium dynamics, we present an analysis of how CICR impacts calcium activity in space in the presence of a calcium buffer and study the potential conditions supporting the propagation of CICR induced Ca2+ waves.

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Reaction-diffusion models with nonlocal constraints naturally arise as limiting cases of coupled bulk-surface models of intracellular signalling. In this paper, a minimal, mass-conserving model of cell-polarization on a curved membrane is analyzed in the limit of slow surface diffusion. Using the tools of formal asymptotics and calculus of variations, we study the characteristic wave-pinning behavior of this system on three dynamical timescales. On the short timescale, generation of an interface separating high- and low-concentration domains is established under suitable conditions. Intermediate timescale dynamics is shown to lead to a uniform growth or shrinking of these domains to sizes which are fixed by global parameters. Finally, the long time dynamics reduces to area-preserving geodesic curvature flow that may lead to multi-interface steady state solutions. These results provide a foundation for studying cell polarization and related phenomena in biologically relevant geometries.

Phase separation, as one important type of pattern formation, plays a critical role in regulating cellular processes and sustaining ecological resilience. Mass-conserving reaction-diffusion (MCRD) models have been proposed to capture the underlying principles of phase separation. However, previous studies have largely established only phenomenological analogies between MCRD dynamics and phase separation. Here, we identify an experimental model system based on the double-stranded DNA-human protein p53 interactive co-condensate (DPIC). Importantly, all parameters required for the MCRD model can be independently and directly measured in this system, without reliance on parameter estimation or unverified assumptions. We demonstrate that (1) the DPICs serve as an ideal experimental system for establishing a direct and quantitative bridge between experimental DPICs and the MCRD framework and (2) the MCRD model captures more than just a phenomenological resemblance to phase separation, and quantitatively reproduces the underlying dynamics.

During macropinocytosis, cells remodel their morphologies for the uptake of extracellular matter. This endocytotic mechanism relies on the collapse and closure of precursory structures, which are propagating actin-based, ring-shaped vertical undulations at the dorsal (top) cell membrane, a.k.a. circular dorsal ruffles (CDRs). As such, CDRs are essential to a range of vital and pathogenic processes alike. Here we show, based on both experimental data and theoretical analysis, that CDRs are propagating fronts of actin polymerization in a bistable system. The theory relies on a novel mass-conserving reaction–diffusion model, which associates the expansion and contraction of waves to distinct counter-propagating front solutions. Moreover, the model predicts that under a change in parameters (for example, biochemical conditions) CDRs may be pinned and fluctuate near the cell boundary or exhibit complex spiral wave dynamics due to a wave instability. We observe both phenomena also in our experiments indicating the conditions for which macropinocytosis is suppressed. Circular dorsal ruffles (CDRs) are important for the vesicular uptake of extracellular matter, but the basis of their wave dynamics is not understood. Here, the authors propose and experimentally test a bistable reaction-diffusion system, which they show accounts for the typical CDR expansion and shrinkage and for aberrant formation of pinned waves and spirals.

Mass-conserving reaction-diffusion systems with bistable nonlinearity are considered under general assumptions. The existence of stationary solutions with a single internal transition layer in such reaction-diffusion systems is shown using the analytical singular perturbation theory. Moreover, a stability criterion for the stationary solutions is provided by calculating the Evans function.

Mass-conserving reaction-diffusion systems with bistable nonlinearity are useful models for studying cell polarity formation, which is a key process in cell division and differentiation. We rigorously show the existence and stability of stationary solutions with a single internal transition layer in such reaction-diffusion systems under general assumptions by the singular perturbation theory. Moreover, we present a meaningful model for understanding the existence of an unstable transition layer solution; our numerical simulations show that the unstable solution is a separatrix of the dynamics of the model.

Mass-conservative reaction-diffusion systems have recently been proposed as a general framework to describe intracellular pattern formation. These systems have been used to model the conformational switching of proteins as they cycle from an inactive state in the cell cytoplasm, to an active state at the cell membrane. The active state then acts as input to downstream effectors. The paradigm of activation by recruitment to the membrane underpins a range of biological pathways - including G-protein signalling, growth control through Ras and PI 3-kinase, and cell polarity through Rac and Rho; all activate their targets by recruiting them from the cytoplasm to the membrane. Global mass conservation lies at the heart of these models reflecting the property that the total number of active and inactive forms, and targets, remains constant. Here we present a conservative arbitrary Lagrangian Eulerian (ALE) finite element method for the approximate solution of systems of bulk-surface reaction-diffusion equations on an evolving two-dimensional domain. Fundamental to the success of the method is the robust generation of bulk and surface meshes. For this purpose, we use a moving mesh partial differential equation (MMPDE) approach. Global conservation of the fully discrete finite element solution is established independently of the ALE velocity field and the time step size. The developed method is applied to model problems with known analytical solutions; these experiments indicate that the method is second-order accurate and globally conservative. The method is further applied to a model of a single cell migrating in the presence of an external chemotactic signal.

Motile eukaryotic cells display distinct modes of migration that often occur within the same cell type. It remains unclear, however, whether transitions between the migratory modes require changes in external conditions, or whether the different modes are coexisting states that emerge from the underlying signaling network. Using a simplified mass-conserved reaction-diffusion model of small GTPase signaling with F-actin mediated feedback, we uncover a distinct bistable mechanism (involving gradient-like phase-separation and traveling waves) and a regime where a polarized mode of migration coexists with spatiotemporal oscillations; the latter, in larger domains, including in three-dimensional surface geometry, result in disordered patterns even in the absence of noise or shape deformations. Indeed, experimental observations of Dictyostelium discoideum show that, upon collision with a rigid boundary, cells may switch from polarized to disordered motion.

The global well-posedness of a large class of reaction-diffusion systems with fractional diffusion in the whole space RN is established. The fractional diffusion accounts for the non-local nature of the problem, modelling scenarios where species follow random walks with long jumps or Lévy flights. The reactions are assumed to preserve the non-negativity of solutions and conserve or, more generally, dissipate the total mass. Even in the case of local diffusion, it is well known that these two natural assumptions alone do not suffice to prevent finite-time blow-up. In this paper, we show that if the nonlinearities grow at most quadratically, then a unique global bounded solution exists regardless of the fractional order of diffusion. This is achieved by proving a regularizing effect of the fractional diffusion operator and combining it with a Hölder continuity result for non-local, nonhomogeneous parabolic equations. When the nonlinearities are super-quadratic but satisfy certain intermediate sum conditions depending on the fractional order, we establish the global well-posedness by developing duality methods for the fractional diffusion. These results substantially extend the theory of reaction-diffusion systems with mass dissipation to the setting of fractional diffusion and unbounded domains.

Cells realize various life activities such as migrations and the ingestion of extracellular substances by deforming their cytoplasm and cell membrane. Many reaction-diffusion systems with mass conservation have been used for describing these cell activities. Among them, a mass-conserved three-component reaction-diffusion system was proposed to describe the dynamics of wavelike actin polymerization in macropinocytosis (Yochelis et al. in Phys. Rev. E 101:022213, 2020). This system numerically exhibits dynamical patterns such as annihilation, crossover, and nucleation of pulses in a relatively large interval. In this article, to investigate the dynamics of wavelike actin polymerization in macropinocytosis, we first establish the condition for the diffusion driven instability in the system. We then rigorously prove the existence of spiky stationary solutions to the system in a bounded interval with the Neumann condition. These solutions play a crucial role in the nucleation of pulses although it is only numerically demonstrated. By reducing the stationary problem to a scalar second order nonlinear equation with a nonlocal term, we construct the desired solution by converting the equation into an integral equation.

Cell polarity is a general cellular process that can be seen in various cell types such as migrating neutrophils and Dictyostelium cells. The Rho small GTP(guanosine 5′-tri phosphate)ases have been shown to regulate cell polarity; however, its mechanism of emergence has yet to be clarified. We first developed a reaction–diffusion model of the Rho GTPases, which exhibits switch-like reversible response to a gradient of extracellular signals, exclusive accumulation of Cdc42 and Rac, or RhoA at the maximal or minimal intensity of the signal, respectively, and tracking of changes of a signal gradient by the polarized peak. The previous cell polarity models proposed by Subramanian and Narang show similar behaviors to our Rho GTPase model, despite the difference in molecular networks. This led us to compare these models, and we found that these models commonly share instability and a mass conservation of components. Based on these common properties, we developed conceptual models of a mass conserved reaction–diffusion system with diffusion–driven instability. These conceptual models retained similar behaviors of cell polarity in the Rho GTPase model. Using these models, we numerically and analytically found that multiple polarized peaks are unstable, resulting in a single stable peak (uniqueness of axis), and that sensitivity toward changes of a signal gradient is specifically restricted at the polarized peak (localized sensitivity). Although molecular networks may differ from one cell type to another, the behaviors of cell polarity in migrating cells seem similar, suggesting that there should be a fundamental principle. Thus, we propose that a mass conserved reaction–diffusion system with diffusion-driven instability is one of such principles of cell polarity.

Today we can use physics to describe in great detail many of the phenomena intervening in the process of life. But no analogous unified description exists for the phenomenon of life itself. In spite of their complexity, all living creatures are out of equilibrium chemical systems sharing four fundamental properties: they (1) handle information, (2) metabolize, (3) self-reproduce and (4) evolve. This small number of features, which in terran life are implemented with biochemistry, point to an underlying simplicity that can be taken as a guide to motivate and implement a theoretical physics style unified description of life using tools from the non-equilibrium physical-chemistry of extended systems. Representing a system with general rules is a well stablished approach to model building and unification in physics, and we do this here to provide an abstract mathematical description of life. We start by reviewing the work of previous authors showing how the properties in the above list can be individually represented with stochastic reaction-diffusion kinetics using polynomial reaction terms. These include "switches" and computation, the kinetic representation of autocatalysis, Turing instability and adaptation in the presence of both deterministic and stochastic environments. Thinking of these properties as existing on a space-time lattice each of whose nodes are subject to a common mass-action kinetics compatible with the above, leads to a very rich dynamical system which, just as natural life, unifies the above properties and can therefore be interpreted as a high level or "outside-in" theoretical physics representation of life. Taking advantage of currently available advanced computational techniques and hardware, we compute the phase plane for this dynamical system both in the deterministic and stochastic cases. We do simulations and show numerically how the system works. We review how to extract useful information that can be mapped into emergent physical phenomena and attributes of importance in life such as the presence of a "membrane" or the time evolution of an individual system's negentropy or mass. Once these are available, we illustrate how to perform some basic phenomenology based on the model's numerical predictions. Applying the above to the idealization of the general Cell Division Cycle (CDC) given almost 25 years ago by Hunt and Murray, we show from the numerical simulations how this system executes a form of the idealized CDC. We also briefly discuss various simulations that show how other properties of living systems such as migration towards more favorable regions or the emergence of effective Lotka-Volterra populations are accounted for by this general and unified view from the "top" of the physics of life. The paper ends with some discussion, conclusions, and comments on some selected directions for future research. The mathematical techniques and powerful simulation tools we use are all well established and presented in a "didactical" style. We include a very rich but concise SI where the numerical details are thoroughly discussed in a way that anyone interested in studying or extending the results would be able to do so.

A reaction–diffusion system with mass conservation modeling cell polarity is considered. A range of the parameters is found where the ω‐limit set of the solution is spatially homogeneous, containing constant stationary solution as well as possible nontrivial spatially homogeneous orbit.

We study diffusion in systems of classical particles whose dynamics conserves the total center of mass. This conservation law leads to several interesting consequences. In finite systems, it allows for equilibrium distributions that are exponentially localized near system boundaries. It also yields an unusual approach to equilibrium, which in d dimensions exhibits scaling with dynamical exponent z=4+d. Similar phenomena occur for dynamics that conserves higher moments of the density, which we systematically classify using a family of nonlinear diffusion equations. In the quantum setting, analogous fermionic systems are shown to form real-space Fermi surfaces, while bosonic versions display a real-space analog of Bose-Einstein condensation.

We study a bulk-surface coupled system that describes the processes of lipid-phase separation and lipid-cholesterol interaction on cell membranes, in which cholesterol exchange between cytosol and cell membrane is also incorporated. The PDE system consists of a surface Cahn-Hilliard equation for the relative concentration of saturated/unsaturated lipids and a surface diffusion-reaction equation for the cholesterol concentration on the membrane, together with a diffusion equation for the cytosolic cholesterol concentration in the bulk. The detailed coupling between bulk and surface evolutions is characterized by a mass exchange term $q$. For the system with a physically relevant singular potential, we first prove the existence, uniqueness and regularity of global weak solutions to the full bulk-surface coupled system under suitable assumptions on the initial data and the mass exchange term $q$. Next, we investigate the large cytosolic diffusion limit that gives a reduction of the full bulk-surface coupled system to a system of surface equations with non-local contributions. Afterwards, we study the long-time behavior of global solutions in two categories, i.e., the equilibrium and non-equilibrium models according to different choices of the mass exchange term $q$. For the full bulk-surface coupled system with a decreasing total free energy, we prove that every global weak solution converges to a single equilibrium as $t\to +\infty$. For the reduced surface system with a mass exchange term of reaction type, we establish the existence of a global attractor.

Abstract. In this paper, we study unique, globally defined uniformly bounded weak solutions for a class of semilinear reaction-diffusion-advection systems. The coefficients of the differential operators and the initial data are only required to be measurable and uniformly bounded. The nonlinearities are quasi-positive and satisfy a commonly called mass control or dissipation of mass property. Moreover, we assume the intermediate sum condition of a certain order. The key feature of this work is the surprising discovery that quasi-positive systems that satisfy an intermediate sum condition automatically give rise to a new class of L-energy type functionals that allow us to obtain requisite uniform a priori bounds. Our methods are sufficiently robust to extend to different boundary conditions, or to certain quasi-linear systems. We also show that in case of mass dissipation, the solution is bounded in sup-norm uniformly in time. We illustrate the applicability of results by showing global existence and large time behavior of models arising from a spatio-temporal spread of infectious disease.

The use of dissipation for the controlled creation of nontrivial quantum many-body correlated states is of much fundamental and practical interest. What is the result of imposing number conservation, which, in closed system, gives rise to diffusive spreading? We investigate this question for a paradigmatic model of a two-band system, with dissipative dynamics aiming to empty one band and to populate the other, which had been introduced before for the dissipative stabilization of topological states. Going beyond the mean-field treatment of the dissipative dynamics, we demonstrate the emergence of a diffusive regime for the particle and hole density modes at intermediate length- and time-scales, which, interestingly, can only be excited in nonlinear response to external fields. We also identify processes that limit the diffusive behavior of this mode at the longest length- and time-scales. Strikingly, we find that these processes lead to a reaction-diffusion dynamics governed by the Fisher-Kolmogorov-Petrovsky-Piskunov equation, making the designed dark state unstable towards a state with a finite particle and hole density.

We study the Oslo model, a paradigm for absorbing-phase transition, on a one-dimensional ring of L sites with a fixed global density ρ[over ¯]; we consider the system strictly above critical density ρ_{c}. Notably, microscopic dynamics conserve both mass and center of mass (CoM), but lack time-reversal symmetry. We show that, despite having highly constrained dynamics due to CoM conservation, the system exhibits diffusive relaxation away from criticality and superdiffusive relaxation near criticality. Furthermore, the CoM conservation severely restricts particle movement, causing the mobility-a transport coefficient analogous to the conductivity for charged particles-to vanish exactly. Indeed, the steady-state temporal growth of current fluctuation is qualitatively different from that observed in diffusive systems with a single conservation law. Remarkably, far from criticality where the relative density Δ=ρ[over ¯]-ρ_{c}≫ρ_{c}, the second cumulant, or the variance, 〈Q_{i}^{2}(T,Δ)〉_{c}, of current Q_{i} across the ith bond up to time T in the steady-state saturates as 〈Q_{i}^{2}〉_{c}≃Σ_{Q}^{2}(Δ)-constT^{-1/2}; near criticality, it grows subdiffusively as 〈Q_{i}^{2}〉_{c}∼T^{α}, with 0<α<1/2, and eventually saturates to Σ_{Q}^{2}(Δ). Interestingly, the asymptotic current fluctuation Σ_{Q}^{2}(Δ) is a nonmonotonic function of Δ: It diverges as Σ_{Q}^{2}(Δ)∼Δ^{2} for Δ≫ρ_{c} and Σ_{Q}^{2}(Δ)∼Δ^{-δ}, with δ>0, for Δ→0^{+}. Using a mass-conservation principle, we exactly determine the exponents δ=2(1-1/ν_{⊥})/ν_{⊥} and α=δ/zν_{⊥} via the correlation-length and dynamic exponents, ν_{⊥} and z, respectively. Finally, we show that in the steady state the self-diffusion coefficient D_{s}(ρ[over ¯]) of tagged particles is connected to activity through the relation D_{s}(ρ[over ¯])=a(ρ[over ¯])/ρ[over ¯].

In this paper, we study the asymptotic behaviour of the solutions to a degenerate reaction–diffusion system. This system admits a continuum of discontinuous stationary solutions due to the effect of a hysteresis process, but only one discontinuous stationary solution is compatible with a principle of preservation of locally invariant regions. Using a macroscopic mass effect which guarantees that fast particles help slow particles to displace, we establish a novel result of convergence of a non trivial set of trajectories towards a discontinuous pattern.

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No abstract available

We investigate the oscillatory dynamics and bifurcation structure of a reaction–diffusion system with bistable nonlinearity and mass conservation, which was proposed by (Otsuji et al., PLoS Comp Biol 3:e108, 2007). The system is a useful model for understanding cell polarity formation. We show that this model exhibits four different spatiotemporal patterns including two types of oscillatory patterns, which can be regarded as cell polarity oscillations with the reversal and non-reversal of polarity, respectively. The trigger causing these patterns is a diffusion-driven (Turing-like) instability. Moreover, we investigate the effects of extracellular signals on the cell polarity oscillations.

Making sense of complex inhomogeneous systems composed of many interacting species is a grand challenge that pervades basically all natural sciences. Phase separation and pattern formation in reaction-diffusion systems have been largely studied as two separate paradigms. Here we show that in reaction-diffusion systems composed of many species, the presence of a conservation law constrains the evolution of the conserved quantity to be governed by a Cahn-Hilliard-like equation. This establishes a direct link with the paradigm of coexistence and recent "active" field theories. Hence, even for complex many-species systems a dramatically simplified but accurate description emerges over coarse spatiotemporal scales. Using the nullcline (the line of homogeneous steady states) as the central motif, we develop a geometrical framework which endows chemical space with a basis and suitable coordinates. This framework allows us to capture and understand the effect of eliminating fast nonconserved degrees of freedom and to explicitly construct coefficients of the coarse field theory. We expect that the theory we develop here will be particularly relevant to advance our understanding of biomolecular condensates.

No abstract available

The present paper introduces a new micro-meso hybrid algorithm based on the Ghost Cell Method concept in which the microscopic subdomain is governed by the Reactive Multi-Particle Collision (RMPC) dynamics. The mesoscopic subdomain is modeled using the Reaction-Diffusion Master Equation (RDME). The RDME is solved by means of the Inhomogeneous Stochastic Simulation Algorithm. No hybrid algorithm has hitherto used the RMPC dynamics for modeling reactions and the trajectories of each individual particle. The RMPC is faster than other molecular based methods and has the advantage of conserving mass, energy and momentum in the collision and free streaming steps. The new algorithm is tested on three reaction-diffusion systems. In all the systems studied, very good agreement with the deterministic solutions of the corresponding differential equations is obtained. In addition, it has been shown that proper discretization of the computational domain results in significant speed-ups in comparison with the full RMPC algorithm.

Weakly nonlinear amplitude equations are derived for the onset of spatially extended patterns on a general class of n-component bulk-surface reaction-diffusion systems in a ball, under the assumption of linear kinetics in the bulk and coupling Robin-type boundary conditions. Linear analysis shows conditions under which various pattern modes can become unstable to either generalised pitchfork or transcritical bifurcations depending on the parity of the spatial wavenumber. Weakly nonlinear analysis is used to derive general expressions for the multi-component amplitude equations of different patterned states. These reduced-order systems are found to agree with prior normal forms for pattern formation bifurcations with O(3) symmetry and they provide information on the stability of bifurcating patterns of different symmetry types. The analysis is complemented with numerical results using a dedicated bulk-surface finite-element method. The theory is illustrated in two examples: a bulk-surface version of the Brusselator and a four-component bulk-surface cell-polarity model.

The bulk-surface wave pinning model is a reaction-diffusion system for studying cell polarisation. It is constituted by a surface reaction-diffusion equation, coupled to a bulk diffusion equation with a non-linear boundary condition. Cell polarisation arises as the surface component develops specific patterns. Since proteins diffuse much faster in the cell interior than on the membrane, in the literature, the bulk component is often assumed to be spatially homogeneous. Therefore, the model can be reduced to a single surface equation. However, in real applications a spatially non-uniform bulk component might be an important player to take into account. In this paper, we study, through numerical computations, the role of the bulk component and, more specifically, how different bulk diffusion rates might affect the polarisation response. We find that the bulk component is indeed a key factor in determining the surface polarisation response. Moreover, for certain geometries, it is the spatial heterogeneity of the bulk component that triggers the polarisation response, which might not be possible in a reduced model. Understanding how polarisation depends on bulk diffusivity might be crucial when studying models of migrating cells, which are naturally subject to domain deformation.

In this article, we formulate new models for coupled systems of bulk-surface reaction–diffusion equations on stationary volumes. The bulk reaction–diffusion equations are coupled to the surface reaction–diffusion equations through linear Robin-type boundary conditions. We then state and prove the necessary conditions for diffusion-driven instability for the coupled system. Owing to the nature of the coupling between bulk and surface dynamics, we are able to decouple the stability analysis of the bulk and surface dynamics. Under a suitable choice of model parameter values, the bulk reaction–diffusion system can induce patterning on the surface independent of whether the surface reaction–diffusion system produces or not, patterning. On the other hand, the surface reaction–diffusion system cannot generate patterns everywhere in the bulk in the absence of patterning from the bulk reaction–diffusion system. For this case, patterns can be induced only in regions close to the surface membrane. Various numerical experiments are presented to support our theoretical findings. Our most revealing numerical result is that, Robin-type boundary conditions seem to introduce a boundary layer coupling the bulk and surface dynamics.

We develop a continuum framework applicable to solid-state hydrogen storage, cell biology and other scenarios where the diffusion of a single constituent within a bulk region is coupled via adsorption/desorption to reactions and diffusion on the boundary of the region. We formulate content balances for all relevant constituents and develop thermodynamically consistent constitutive equations. The latter encompass two classes of kinetics for adsorption/desorption and chemical reactions—fast and Marcelin–De Donder, and the second class includes mass action kinetics as a special case. We apply the framework to derive a system consisting of the standard diffusion equation in bulk and FitzHugh–Nagumo type surface reaction–diffusion system of equations on the boundary. We also study the linear stability of a homogeneous steady state in a spherical region and establish sufficient conditions for the occurrence of instabilities driven by surface diffusion. These findings are verified through numerical simulations which reveal that instabilities driven by diffusion lead to the emergence of steady-state spatial patterns from random initial conditions and that bulk diffusion can suppress spatial patterns, in which case temporal oscillations can ensue. We include an extension of our framework that accounts for mechanochemical coupling when the bulk region is occupied by a deformable solid. This article is part of the theme issue ‘Foundational issues, analysis and geometry in continuum mechanics’.

No abstract available

Even the simplest cells show a remarkable degree of intracellular patterning. Like developing multicellular organisms, single cells break symmetry to establish polarity axes, pattern their cortex and interior, and undergo morphogenesis to acquire sometimes complex shapes. Symmetry‐breaking and molecular patterns can be established through coupling of negative and positive feedback reactions in biochemical reaction‐diffusion systems. Physical forces, perhaps best studied in the contraction of the metazoan acto‐myosin cortex, which induces cortical and cytoplasmic flows, also serve to pattern‐associated components. A less investigated physical perturbation is the in‐plane flow of plasma membrane material caused by membrane trafficking. In this review, we discuss how bulk membrane flows can be generated at sites of active polarized secretion and growth, how they affect the distribution of membrane‐associated proteins, and how they may be harnessed for patterning and directional movement in cells across the tree of life.

No abstract available

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Min system, which determines the cell division plane of bacteria, uses the localization change of protein (Min wave) emerged by a reaction-diffusion coupling. Although previous studies have shown that cell-sized space and boundaries modulate shape and speed of Min waves, its effects on Min wave emergence was still elusive. Here, by using a fully confined microsized space as a mimic of live cells, we revealed that confinement changes conditions for Min wave emergence. In the microsized space, an increase of surface-to-volume ratio changed the localization efficiency of proteins on membranes, and therefore, suppression of the localization change was necessary to produce stable Min wave generations. Furthermore, we showed that the cell-sized space more strictly limits parameters for wave emergence because confinement inhibits instability and excitability of the system. These results illuminate that confinement of reaction-diffusion systems works as a controller of spatiotemporal patterns in live cells.

Active phase separations evade canonical thermodynamic descriptions and have thus challenged our understanding of coexistence and interfacial phenomena. Considerable progress has been made towards a nonequilibrium theoretical description of these traditionally thermodynamic concepts. Spatial parity symmetry is conspicuously assumed in much of this progress, despite the ubiquity of chirality in experimentally realized systems. In this Letter, we derive a theory for the phase coexistence and interfacial fluctuations of a system that microscopically violates spatial parity. We find suppression of the phase separation as chirality is increased as well as the development of steady-state currents tangential to the interface dividing the phases. These odd flows are irrelevant to stationary interfacial properties, with stability, capillary fluctuations, and surface area minimization determined entirely by the capillary surface tension. Using large-scale Brownian dynamics simulations, we find excellent agreement with our theoretical scaling predictions.

The crystallization of hard spheres at equilibrium is perhaps the most familiar example of an entropically-driven phase transition. In recent years, it has become clear that activity can dramatically alter this order-disorder transition in unexpected ways. The theoretical description of active crystallization has remained elusive as the traditional thermodynamic arguments that shape our understanding of passive freezing are inapplicable to active systems. Here, we develop a statistical mechanical description of the one-body density field and a nonconserved order parameter field that represents local crystalline order. We develop equations of state, guided by computer simulations, describing the crystallinity field which result in shifting the order-disorder transition to higher packing fractions with increasing activity. We then leverage our recent dynamical theory of coexistence to construct the full phase diagram of active Brownian spheres, quantitatively recapitulating both the solid-fluid and liquid-gas coexistence curves along with the solid-liquid-gas triple point.

No abstract available

No abstract available

We introduce a multispecies lattice-gas model for motor protein driven collective cargo transport on cellular filaments. We use this model to describe and analyze the collective motion of interacting vesicle cargos being carried by oppositely directed molecular motors, moving on a single biofilament. Building on a totally asymmetric exclusion process to characterize the motion of the interacting cargos, we allow for mass exchange with the environment, input, and output at filament boundaries and focus on the role of interconversion rates and how they affect the directionality of the net cargo transport. We quantify the effect of the various different competing processes in terms of nonequilibrium phase diagrams. The interplay of interconversion rates, which allow for flux reversal and evaporation-deposition processes, introduces qualitatively unique features in the phase diagrams. We observe regimes of three-phase coexistence, the possibility of phase re-entrance, and a significant flexibility in how the different phase boundaries shift in response to changes in control parameters. The moving steady-state solutions of this model allows for different possibilities for the spatial distribution of cargo vesicles, ranging from homogeneous distribution of vesicles to polarized distributions, characterized by inhomogeneities or shocks. Current reversals due to internal regulation emerge naturally within the framework of this model. We believe that this minimal model will clarify the understanding of many features of collective vesicle transport, apart from serving as the basis for building more exact quantitative models for vesicle transport relevant to various in vivo situations.

No abstract available

Motility and nonreciprocity are two primary mechanisms for self-organization in active matter. In a recent study [], we explored their joint influence in a minimal model of two-species quorum-sensing active particles interacting via mutual motility regulation. Our results notably revealed a highly dynamic phase of chaotic chasing bands that is absent when either nonreciprocity or self-propulsion is missing. Here, we examine further the phase behavior of nonreciprocal quorum-sensing active particles, distinguishing between the regimes of weak and strong nonreciprocity. In the weakly nonreciprocal regime, this system exhibits multicomponent motility-induced phase separation. We establish an analytical criterion for the associated phase coexistence, enabling a quantitative prediction of the phase diagram of a large class of nonreciprocal mixtures. For strong nonreciprocity, where the dynamics is chase-and-run-like, we determine the phase behavior and show that it depends strongly on the scale of observation. In small systems, our numerical simulations reveal a phenomenology consistent with phenomenological models, comprising traveling phase-separated domains and spiral-like defect patterns. However, we show that these structures are generically unstable in large systems, where they are superseded by bulk phase coexistence between domains that are either homogeneous or populated by mesoscopic chasing bands. Crucially, this implies that collective motion totally vanishes at large scales, while the breakdown of our analytical criterion for this phase coexistence with multiscale structures prevents us from predicting the corresponding phase diagram. Published by the American Physical Society 2025

Active Brownian particles (ABPs) serve as a minimal model of active matter systems. When ABPs are sufficiently persistent, they undergo a liquid-gas phase separation and, in the presence of obstacles, accumulate around them, forming a wetting layer. Here, we perform simulations of ABPs in a quasi-one-dimensional domain in the presence of a wall, studying the dynamics of the polarization field. Over the course of time, we observe a transition from a homogeneous (where all particles are aligned) to a heterogeneous (where particles align only at the interface) polarization regime. We propose coarse-grained equations for the density and polarization fields based on microscopic and phenomenological arguments that correctly account for the observed phenomena.

Chemical activity is central in active matter, where local fuel consumption can lead to self-propulsion and phase separation. However, phase separation can also originate from passive physical interactions. To understand the influence of chemical activity on phase separation, we study mixtures where solvent species interconvert while solutes segregate. We demonstrate that such reactions affect phase separation by altering the chemical potential balance and by introducing an osmotic pressure difference at interfaces. However, the system does not permit a pseudopressure balance, and bulk compositions depend on which phases are in contact. Consequently, phase coexistence is no longer transitive, which enables self-propulsion and more complex dynamics.

Multicomponent phase separation is a routine occurrence in both living and synthetic systems. Thermodynamics provides a straightforward path to determine the phase boundaries that characterize these transitions for systems at equilibrium. The prevalence of phase separation in complex systems outside the confines of equilibrium motivates the need for a nonequilibrium theory of multicomponent phase coexistence. Here, we develop a mechanical theory for coexistence that casts coexistence criteria into the familiar form of equality of state functions. Our theory generalizes traditional equilibrium notions such as the species chemical potential and thermodynamic pressure to systems out of equilibrium. Crucially, while these notions may not be identifiable for all nonequilibrium systems, we numerically verify their existence for a variety of systems by introducing the phenomenological multicomponent active model B+. Our work establishes an initial framework for understanding multicomponent coexistence that we hope can serve as the basis for a comprehensive theory for high-dimensional nonequilibrium phase transitions.

Whereas self-propelled hard discs undergo motility-induced phase separation, self-propelled rods exhibit a variety of nonequilibrium phenomena, including clustering, collective motion, and spatio-temporal chaos. In this work, we present a theoretical framework representing active particles by continuum fields. This concept combines the simplicity of alignment-based models, enabling analytical studies, and realistic models that incorporate the shape of self-propelled objects explicitly. By varying particle shape from circular to ellipsoidal, we show how nonequilibrium stresses acting among self-propelled rods destabilize motility-induced phase separation and facilitate orientational ordering, thereby connecting the realms of scalar and vectorial active matter. Though the interaction potential is strictly apolar, both, polar and nematic order may emerge and even coexist. Accordingly, the symmetry of ordered states is a dynamical property in active matter. The presented framework may represent various systems including bacterial colonies, cytoskeletal extracts, or shaken granular media. Interacting self-propelled particles exhibit phase separation or collective motion depending on particle shape. A unified theory connecting these paradigms represents a major challenge in active matter, which the authors address here by modeling active particles as continuum fields.

Active constituents burn fuel to sustain individual motion, giving rise to collective effects that are not seen in systems at thermal equilibrium, such as phase separation with purely repulsive interactions. There is a great potential in harnessing the striking phenomenology of active matter to build novel controllable and responsive materials that surpass passive ones. Yet, we currently lack a systematic roadmap to predict the protocols driving active systems between different states in a way that is thermodynamically optimal. Equilibrium thermodynamics is an inadequate foundation to this end, due to the dissipation rate arising from the constant fuel consumption in active matter. Here, we derive and implement a versatile framework for the thermodynamic control of active matter. Combining recent developments in stochastic thermodynamics and nonequilibrium response theory, our approach shows how to find the optimal control for either continuous- or discrete-state active systems operating arbitrarily far from equilibrium. Our results open the door to designing novel active materials which are not only built to stabilize specific nonequilibrium collective states, but are also optimized to switch between different states at minimum dissipation.

We investigate motility-induced phase separation of active Brownian particles, which are modeled as purely repulsive spheres that move due to a constant swim force with freely diffusing orientation. We develop on the basis of power functional concepts an analytical theory for nonequilibrium phase coexistence and interfacial structure. Theoretical predictions are validated against Brownian dynamics computer simulations. We show that the internal one-body force field has four nonequilibrium contributions: (i) isotropic drag and (ii) interfacial drag forces against the forward motion, (iii) a superadiabatic spherical pressure gradient, and (iv) the quiet life gradient force. The intrinsic spherical pressure is balanced by the swim pressure, which arises from the polarization of the free interface. The quiet life force opposes the adiabatic force, which is due to the inhomogeneous density distribution. The balance of quiet life and adiabatic forces determines bulk coexistence via equality of two bulk state functions, which are independent of interfacial contributions. The internal force fields are kinematic functionals which depend on density and current but are independent of external and swim forces, consistent with power functional theory. The phase transition originates from nonequilibrium repulsion, with the agile gas being more repulsive than the quiet liquid.

Significance A long-standing challenge in statistical mechanics has been to quantify the nonequilibrium nature of active systems such as swarming bacteria, schooling fish, and flocking birds. The entropy production rate characterizes how far from equilibrium a system resides, while probability currents describe nonequilibrium transport processes that occur even at statistical steady state. Computation of these quantities has proved difficult, as they depend on the system’s unknown high-dimensional probability density. Here, we introduce a broadly applicable deep learning framework that fuses recent advances in generative modeling with stochastic thermodynamics, yielding access to this canonically intractable density. Applying the method to phase-separated active systems, we find that nonequilibrium currents and the entropy production rate are dominated by interfacial contributions, confirming recent theoretical predictions.

We demonstrate that deep learning techniques can be used to predict motility-induced phase separation (MIPS) in suspensions of active Brownian particles (ABPs) by creating a notion of phase at the particle level. Using a fully connected network in conjunction with a graph neural network we use individual particle features to predict to which phase a particle belongs. From this, we are able to compute the fraction of dilute particles to determine if the system is in the homogeneous dilute, dense, or coexistence region. Our predictions are compared against the MIPS binodal computed from simulation. The strong agreement between the two suggests that machine learning provides an effective way to determine the phase behavior of ABPs and could prove useful for determining more complex phase diagrams.

Via computer simulations, we study kinetics of pattern formation in a two-dimensional active matter system. Self-propulsion in our model is incorporated via the Vicsek-like activity, i.e., particles have the tendency of aligning their velocities with the average directions of motion of their neighbors. In addition to this dynamic or active interaction, there exists passive inter-particle interaction in the model for which we have chosen the standard Lennard-Jones form. Following quenches of homogeneous configurations to a point deep inside the region of coexistence between high and low density phases, as the systems exhibit formation and evolution of particle-rich clusters, we investigate properties related to the morphology, growth, and aging. A focus of our study is on the understanding of the effects of structure on growth and aging. To quantify the latter, we use the two-time order-parameter autocorrelation function. This correlation, as well as the growth, is observed to follow power-law time dependence, qualitatively similar to the scaling behavior reported for passive systems. The values of the exponents have been estimated and discussed by comparing with the previously obtained numbers for other dimensions as well as with the new results for the passive limit of the considered model. We have also presented results on the effects of temperature on the activity mediated phase separation.

Two hallmarks of nonequilibrium systems, from active colloids to animal herds, are agent motility and nonreciprocal interactions. Their interplay creates feedback loops that lead to complex spatiotemporal dynamics crucial to understand and control the nonlinear response of active systems. Here, we introduce a minimal model that captures these two features at the microscopic scale while admitting an exact hydrodynamic theory valid also in the fully nonlinear regime. Using statistical mechanics techniques, we exactly coarse-grain our nonreciprocal microscopic model into a fluctuating hydrodynamics and use dynamical systems insights to analyze the resulting equations. In the absence of motility, we find two transitions to oscillatory phases occurring via distinct mechanisms: a Hopf bifurcation and a saddle node on invariant circle bifurcation. In the presence of motility, this rigorous approach, complemented by numerical simulations, allows us to quantitatively assess the hitherto neglected impact of interspecies nonreciprocity on a paradigmatic transition in active matter: the emergence of collective motion. When nonreciprocity is weak, we show that flocking is accelerated and bands tend to synchronize with a spatial overlap controlled by nonlinearities. When nonreciprocity is strong, flocking is superseded by a chase and rest dynamical phase, where each species alternates between a chasing state, when they propagate, and a resting state, when they stand still. Phenomenological models with linear nonreciprocal couplings fail to predict the chase and rest phase, which illustrates the usefulness of our exact coarse-graining procedure. Finally, we demonstrate how fluctuations in finite systems can be harnessed to characterize microscopic nonreciprocity from macroscopic time-correlation functions, even in phases where nonreciprocal interactions do not affect the thermodynamic steady state.

In recent years, there has been considerable push toward the biomedical applications with active particles, which have great potential to revolutionize disease diagnostics and therapy. The direct penetration of active particles through the cell membrane leads to more efficient intracellular delivery than previously considered endocytosis processes but may cause membrane disruption. Understanding fundamental behaviors of cell membranes in response to such extreme impacts by active particles is crucial to develop active particle-based biomedical technologies and manage health and safety issues in this emerging field. Unfortunately, the physical principles underlying the nonequilibrium behaviors from endocytosis to direct penetration remain elusive, and experiments are challenging. Here, we present a computed dynamic phase diagram for transmembrane transport of active particles and identify four characteristic dynamic phases in endocytosis and direct penetration according to the particle activity and membrane tension. The boundaries dividing these phases are analytically obtained with theoretical models, elucidating the nonequilibrium physics and criteria for the transition between different phases. Furthermore, we numerically and experimentally show three distinct dynamic regimes related to the interplay between necking and wrapping during the endocytosis process of active particles, which strikingly contrast the regimes for passive particles. Overall, these findings could be useful for sharpening the understanding of basic principles underlying biological issues related to the safe and efficient biomedical applications of such emerging matters.

Active matter concerns many-body systems comproed of living or self-driven agents that collectively exhibit macroscopic phenomena distinct from conventional passive matter. Using Schwinger-Keldysh effective field theory, we develop a hydrodynamic framework for thermal active matter that accounts for energy balance, local temperature variations, and the ensuing stochastic effects. By modeling active matter as a driven open system, we show that the source of active contributions to hydrodynamics, violations of fluctuation-dissipation theorems, and detailed balance is rooted in the breaking of time-translation symmetry due to the presence of fuel consumption and an external environmental bath. In addition, our framework allows for nonequilibrium steady states that produce entropy, with a well-defined notion of steady-state temperature. We use our framework of active hydrodynamics to develop effective field theory actions for active superfluids and active nematics that offer a first-principle derivation of various active transport coefficients and feature activity-induced phase transitions. We also show how to incorporate temperature, energy, and noise in fluctuating hydrodynamics for active matter. Our work suggests a broader perspective on active matter that can leave an imprint across scales.

Nonequilibrium steady states of vibrated inelastic frictionless spheres are investigated in quasi-two-dimensional confinement via molecular dynamics simulations. The phase diagram in the density-amplitude plane exhibits a fluidlike disordered and an ordered phase with threefold symmetry, as well as phase coexistence between the two. A dynamical mechanism exists that brings about metastable traveling clusters and at the same time stable clusters with anisotropic shapes at low vibration amplitude. Moreover, there is a square bilayer state which is connected to the fluid by BKTHNY-type two-step melting with an intermediate tetratic phase. The critical behavior of the two continuous transitions is studied in detail. For the fluid-tetratic transition, critical exponents of γ[over ̃]=1.73, η_{4}≈1/4, and z=2.05 are obtained.

First-order nonequilibrium phase transitions observed in active matter, fluid dynamics, biology, climate science, and other systems with irreversible dynamics are challenging to analyze because they cannot be inferred from a simple free energy minimization principle. Rather the mechanism of these transitions depends crucially on the system's dynamics, which requires us to analyze them in trajectory space rather than in phase space. Here we consider situations where the path of these transitions can be characterized as the minimizer of an action, whose minimum value can be used in a nonequilibrium generalization of the Arrhenius law to calculate the system's phase diagram. We also develop efficient numerical tools for the minimization of this action. These tools are general enough to be transportable to many situations of interest, in particular when the fluctuations present in the microscopic system are non-Gaussian and its dynamics is not governed by the standard Langevin equation. As an illustration, first-order phase transitions in two spatially-extended nonequilibrium systems are analyzed: a modified Ginzburg-Landau equation with a chemical potential which is non-gradient, and a reaction-diffusion network based on the Schl\"ogl model. The phase diagrams of both systems are calculated as a function of their control parameters, and the paths of the transitions, including their critical nuclei, are identified. These results clearly demonstrate the nonequilibrium nature of the transitions, with differing forward and backward paths.

In this article, we investigate the impact of self-alignment and anti-self-alignment on collective phenomena in dense active matter. These mechanisms correspond to effective torques that align or anti-align a particle's orientation with its velocity, as observed in active granular systems. In the context of motility-induced phase separation (MIPS)-a non-equilibrium coexistence between a dense clustered phase and a dilute homogeneous phase-both self- and anti-self-alignment are found to suppress clustering. In particular, increasing self-alignment strength first leads to flocking within the dense cluster and eventually to the emergence of a homogeneous flocking phase. In contrast, anti-self-alignment induces a freezing phenomenon, progressively reducing particle speed until MIPS is suppressed and a homogeneous phase is recovered. These results are supported by scaling arguments and are amenable to experimental verification in high-density active granular systems exhibiting self- or anti-self-alignment.

Motility-induced phase separation (MIPS) is a distinctive phenomenon in active matter that arises from its inherent nonequilibrium nature. Despite recent progress in understanding MIPS in dry active systems, it has been debated whether MIPS can be observed in wet systems in which fluid-mediated hydrodynamic interactions (HIs) are present. We use theory and large-scale active fast Stokesian dynamics simulations of the so-called squirmer model to show that collision-induced pusher force dipoles, which are present even for the simplest neutral squirmers (stealth swimmers), destroy MIPS when HIs are included. Both rotational and translational HIs independently suppress phase separation: rotation by shortening a swimmer's persistence length (and thus reducing the swim pressure), and translation by a confinement-scale advective fluid flow. We further clarify that collisional dipoles between swimmers and boundaries can generate attractive flows that promote particle aggregation observed in some previous simulations and experiments. Finally, we show how to recover MIPS in fluidic environments by tuning the magnitude of the HIs through brushlike surface coatings on the active particles.

We investigate the effect of rotational inertia on the collective phenomena of underdamped active systems and show that the increase of the moment of inertia of each particle favors non-equilibrium phase coexistence, known as motility induced phase separation, and counteracts its suppression due to translational inertia. Our conclusion is supported by a non-equilibrium phase diagram (in the plane spanned by rotational inertial time and translational inertial time) whose transition line is understood theoretically through scaling arguments. In addition, rotational inertia increases the correlation length of the spatial velocity correlations in the dense cluster. The fact that rotational inertia enhances collective phenomena, such as motility induced phase separation and spatial velocity correlations, is strongly linked to the increase of rotational persistence. Moreover, large moments of inertia induce non-monotonic temporal (cross) correlations between translational and rotational degrees of freedom truly absent in non-equilibrium systems.

Chiral active matter widely exists in nature and exhibits rich dynamical behaviors. Among these, chiral active particles (CAPs) with alignment effects show collective motions such as orderly rotating droplets and distinct phase transitions under different chirality degrees. However, the underlying dynamical and thermodynamical mechanisms of the phase transitions in the CAP system are not quite clear. Here, by combining the nonequilibrium landscape-flux theory with the coarse-grained mapping method, we quantified the potential landscape and the flux field to reflect global driving forces of the CAP system, characterizing the number and location of the steady states. Moreover, we revealed that mean flux and entropy production rate are respectively the dynamical and thermodynamical origins for the nonequilibrium phase transition, further providing a practical tool to confirm the continuity of the phase transition and the phase boundary. Our findings may inspire the design of experimental CAPs and present a framework for investigating phase transition behaviors in other complex active systems. Chiral active matter, including biological swimmers such as E. coli and sperm cells, exhibits complex nonequilibrium phase transitions influenced by noise intensity and particle angular velocity. Here, the authors employ coarse-grained mapping and landscape-flux theory to such transitions, with a focus on the dynamical and thermodynamic origins, as well as the implications for time-reversal symmetry breaking of the system.

Time-reversal symmetry breaking and entropy production are universal features of nonequilibrium phenomena. Despite its importance in the physics of active and living systems, the entropy production of systems with many degrees of freedom has remained of little practical significance because the high dimensionality of their state space makes it difficult to measure. Here we introduce a local measure of entropy production and a numerical protocol to estimate it. We establish a connection between the entropy production and extractability of work in a given region of the system and show how this quantity depends crucially on the degrees of freedom being tracked. We validate our approach in theory, simulation, and experiments by considering systems of active Brownian particles undergoing motility-induced phase separation, as well as active Brownian particles and E.coli in a rectifying device in which the time-reversal asymmetry of the particle dynamics couples to spatial asymmetry to reveal its effects on a macroscopic scale.

Confining in space the equilibrium fluctuations of statistical systems with long-range correlations is known to result into effective forces on the boundaries. Here we demonstrate the occurrence of Casimir-like forces in the nonequilibrium context provided by flocking active matter. In particular, we consider a system of aligning self-propelled particles in two spatial dimensions that are transversally confined by reflecting or partially reflecting walls. We show that in the ordered flocking phase this confined active vectorial fluid is characterized by extensive boundary layers, as opposed to the finite ones usually observed in confined scalar active matter. Moreover, a finite-size, fluctuation-induced contribution to the pressure on the wall emerges, which decays slowly and algebraically upon increasing the distance between the walls. We explain our findings-which display a certain degree of universality-within a hydrodynamic description of the density and velocity fields.

We investigate the susceptibility of long-range ordered phases of two-dimensional dry aligning active matter to population disorder, taken in the form of a distribution of intrinsic individual chiralities. Using a combination of particle-level models and hydrodynamic theories derived from them, we show that while in finite systems all ordered phases resist a finite amount of such chirality disorder, the homogeneous ones (polar flocks and active nematics) are unstable to any amount of disorder in the infinite-size limit. On the other hand, we find that the inhomogeneous solutions of the coexistence phase (bands) may resist a finite amount of chirality disorder even asymptotically.

In active φ^{4} field theories the nonequilibrium terms play an important role in describing active phase separation; however, they are irrelevant, in the renormalization group sense, at the critical point. Their irrelevance makes the critical exponents the same as those of the Ising universality class. Despite their irrelevance, they contribute to a nontrivial scaling of the entropy production rate at criticality. We consider the nonequilibrium dynamics of a nonconserved scalar field φ (Model A) driven out-of-equilibrium by a persistent noise that is correlated on a finite timescale τ, as in the case of active baths. We perform the computation of the density of entropy production rate σ and we study its scaling near the critical point. We find that similar to the case of active Model A, and although the nonlinearities responsible for nonvanishing entropy production rates in the two models are quite different, the irrelevant parameter τ makes the critical dynamics irreversible.

Self-propelled particles that are subject to noise are a well-established generic model system for active matter. A homogeneous alignment field can be used to orient the direction of the self-propulsion velocity and to model systems like phoretic Janus particles with a magnetic dipole moment or magnetotactic bacteria in an external magnetic field. Computer simulations are used to predict the phase behavior and dynamics of self-propelled Brownian particles in a homogeneous alignment field in two dimensions. Phase boundaries of the gas-liquid coexistence region are calculated for various Péclet numbers, particle densities, and alignment field strengths. Critical points and exponents are calculated and, in agreement with previous simulations, do not seem to belong to the universality class of the 2D Ising model. Finally, the dynamics of spinodal decomposition for quenching the system from the one-phase to the two-phase coexistence region by increasing the Péclet number is characterized. Our results may help to identify parameters for optimal transport of active matter in complex environments.

Motility-induced phase separation (MIPS) is the hallmark of non-equilibrium phase transition in active matter. Here, by means of Brownian dynamics simulations, we determine the phase behavior and the critical point for phase separation induced by motility of a two-dimensional system of soft active Brownian particles, whose interaction is modeled by the generalized purely repulsive Weeks–Chandler–Andersen potential. We refer to this model as soft active Brownian particles. We determine and analyze the influence of particle softness on the MIPS and show that the liquid–gas coexistence region is wider, the softer the interparticle interactions becomes. Moreover, the critical value of the self-propulsion velocity at which diluted and dense phases start to coexist also increases; as a consequence, the softer the particle interaction is, the bigger self-propulsion velocities are needed in order to observe a MIPS.

Many active materials, such as bacteria and cells, are deformable. Deformability significantly affects their collective behaviors and movements in complex environments. Here, we introduce a two-dimensional deformable active vesicle (DAV) model to emulate cell-like deformable active matter, wherein the deformability can be continuously adjusted. We find that changes in deformability can induce phase separation of DAVs. The system can transition between a homogeneous gas state, a coexistence of gas and liquid, and a coexistence of gas and solid. The occurrence of deformation-induced phase separation is accompanied by nonmonotonic changes in effective concentration, particle size and shape. Moreover, the degree of deformability also impacts the motility and stress within the dense phase following phase separation. Our results offer new insights into the role of deformability in the collective behavior of active matter.

One of the key hallmarks of dense active matter in the liquid, supercooled, and solid phases is the so-called equal-time velocity correlations. Crucially, these correlations can emerge spontaneously, i.e., they require no explicit alignment interactions, and therefore represent a generic feature of dense active matter. This indicates that for a meaningful comparison or possible mapping between active and passive liquids one not only needs to understand their structural properties, but also the impact of these velocity correlations. This has already prompted several simulation and theoretical studies, though they are mostly focused on athermal systems and thus overlook the effect of translational diffusion. Here, we present a fully microscopic method to calculate nonequilibrium correlations in two-dimensional systems of thermal active Brownian particles (ABPs). We use the integration through transients formalism together with (active) mode-coupling theory and analytically calculate qualitatively consistent static structure factors and active velocity correlations. We complement our theoretical results with simulations of both thermal and athermal ABPs which exemplify the disruptive role that thermal noise has on velocity correlations.

A lattice model is used to study repulsive active particles at a planar surface. A rejection-free Kinetic Monte Carlo method is employed to characterize the wetting behaviour. The model predicts a motility-induced phase separation of active particles, and the bulk coexistence of dense liquid-like and dilute vapour-like steady states is determined. An "ensemble", with a varying number of particles, analogous to a grand canonical ensemble in equilibrium, is introduced. The formation and growth of the liquid film between the solid surface and the vapour phase is investigated. At constant activity, as the system is brought towards coexistence from the vapour side, the thickness of the adsorbed film exhibits a divergent behaviour regardless of the activity. This suggests a complete wetting scenario along the full coexistence curve.

By means of computer simulations, we have investigated the gas–solid phase separation of active Brownian particles (ABPs) under the confinement of two hard walls, distinct from the gas–liquid phase separation typically seen in bulk systems. Our results show that the distance (D) between the hard walls plays a crucial role. Increasing D may facilitate the formation of gas–solid phase separation perpendicular to the hard walls, while decreasing D may suppress such phase separation. Interestingly, when D is decreased further and the lateral system size is increased accordingly to maintain a constant volume, a new reoriented phase separation pattern in the system emerges, i.e., the gas–solid phase coexistence can be found in those layers parallel to the inner surfaces of two hard walls. These intriguing findings illustrate how ABPs can achieve simultaneous localization and crystallization under imposed boundary confinement, thereby fundamentally altering the pathway of phase separation. Also, such understanding may provide a valuable pathway for optimizing the design of systems full of active matters such as micro-robotics or targeted delivery platforms.

No abstract available

We demonstrate that the mechanically defined "isothermal" compressibility behaves as a thermodynamic-like response function for suspensions of active Brownian particles. The compressibility computed from the active pressure-a combination of the collision and unique swim pressures-is capable of predicting the critical point for motility induced phase separation, as expected from the mechanical stability criterion. We relate this mechanical definition to the static structure factor via an active form of the thermodynamic compressibility equation and find the two to be equivalent, as would be the case for equilibrium systems. This equivalence indicates that compressibility behaves like a thermodynamic response function, even when activity is large. Finally, we discuss the importance of the phase interface when defining an active chemical potential. Previous definitions of the active chemical potential are shown to be accurate above the critical point but breakdown in the coexistence region. Inclusion of the swim pressure in the mechanical compressibility definition suggests that the interface is essential for determining phase behavior.

Protein pattern formation is essential for the spatial organization of many intracellular processes like cell division, flagellum positioning, and chemotaxis. A prominent example of intracellular patterns are the oscillatory pole-to-pole oscillations of Min proteins in \textit{E. coli} whose biological function is to ensure precise cell division. Cell polarization, a prerequisite for processes such as stem cell differentiation and cell polarity in yeast, is also mediated by a diffusion-reaction process. More generally, these functional modules of cells serve as model systems for self-organization, one of the core principles of life. Under which conditions spatio-temporal patterns emerge, and how these patterns are regulated by biochemical and geometrical factors are major aspects of current research. Here we review recent theoretical and experimental advances in the field of intracellular pattern formation, focusing on general design principles and fundamental physical mechanisms.

Spatial organization of proteins in cells is important for many biological functions. In general, the nonlinear, spatially coupled models for protein-pattern formation are only accessible to numerical simulations, which has limited insight into the general underlying principles. To overcome this limitation, we adopt the setting of two diffusively coupled, well-mixed compartments that represents the elementary feature of any pattern -- an interface. For intracellular systems, the total numbers of proteins are conserved on the relevant timescale of pattern formation. Thus, the essential dynamics is the redistribution of the globally conserved mass densities between the two compartments. We present a phase-portrait analysis in the phase-space of the redistributed masses that provides insights on the physical mechanisms underlying pattern formation. We demonstrate this approach for several paradigmatic model systems. In particular, we show that the pole-to-pole Min oscillations in Escherichia coli are relaxation oscillations of the MinD polarity orientation. This reveals a close relation between cell polarity oscillatory patterns in cells. Critically, our findings suggest that the design principles of intracellular pattern formation are found in characteristic features in these phase portraits (nullclines and fixed points). These features are not uniquely determined by the topology of the protein-interaction network but depend on parameters (kinetic rates, diffusion constants) and distinct networks can give rise to equivalent phase portrait features.

We demonstrate that particles confined to two dimensions (2d) and subjected to a one-dimensional (1d) periodic potential exhibit a rich phase diagram, with both ``locked floating solids'' and smectic phases. The resulting phases and phase transitions are studied as a function of temperature and potential strength. We find reentrant melting as a function of the potential strength. Our results lead to universal predictions consistent with recent experiments on 2d colloids in the presence of a laser-induced 1d periodic potential.

The theory of pattern formation in reaction-diffusion systems is extended to the case of a directed network. Due to the structure of the network Laplacian of the scrutinised system, the dispersion relation has both real and imaginary parts, at variance with the conventional case for a symmetric network. It is found that the homogeneous fixed point can become unstable due to the topology of the network, resulting in a new class of instabilities which cannot be induced on undirected graphs. Results from a linear stability analysis allow the instability region to be analytically traced. Numerical simulations show that the instability can lead to travelling waves, or quasi-stationary patterns, depending on the characteristics of the underlying graph. The results presented here could impact on the diverse range of disciplines where directed networks are found, such as neuroscience, computer networks and traffic systems.

Phase separation in passive systems leads to uncontrolled droplet growth, limiting structural control in soft materials and cells. We identify a generic mechanism to arrest coarsening based on chemical interconversion between molecular species with different diffusivities. Sharp-interface theory and simulations show that when the faster-diffusing species becomes enriched inside droplets, composition gradients emerge that oppose mass influx. This transport asymmetry stabilizes droplet sizes even without interaction asymmetries, offering a minimal route to regulate structure formation in active emulsions.

The Min proteins from Escherichia coli can self-organize into traveling waves on supported lipid bilayers. In Proc. Natl. Acad. Sci. USA 109, 15283 (2012) we showed that these waves are guided along the boundaries of membrane patches. We introduced an effective two-dimensional model reproducing the observed patterns. In arXiv:1403.5934v1, Jacob Halatek and Erwin Frey contest the ability of our effective two-dimensional model to describe the dynamics of Min proteins on patterned supported lipid bilayers. We thank Halatek and Frey for their interest in our work and for again highlighting the importance of dimensionality and geometry for pattern formation by the Min proteins. Here we reply in detail to the objections by Halatek and Frey and show that (1) our effective two-dimensional model reproduces the observed patterns on isolated patches and that (2) a three-dimensional version of our model produces similar patterns on square patches.

Important cellular processes, such as cell motility and cell division, are coordinated by cell polarity, which is determined by the non-uniform distribution of certain proteins. Such protein patterns form via an interplay of protein reactions and protein transport. Since Turing's seminal work, the formation of protein patterns resulting from the interplay between reactions and diffusive transport has been widely studied. Over the last few years, increasing evidence shows that also advective transport, resulting from cytosolic and cortical flows, is present in many cells. However, it remains unclear how and whether these flows contribute to protein-pattern formation. To address this question, we use a minimal model that conserves the total protein mass to characterize the effects of cytosolic flow on pattern formation. Combining a linear stability analysis with numerical simulations, we find that membrane-bound protein patterns propagate against the direction of cytoplasmic flow with a speed that is maximal for intermediate flow speed. We show that the mechanism underlying this pattern propagation relies on a higher protein influx on the upstream side of the pattern compared to the downstream side. Furthermore, we find that cytosolic flow can change the membrane pattern qualitatively from a peak pattern to a mesa pattern. Finally, our study shows that a non-uniform flow profile can induce pattern formation by triggering a regional lateral instability.

Experimental studies of protein-pattern formation have stimulated new interest in the dynamics of reaction-diffusion systems. However, a comprehensive theoretical understanding of the dynamics of such highly nonlinear, spatially extended systems is still missing. Here we show how a description in phase space, which has proven invaluable in shaping our intuition about the dynamics of nonlinear ordinary differential equations, can be generalized to mass-conserving reaction-diffusion (McRD) systems. We present a comprehensive analysis of two-component McRD systems, which serve as paradigmatic minimal systems that encapsulate the core principles and concepts of the local equilibria theory introduced in the paper. The key insight underlying this theory is that shifting local (reactive) equilibria -- controlled by the local total density -- give rise to concentration gradients that drive diffusive redistribution of total density. We show how this dynamic interplay can be embedded in the phase plane of the reaction kinetics in terms of simple geometric objects: the reactive nullcline and the diffusive flux-balance subspace. On this phase-space level, physical insight can be gained from geometric criteria and graphical constructions. The effects of nonlinearities on the global dynamics are simply encoded in the curved shape of the reactive nullcline. In particular, we show that the pattern-forming `Turing instability' in McRD systems is a mass-redistribution instability, and that the features and bifurcations of patterns can be characterized based on regional dispersion relations, associated to distinct spatial regions (plateaus and interfaces) of the patterns. In an extensive outlook section, we detail concrete approaches to generalize local equilibria theory in several directions, including systems with more than two-components, weakly-broken mass conservation, and active matter systems.

Here we provide a thorough discussion of the model for Min protein dynamics proposed by Schweizer et al. [11]. The manuscript serves as supplementary document for our letter to the editor to appear in PNAS. Our analysis is based on the original COMSOL simulation files that were used for the publication. We show that all computational data in Schweizer et al. rely on exploitation of simulation artifacts and various unmentioned modifications of model parameters that strikingly contradict the experimental setup and experimental data. We find that the model neither accounts for MinE membrane interactions nor for any observed MinDE protein patterns. All conclusions drawn from the computational model are void. There is no evidence at all that persistent MinE membrane binding has any role in geometry sensing.

Intracellular protein patterns are described by (nearly) mass-conserving reaction-diffusion systems. While these patterns initially form out of a homogeneous steady state due to the well-understood Turing instability, no general theory exists for the dynamics of fully nonlinear patterns. We develop a unifying theory for wavelength-selection dynamics in (nearly) mass-conserving two-component reaction-diffusion systems independent of the specific mathematical model chosen. This encompasses both the dynamics of the mesa- and peak-shaped patterns found in these systems. Our analysis uncovers a diffusion- and a reaction-limited regime of the dynamics, which provides a systematic link between the dynamics of mass-conserving reaction-diffusion systems and the Cahn-Hilliard as well as conserved Allen-Cahn equations, respectively. A stability threshold in the family of stationary patterns with different wavelengths predicts the wavelength selected for the final stationary pattern. At short wavelengths, self-amplifying mass transport between single pattern domains drives coarsening while at large wavelengths weak source terms that break strict mass conservation lead to an arrest of the coarsening process. The rate of mass competition between pattern domains is calculated analytically using singular perturbation theory, and rationalized in terms of the underlying physical processes. The resulting closed-form analytical expressions enable us to quantitatively predict the coarsening dynamics and the final pattern wavelength. We find excellent agreement of these expressions with numerical results. The systematic understanding of the length-scale dynamics of fully nonlinear patterns in two-component systems provided here builds the basis to reveal the mechanisms underlying wavelength selection in multi-component systems with potentially several conservation laws.

Biological systems often consist of a small number of constituents and are therefore inherently noisy. To function effectively, these systems must employ mechanisms to constrain the accumulation of noise. Such mechanisms have been extensively studied and comprise the constraint by external forces, nonlinear interactions, or the resetting of the system to a predefined state. Here, we propose a fourth paradigm for noise constraint: self-organized resetting, where the resetting rate and position emerge from self-organization through time-discrete interactions. We study general properties of self-organized resetting systems using the paradigmatic example of cooperative resetting, where random pairs of Brownian particles are reset to their respective average. We demonstrate that such systems undergo a delocalization phase transition, separating regimes of constrained and unconstrained noise accumulation. Additionally, we show that systems with self-organized resetting can adapt to external forces and optimize search behavior for reaching target values. Self-organized resetting has various applications in nature and technology, which we demonstrate in the context of sexual interactions in fungi and spatial dispersion in shared mobility services. This work opens routes into the application of self-organized resetting across various systems in biology and technology.

In this paper we prove that positive weak solutions for quasilinear parabolic equations on bounded domains subject to homogenous Neumann boundary conditions becme classical and global under the unique condition that the reaction doesn't change sign after certain positive time. We apply this result to reaction diffusion systems and prove global existence of theirs positive weak solutions under the same condition on theirs reactions. The nonlinearities growth isn't taken in consideration. The proof is based on the maximum principle.

Shadow systems are an approximation of reaction-diffusion-type problems obtained in the infinite diffusion coefficient limit. They allow reducing complexity of the system and hence facilitate its analysis. The quality of approximation can be considered in three time regimes: (i) short-time intervals taking account for the initial time layer, (ii) long-time intervals scaling with the diffusion coefficient and tending to infinity for diffusion tending to infinity, and (iii) asymptotic state for times up to $T = \infty$. In this paper we focus on uniform error estimates in the long-time case. Using linearization at a time-dependent shadow solution, we derive sufficient conditions for control of the errors. The employed methods are cut-off techniques and $L^p$-estimates combined with stability conditions for the linearized shadow system. Additionally, we show that the global-in-time extension of the uniform error estimates may fail without stronger assumptions on the model linearization. The approach is presented on example of reaction-diffusion equations coupled to ordinary differential equations (ODEs), including classical reaction-diffusion system. The results are illustrated by examples showing necessity and applicability of the established conditions.

Recently, much interest has gained the numerical approximation of equations of the Spatial Segregation of Reaction-diffusion systems with m population densities. These problems are governed by a minimization problem subject to the closed but non-convex set. In the present work we deal with the numerical approximation of equations of stationary states for a certain class of the Spatial Segregation of Reaction-diffusion system with two population densities having disjoint support. We prove the convergence of the numerical algorithm for two competing populations with non-negative internal dynamics $f_i(x)\geq 0.$ At the end of the paper we present computational tests.

We consider a reaction-diffusion system where some components react and diffuse on the boundary of a region, while other components diffuse in the interior and react with those on the boundary through mass transport. We establish local well-posedness and global existence of solutions for these systems using classical potential theory and linear estimates for initial boundary value problems.

We consider a system of reaction-diffusion equations in a bounded interval of the real line, with emphasis on the metastable dynamics, whereby the time-dependent solution approaches its steady state in an asymptotically exponentially long time interval as the viscosity coefficient $\varepsilon>0$ goes to zero. To rigorous describe such behavior, we analyze the dynamics of solutions in a neighborhood of a one-parameter family of approximate steady states, and we derive an ODE for the position of the internal interfaces.

This is the first part of our study of inertial manifolds for the system of 1D reaction-diffusion-advection equations which is devoted to the case of Dirichlet or Neumann boundary conditions. Although this problem does not initially possess the spectral gap property, it is shown that this property is satisfied after the proper non-local change of the dependent variable. The case of periodic boundary conditions where the situation is principally different and the inertial manifold may not exist is considered in the second part of our study.

The Zubarev nonequilibrium statistical operator (NSO) method in Renyi statistics is discussed. The solution of $q$-parametrized Liouville equation within the NSO method is obtained. A statistical approach for a consistent description of reaction-diffusion processes in "gas-adsorbate-metal" system is proposed using the NSO method in Renyi statistics.

Lecture notes on elements of nonequilibrium statistical mechanics: (1) a characterization of the nonequilibrium condition, largely by contrast to equilibrium; (2) a retelling of some of the great performances of the more distant past, including the perspectives of Boltzmann and Onsager; and (3) more recent methods and concepts, from local detailed balance and the identification of entropy fluxes to dynamical fluctuation theory, and the importance of dynamical activity.

Using an analytically tractable lattice model for reaction-diffusion processes of hard-core particles we demonstrate that under nonequilibrium conditions phase coexistence may arise even if the system is effectively one-dimensional as e.g. in the channel system of some zeolites or in artificial optical lattices. In our model involving two species of particles a steady-state particle current is maintained by a density gradient between the channel boundaries and by the influence of an external driving force. This leads to the development of a fluctuating but always microscopically sharp interface between two domains of different densities which are fixed by the boundary chemical potentials. The internal structure of the interface becomes very simple for strong driving force. We calculate the drift velocity and diffusion coefficient of the interface in terms of the microscopic model parameters.

The reversible reactions like A+B <-> C in the many-component diffusive system affect the diffusive properties of the constituents. The effective conjugation of irreversible processes of different dimensionality takes place due to the stationarity in the system and can lead to essential increase of the resulting diffusive fluxes. The exact equations for the spatial concentration profiles of the components are difficult to treat analytically. We solve approximately the equations for the concentration profiles of the reaction-diffusion components in the spherical geometry in the application to the problem of the enhanced oxygen transfer through a biological membrane and to the mathematically similar problem of surface diffusion in a solid body. In the latter case the spherical geometry can be an adequate tool for describing the surface of a real solid body which can be modeled as a fractal object formed of sequences of spherical surfaces with different radii.

We study dynamics of pattern formation in systems belonging to class of reaction-Cattaneo models including persistent diffusion (memory effects of the diffusion flux). It was shown that due to the memory effects pattern seletion process are realized. We have found that oscillatory behavior of the radius of the adsorbate islands is governed by finite propagation speed. It is shown that stabilization of nano-patterns in such models is possible only by nonequilibrium chemical reactions. Oscillatory dynamics of pattern formation is studied in details by numerical simulations.

We develop a statistical toolbox for a quantitative model evaluation of stochastic reaction-diffusion systems modeling space-time evolution of biophysical quantities on the intracellular level. Starting from space-time data $X_N(t,x)$, as, e.g., provided in fluorescence microscopy recordings, we discuss basic modelling principles for conditional mean trend and fluctuations in the class of stochastic reaction-diffusion systems, and subsequently develop statistical inference methods for parameter estimation. With a view towards application to real data, we discuss estimation errors and confidence intervals, in particular in dependence of spatial resolution of measurements, and investigate the impact of misspecified reaction terms and noise coefficients. We also briefly touch implementation issues of the statistical estimators. As a proof of concept we apply our toolbox to the statistical inference on intracellular actin concentration in the social amoeba Dictyostelium discoideum.

Reaction-diffusion systems with mass dissipation are known to possess blow-up solutions in high dimensions when the nonlinearities have super quadratic growth rates. In dimension one, it has been shown recently that one can have global existence of bounded solutions if nonlinearities are at most cubic. For the cubic intermediate sum condition, i.e. nonlinearities might have arbitrarily high growth rates, an additional entropy inequality had to be imposed. In this article, we remove this extra entropy assumption completely and obtain global boundedness for reaction-diffusion systems with cubic intermediate sum condition. The novel idea is to show a non-concentration phenomenon for mass dissipating systems, that is the mass dissipation implies a dissipation in a Morrey space $\mathsf{M}^{1,δ}(Ω)$ for some $δ>0$. As far as we are concerned, it is the first time such a bound is derived for mass dissipating reaction-diffusion systems. The results are then applied to obtain global existence and boundedness of solutions to an oscillatory Belousov-Zhabotinsky system, which satisfies cubic intermediate sum condition but does not fulfill the entropy assumption. Extensions include global existence mass controlled systems with slightly-super cubic intermediate sum condition.

Modeling and simulation are transforming all fields of biology. Tools like AlphaFold have revolutionized structural biology, while molecular dynamics simulations provide invaluable insights into the behavior of macromolecules in solution or on membranes. In contrast, we lack effective tools to represent the dynamic behavior of the endomembrane system. Static diagrams that connect organelles with arrows are used to depict transport across space and time but fail to specify the underlying mechanisms. This static representation obscures the dynamism of intracellular traffic, freezing it in an immobilized framework. The intracellular transport of transferrin, a key process for cellular iron delivery, is among the best-characterized trafficking pathways. In this commentary, we revisit this process using a mathematical simulation of the endomembrane system. Our model reproduces many experimental observations and highlights the strong contrast between dynamic simulations and static illustrations. This work underscores the urgent need for a consensus-based minimal functional working model for the endomembrane system and emphasizes the importance of generating more quantitative experimental data -- including precise measurements of organelle size, volume, and transport kinetics -- practices that were more common among cell biologists in past decades.

Diffusion of transported particles in the intracellular medium is described by means of a generalized diffusion equation containing forces due to the cytoskeleton network and to the protein motors. We find that the enhanced diffusion observed in experiments depends on the nature of the force exerted by the protein motors and on parameters characterizing the intracellular medium which is described in terms of a generalized Debye spectrum for the noise density of states.

We study the dynamics of intracellular calcium oscillations in the presence of proteins that bind calcium on multiple sites and that are generally believed to act as passive calcium buffers in cells. We find that multisite calcium-binding proteins set a sharp threshold for calcium oscillations. Even with high concentrations of calcium-binding proteins, internal noise, which shows up spontaneously in cells in the process of calcium wave formation, can lead to self-oscillations. This produces oscillatory behaviors strikingly similar to those observed in real cells. In addition, for given intracellular concentrations of both calcium and calcium-binding proteins the regularity of these oscillations changes and reaches a maximum as a function noise variance, and the overall system dynamics displays stochastic coherence. We conclude that calcium-binding proteins may have an important and active role in cellular communication.

We model the spatiotemporal dynamics of cellular protein concentrations near membranes composed of different lipids using a three-variable continuum model for membrane-bound protein, cytosolic protein, and the local composition of a binary lipid membrane. The model contains two globally conserved quantities: the total protein content and the average fractions of the two lipid species. It combines a conserved reaction-diffusion model for protein dynamics with a Cahn-Hilliard equation for lipid demixing. Linear stability analysis of the homogeneous steady state and direct numerical simulations show that the lipid dynamics undergoes classical phase separation, whereas the protein dynamics exhibits oscillatory phase separation for intermediate total protein contents, associated with a long-wavelength instability and traveling domains. In parameter regions where both instabilities are present, we find multiscale patterns with larger-scale traveling and rotating protein domains coexisting with smaller-scale stationary lipid domains. In this regime, traveling protein domains coexist with arrested coarsening of stationary lipid domains above a critical coupling. We further show that the main instabilities and phase diagram are well captured by an extension of a recently proposed conserved FitzHugh-Nagumo model for non-reciprocal pattern formation. The extended model consists of two non-reciprocally coupled Cahn-Hilliard equations with different interface tensions, reflecting the distinct physical properties of lipids and proteins. This also explains the observed asymmetry between static lipid patterns and traveling protein patterns.

We study global-in-time behavior of the solution to a reaction-diffusion system with mass conservation, as proposed in the study of cell polarity, particularly, the second model of \cite{oi07}. First, we show global-in-time existence of solution with compact orbit and then we examine stability and instability of stationary solutions.}{Reaction diffusion system; mass conservation; cell polarity; global-in-time behavior; Lyapunov function.

This paper considers quadratic and super-quadratic reaction-diffusion systems for reversible chemistry, for which all species satisfy uniform-in-time $L^1$ a-priori estimates, for instance, as a consequence of suitable mass conservation laws. A new result on the global existence of classical solutions is proved in three and higher space dimensions by combining regularity and interpolation arguments in Bochner spaces, a bootstrap scheme and a weak comparison argument. Moreover, provided that the considered system allows for entropy entropy-dissipation estimates proving exponential convergence to equilibrium, we are also able to prove that solutions are bounded uniformly-in-time.

Mass-conserving reaction-diffusion (MCRD) systems are widely used to model phase separation and pattern formation in cell polarity, biomolecular condensates, and ecological systems. Numerical simulations and formal asymptotic analysis suggest that such models can support stationary patterns with sharp internal interfaces. In this work, we establish for a general class of bistable MCRD systems the existence of nonconstant radially symmetric stationary solutions with a single internal transition layer on an $N$-dimensional ball, for general spatial dimension $N$. Our approach incorporates the global mass constraint directly into a refined matched-asymptotic framework complemented by a uniform spectral/linear analysis. Beyond mere existence, our framework yields arbitrarily high-order asymptotic approximations of the constructed solutions together with quantitative uniform error estimates, which provides a quantitative higher-dimensional theory of transition-layer patterns in MCRD systems and a rigorous justification for their use in modeling phase separation and pattern formation in biological and ecological settings.

Mass conservation in chemical species appears in a broad class of reaction-diffusion systems (RDs) and is known to bring about coarsening of the pattern in chemical concentration. Recent theoretical studies on RDs with mass conservation (MCRDs) reported that the interfacial curvature between two states contributes to the coarsening process, reminiscent of phase separation phenomena. However, since MCRDs do not presuppose a variational principle, it is largely unknown whether description of surface tension is operative or not. In this study, we numerically and theoretically explore the coarsening process of patterns in MCRDs in two and three dimensions. We identify the parameter regions where the homogeneous steady state becomes stable, unstable, and metastable. In the unstable region, pattern formation is triggered by usual Turing instability, whereas in the metastable region, nucleation-growth-type pattern formation is observed. In the later stage, spherical droplet patterns are observed in both regions, where they obey a relation similar to the Young-Laplace law and coarsen following the evaporation-condensation mechanism. These results demonstrate that in the presence of a conserved variable, a physical quantity similar to surface tension is relevant to MCRDs, which provides new insight into molecular self-assembly driven by chemical reactions.

Reaction diffusion systems with Turing instability and mass conservation are studied. In such systems, abrupt decays of stripes follow quasi-stationary states in sequence. At steady state, the distance between stripes is much longer than that estimated by linear stability analysis at a homogeneous state given by alternative stability conditions. We show that there exist systems in which a one-stripe pattern is solely steady state for an arbitrary size of the systems. The applicability to cell biology is discussed.

On a two-dimensional circular domain, we analyze the formation of spatio-temporal patterns for a class of coupled bulk-surface reaction-diffusion models for which a passive diffusion process occurring in the interior bulk domain is linearly coupled to a nonlinear reaction-diffusion process on the domain boundary. For this coupled PDE system we construct a radially symmetric steady state solution and from a linearized stability analysis formulate criteria for which this base state can undergo either a Hopf bifurcation, a symmetry-breaking pitchfork (or Turing) bifurcation, or a codimension-two pitchfork-Hopf bifurcation. For each of these three types of bifurcations, a multiple time-scale asymptotic analysis is used to derive normal form amplitude equations characterizing the local branching behavior of spatio-temporal patterns in the weakly nonlinear regime. Among the novel aspects of this weakly nonlinear analysis are the two-dimensionality of the bulk domain, the systematic treatment of arbitrary reaction kinetics restricted to the boundary, the bifurcation parameters which arise in the boundary conditions, and the underlying spectral problem where both the differential operator and the boundary conditions involve the eigenvalue parameter. The normal form theory is illustrated for both Schnakenberg and Brusselator reaction kinetics, and the weakly nonlinear results are favorably compared with numerical bifurcation results and results from time-dependent PDE simulations of the coupled bulk-surface system. Overall, the results show the existence of either subcritical or supercritical Hopf and symmetry-breaking bifurcations, and mixed-mode oscillations characteristic of codimension-two bifurcations. Finally, the formation of global structures such as large amplitude rotating waves is briefly explored through PDE numerical simulations.

Several cellular activities, such as directed cell migration, are coordinated by an intricate network of biochemical reactions which lead to a polarised state of the cell, in which cellular symmetry is broken, causing the cell to have a well defined front and back. Recent work on balancing biological complexity with mathematical tractability resulted in the proposal and formulation of a famous minimal model for cell polarisation, known as the wave pinning model. In this study, we present a three-dimensional generalisation of this mathematical framework through the maturing theory of coupled bulk-surface semilinear partial differential equations in which protein compartmentalisation becomes natural. We show how a local perturbation over the surface can trigger propagating reactions, eventually stopped in a stable profile by the interplay with the bulk component. We describe the behavior of the model through asymptotic and local perturbation analysis, in which the role of the geometry is investigated. The bulk-surface finite element method is used to generate numerical simulations over simple and complex geometries, which confirm our analysis, showing pattern formation due to propagation and pinning dynamics. The generality of our mathematical and computational framework allows to study more complex biochemical reactions and biomechanical properties associated with cell polarisation in multi-dimensions.

We present an overview of phase field modeling of active matter systems as a tool for capturing various aspects of complex and active interfaces. We first describe how interfaces between different phases are characterized in phase field models and provide simple fundamental governing equations that describe their evolution. For a simple model, we then show how physical properties of the interface, such as surface tension and interface thickness, can be recovered from these equations. We then explain how the phase field formulation can be coupled to various active matter realizations and discuss three particular examples of continuum biphasic active matter: active nematic-isotropic interfaces, active matter in viscoelastic environments, and active shells in fluid background. Finally, we describe how multiple phase fields can be used to model active cellular monolayers and present a general framework that can be applied to the study of tissue behaviour and collective migration.

Large-scale collective behavior in suspensions of many particles can be understood from the balance of statistical forces emerging beyond the direct microscopic particle interactions. Here we review some aspects of the collective forces that can arise in suspensions of self-propelled active Brownian particles: wall forces under confinement, interfacial forces, and forces on immersed bodies mediated by the suspension. Even for non-aligning active particles, these forces are intimately related to a non-uniform polarization of particle orientations induced by walls and bodies, or inhomogeneous density profiles. We conclude by pointing out future directions and promising areas for the application of collective forces in synthetic active matter, as well as their role in living active matter.

These notes focus on the description of the phases of matter in two dimensions. Firstly, we present a brief discussion of the phase diagrams of bidimensional interacting passive systems, and their numerical and experimental measurements. The presentation will be short and schematic. We will complement these notes with a rather complete bibliography that should guide the students in their study of the development of this very rich subject over the last century. Secondly, we summarise very recent results on the phase diagrams of active Brownian disks and active dumbbell systems in two dimensions. The idea is to identify all the phases and to relate, when this is possible, the ones found in the passive limit with the ones observed at large values of the activity, at high and low densities, and for both types of constituents. Proposals for the mechanisms leading to these phases will be discussed. The physics of bidimensional active systems open many questions, some of which will be listed by the end of the Chapter.

It is common in the study of a dizzying array of soft matter systems to perform agent-based simulations of particles interacting via conservative and often short-ranged forces. In this context, well-established algorithms for efficiently computing the set of pairs of interacting particles have established excellent open-source packages to efficiently simulate large systems over long time scales -- a crucial consideration given the separation in time- and length-scales often observed in soft matter. What happens, though, when we think more broadly about what it means to construct a neighbor list? What if interactions are non-reciprocal, or if the "range" of an interaction is determined not by a distance scale but according to some other consideration? As the field of soft and active matter increasingly considers the properties of living matter -- from the cellular to the super-organismal scale -- these questions become increasingly relevant, and encourage us to think about new physical and computational paradigms in the modeling of active matter. In this chapter we examine case studies in the use of non-metric interactions.

Planktonic active matter represents an emergent system spanning different scales: individual, population and community; and complexity arising from sub-cellular and cellular to collective and ecosystem scale dynamics. This cross-scale active matter system responds to a range of abiotic (temperature, fluid flow and light conditions) and biotic factors (nutrients, pH, secondary metabolites) characteristic to the relevant ecosystems they are part of. Active modulation of cell phenotypes, including morphology, motility, and intracellular organization enable planktonic microbes to dynamically interact with other individuals and species; and adapt - often rapidly - to the changes in their environment. In this chapter, I discuss both traditional and contemporary approaches to study the dynamics of this multi-scale active matter system from a mechanistic standpoint, with specific references to their local settings and their ability to actively tune the behaviour and physiology, and the emergent structures and functions they elicit under natural ecological constraints as well as due to the shifting climatic trends.

A wide range of experimental systems including gliding, swarming and swimming bacteria, in-vitro motility assays as well as shaken granular media are commonly described as self-propelled rods. Large ensembles of those entities display a large variety of self-organized, collective phenomena, including formation of moving polar clusters, polar and nematic dynamic bands, mobility-induced phase separation, topological defects and mesoscale turbulence, among others. Here, we give a brief survey of experimental observations and review the theoretical description of self-propelled rods. Our focus is on the emergent pattern formation of ensembles of dry self-propelled rods governed by short-ranged, contact mediated interactions and their wet counterparts that are also subject to long-ranged hydrodynamic flows. Altogether, self-propelled rods provide an overarching theme covering many aspects of active matter containing well-explored limiting cases. Their collective behavior not only bridges the well-studied regimes of polar self-propelled particles and active nematics, and includes active phase separation, but also reveals a rich variety of new patterns.

Nonequilibrium phase transitions are routinely observed in both natural and synthetic systems. The ubiquity of these transitions highlights the conspicuous absence of a general theory of phase coexistence that is broadly applicable to both nonequilibrium and equilibrium systems. Here, we present a general mechanical theory for phase separation rooted in ideas explored nearly a half-century ago in the study of inhomogeneous fluids. The core idea is that the mechanical forces within the interface separating two coexisting phases uniquely determine coexistence criteria, regardless of whether a system is in equilibrium or not. We demonstrate the power and utility of this theory by applying it to active Brownian particles, predicting a quantitative phase diagram for motility-induced phase separation in both two and three dimensions. This formulation additionally allows for the prediction of novel interfacial phenomena, such as an increasing interface width while moving deeper into the two-phase region, a uniquely nonequilibrium effect confirmed by computer simulations. The self-consistent determination of bulk phase behavior and interfacial phenomena offered by this mechanical perspective provide a concrete path forward towards a general theory for nonequilibrium phase transitions.

Motility-induced phase separation (MIPS), the phenomenon in which purely repulsive active particles undergo a liquid-gas phase separation, is among the simplest and most widely studied examples of a nonequilibrium phase transition. Here, we show that states of MIPS coexistence are in fact only metastable for three-dimensional active Brownian particles over a very broad range of conditions, decaying at long times through an ordering transition we call active crystallization. At an activity just above the MIPS critical point, the liquid-gas binodal is superseded by the crystal-fluid coexistence curve, with solid, liquid, and gas all coexisting at the triple point where the two curves intersect. Nucleating an active crystal from a disordered fluid, however, requires a rare fluctuation that exhibits the nearly close-packed density of the solid phase. The corresponding barrier to crystallization is surmountable on a feasible timescale only at high activity, and only at fluid densities near maximal packing. The glassiness expected for such dense liquids at equilibrium is strongly mitigated by active forces, so that the lifetime of liquid-gas coexistence declines steadily with increasing activity, manifesting in simulations as a facile spontaneous crystallization at extremely high activity.

Gibbs' phase rule states that two-phase coexistence of a single-component system, characterized by an n-dimensional parameter-space, may occur in an n-1-dimensional region. For example, the two equilibrium phases of the Ising model coexist on a line in the temperature-magnetic-field phase diagram. Nonequilibrium systems may violate this rule and several models, where phase coexistence occurs over a finite (n-dimensional) region of the parameter space, have been reported. The first example of this behaviour was found in Toom's model [Toom,Geoff,GG], that exhibits generic bistability, i.e. two-phase coexistence over a finite region of its two-dimensional parameter space (see Section 1). In addition to its interest as a genuine nonequilibrium property, generic multistability, defined as a generalization of bistability, is both of practical and theoretical relevance. In particular, it has been used recently to argue that some complex structures appearing in nature could be truly stable rather than metastable (with important applications in theoretical biology), and as the theoretical basis for an error-correction method in computer science (see [GG,Gacs] for an illuminating and pedagogical discussion of these ideas).

We combine experiments, theory, and simulations to investigate the coexistence of nonequilibrium phases emerging from interacting colloidal particles that are electrokinetically propelled in a nematic liquid crystal solvent. We directly determine the mechanical pressure within the radial assemblies and measure a non-equilibrium equation of state for this athermal driven system. A generic model combines phoretic propulsion with the interplay between electrostatic effects and liquid-crystal-mediated hydrodynamics, which are effectively cast into a long-range interparticle repulsion, while elasticity plays a subdominant role. Simulations based on this model explain the observed collective organization process and phase coexistence quantitatively. Our colloidal assemblies provide an experimental test-bed to investigate the fundamental role of phoretic pressure in the organization of driven out-of-equilibrium matter.

A major challenge in the study of active matter lies in quantitative characterization of phases and transitions between them. We show how the entropy of a collection of active objects can be used to classify regimes and spatial patterns in their collective behavior. Specifically, we estimate the contributions to the total entropy from correlations between the degrees of freedom of position and orientation. This analysis pin-points the flocking transition in the Vicsek model while clarifying the physical mechanism behind the transition. When applied to experiments on swarming Bacillus subtilis with different cell aspect ratios and overall bacterial area fractions, the entropy analysis reveals a rich phase diagram with transitions between qualitatively different swarm statistics. We discuss physical and biological implications of these findings.

We investigate the effect of translational and rotational inertia on motility-induced phase separation in underdamped active dumbbells and identify the emergence of four distinct kinetic temperatures across the coexisting phases-unlike in overdamped systems. We find that the dilute, gas-like phase consistently exhibits a higher translational kinetic temperature than the dense, liquid-like phase, with this difference amplified by increasing the rotational inertia. Rotational kinetic temperatures display a similar trend, with the dense phase remaining colder than the dilute phase; however, in this case the temperature difference grows with translational inertia and activity, while becoming practically independent of rotational inertia. This counterintuitive behavior arises from the interplay of activity-driven collisions with both translational and rotational inertia in the coexisting phases. Our results highlight the critical role of translational and rotational inertia in shaping the kinetic temperature landscape of motility-induced phase separation and offer new insights into the nonequilibrium thermodynamics of active matter.

The glass transition, extensively studied in dense fluids, polymers, or colloids, corresponds to a dramatic evolution of equilibrium transport coefficients upon a modest change of control parameter, like temperature or pressure. A similar phenomenology is found in many systems evolving far from equilibrium, such as driven granular media, active and living matter. While many theories compete to describe the glass transition at thermal equilibrium, very little is understood far from equilibrium. Here, we solve the dynamics of a specific, yet representative, class of glass models in the presence of nonthermal driving forces and energy dissipation, and show that a dynamic arrest can take place in these nonequilibrium conditions. While the location of the transition depends on the specifics of the driving mechanisms, important features of the glassy dynamics are insensitive to details, suggesting that an `effective' thermal dynamics generically emerges at long time scales in nonequilibrium systems close to dynamic arrest.

Phase separation routinely occurs in both living and synthetic systems. These phases are often complex and distinguished by features including crystallinity, nematic order, and a host of other nonconserved order parameters. For systems at equilibrium, the phase boundaries that characterize these transitions can be straightforwardly determined through the framework of thermodynamics. The prevalence of phase separation in active and driven systems motivates the need for a genuinely nonequilibrium theory for the coexistence of complex phases. Here, we develop a dynamical theory of coexistence when both conserved and nonconserved order parameters are present, casting coexistence criteria into the familiar form of equality of state functions. Our theory generalizes thermodynamic notions such as the chemical potential and Gibbs-Duhem relation to systems out of equilibrium. While these notions may not exist for all nonequilibrium systems, we numerically verify their existence for a variety of systems by introducing the phenomenological Active Model C+. We hope our work aids in the development of a comprehensive theory of high-dimensional nonequilibrium phase diagrams.

Depinning and nonequilibrium transitions within sliding states in systems driven over quenched disorder arise across a wide spectrum of size scales ranging from atomic friction at the nanoscale, flux motion in type-II superconductors at the mesoscale, colloidal motion in disordered media at the microscale, and plate tectonics at geological length scales. Here we show that active matter or self-propelled particles interacting with quenched disorder under an external drive represents a new class of system that can also exhibit pinning-depinning phenomena, plastic flow phases, and nonequilibrium sliding transitions that are correlated with distinct morphologies and velocity-force curve signatures. When interactions with the substrate are strong, a homogeneous pinned liquid phase forms that depins plastically into a uniform disordered phase and then dynamically transitions first into a moving stripe coexisting with a pinned liquid and then into a moving phase separated state at higher drives. We numerically map the resulting dynamical phase diagrams as a function of external drive, substrate interaction strength, and self-propulsion correlation length. These phases can be observed for active matter moving through random disorder. Our results indicate that intrinsically nonequilibrium systems can exhibit additional nonequilibrium transitions when subjected to an external drive.

Active Brownian particles (ABPs) with pure repulsion is an ideal model to understand the effect of nonequilibrium on collective behaviors. It has long been established that activity can create effective attractions leading to motility-induced phase separation (MIPS), whose role is similar to that of (inverse) temperature in the simplest equilibrium system with attractive inter-particle interactions. Here, our theoretical analysis based on a kinetic theory of MIPS shows that a new type of activity-induced nonequilibrium vaporization is able to hinder the formation of dense phase when activity is large enough. Such nonequilibrium vaporization along with the activity-induced effective attraction thus lead to a MIPS reentrance. Numerical simulations verify such nonequilibrium effect induced solely by activity on phase behaviors of ABPs, and further demonstrate the dependence of MIPS on activity and the strength of inter-particle interaction predicted by our theoretical analysis. Our findings highlight the unique role played by the nonequilibrium nature of activity on phase behaviors of active systems, which may inspire deep insights into the essential difference between equilibrium and nonequilibrium systems.

The spatiotemporal oscillations of the Min proteins in the bacterium Escherichia coli play an important role in cell division. A number of different models have been proposed to explain the dynamics from the underlying biochemistry. Here, we extend a previously described discrete polymer model from a deterministic to a stochastic formulation. We express the stochastic evolution of the oscillatory system as a map from the probability distribution of maximum polymer length in one period of the oscillation to the probability distribution of maximum polymer length half a period later and solve for the fixed point of the map with a combined analytical and numerical technique. This solution gives a theoretical prediction of the distributions of both lengths of the polar MinD zones and periods of oscillations -- both of which are experimentally measurable. The model provides an interesting example of a stochastic hybrid system that is, in some limits, analytically tractable.