The healthy growth and maintenance of a biological system depends on the precise spatial organization of molecules within the cell through the dissipation of energy. Reaction–diffusion mechanisms can facilitate this organization, as can directional cargo transport orchestrated by motor proteins, by relying on specific protein interactions. However, transport of material through the cell can also be achieved by active processes based on non-specific, purely physical mechanisms, a phenomenon that remains poorly explored. Here, using a combined experimental and theoretical approach, we discover and describe a hidden function of the Escherichia coli MinDE protein system: in addition to forming dynamic patterns, this system accomplishes the directional active transport of functionally unrelated cargo on membranes. Remarkably, this mechanism enables the sorting of diffusive objects according to their effective size, as evidenced using modular DNA origami–streptavidin nanostructures. We show that the diffusive fluxes of MinDE and non-specific cargo couple via density-dependent friction. This non-specific process constitutes a diffusiophoretic mechanism, as yet unknown in a cell biology setting. This nonlinear coupling between diffusive fluxes could represent a generic physical mechanism for establishing intracellular organization.

Motivated by the wealth of experimental data recently available, we present a cellular-automaton-based modeling framework focussing on high-level cell functions and their concerted effect on cellular migration patterns. Specifically, we formulate a coarse-grained description of cell polarity through self-regulated actin organization and its response to mechanical cues. Furthermore, we address the impact of cell adhesion on collective migration in cell cohorts. The model faithfully reproduces typical cell shapes and movements down to the level of single cells, yet allows for the efficient simulation of confluent tissues. In confined circular geometries, we find that specific properties of individual cells (polarizability; contractility) influence the emerging collective motion of small cell cohorts. Finally, we study the properties of expanding cellular monolayers (front morphology; stress and velocity distributions) at the level of extended tissues.

Highlights d Endothelial cells communicate mechanically in a basement membrane material d A defined stiffness range, plasticity, and the presence of laminin are mandatory d Cells remodel the matrix forming stiff bridges, which precede cell protrusions d These processes are an organizing principle for vascular network formation

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A wealth of experimental data relating to the emergence of collective cell migration as one proceeds from the behavioral dynamics of small cohorts of cells to the coordinated migratory response of cells in extended tissues is now available. Integrating these findings into a mechanistic picture of cell migration that is applicable across such a broad range of system sizes constitutes a crucial step towards a better understanding of the basic factors that determine the emergence of collective cell motion. Here we present a cellular-automaton-based modeling framework, which focuses on the integration of high-level cell functions and their concerted effect on cellular migration patterns. In particular, we adopt a top-down approach to incorporate a coarse-grained description of cell polarity and its response to mechanical cues, and address the impact of cell adhesion on collective migration in cell groups. We demonstrate that the model faithfully reproduces typical cell shapes and movements down to the level of single cells, yet is computationally efficient enough to allow for the simulation of (currently) up to O(10 4 ) cells. To develop a mechanistic picture that illuminates the relationship between cell functions and collective migration, we present a detailed study of small groups of cells in confined circular geometries, and discuss the emerging patterns of collective motion in terms of specific cellular properties. Finally, we apply our computational model at the level of extended tissues, and investigate stress and velocity distributions, as well as front morphologies, in expanding cellular sheets.Cell movements range from uncoordinated ruffling of cell boundaries to the migration of single cells [1] to the collective motions of cohesive cell groups [2]. Singlecell migration enables cells to move towards and between tissue compartments -a process that plays an important role in the inflammation-induced migration of leukocytes [3]. One can distinguish between amoeboid and mesenchymal migration, which are characterized by widely different cell morphologies and adhesive interactions with their respective environments [4,5]. Cells may also form cohesive clusters and mobilize as a collective [6][7][8][9][10][11]. This last mode of cell migration is known to drive tissue remodelling during embryonic morphogenesis [12] and wound repair [13].Despite this broad diversity of migration modes, there appears to be a general consensus that all require (to varying degrees) the following factors: (i) Cell polarization, cytoskeletal (re)organization, and force generation driven by the interplay between actin polymerization and contraction of acto-myosin networks. (ii) Cell-cell cohesion and coupling mediated by adherens-junction proteins which are coupled to the cytoskeleton. (iii) Guidance by chemical and physical signals. The basic functionalities implemented by these different factors confer on cells the ability to generate forces, adhere (differentially) to each other and to a substrate, and respond to mechanical and chemic...

We consider a simple Markovian class of the stochastic Wilson-Cowan type models of neuronal network dynamics, which incorporates stochastic delay caused by the existence of a refractory period of neurons. From the point of view of the dynamics of the individual elements, we are dealing with a network of non-Markovian stochastic two-state oscillators with memory, which are coupled globally in a mean-field fashion. This interrelation of a higher-dimensional Markovian and lower-dimensional non-Markovian dynamics is discussed in its relevance to the general problem of the network dynamics of complex elements possessing memory. The simplest model of this class is provided by a three-state Markovian neuron with one refractory state, which causes firing delay with an exponentially decaying memory within the two-state reduced model. This basic model is used to study critical avalanche dynamics (the noise sustained criticality) in a balanced feedforward network consisting of the excitatory and inhibitory neurons. Such avalanches emerge due to the network size dependent noise (mesoscopic noise). Numerical simulations reveal an intermediate power law in the distribution of avalanche sizes with the critical exponent around −1.16. We show that this power law is robust upon a variation of the refractory time over several orders of magnitude. However, the avalanche time distribution is biexponential. It does not reflect any genuine power law dependence. belongs to statistical physics or system biophysics. Physical models such as SOC are especially important and helpful here.The Wilson and Cowan model [12] presents one of the well-established models of neuronal network dynamics [13]. It incorporates individual elements in a simplest possible fashion as two-state stochastic oscillators with one quiescent state and one excited state, and random transitions between these two states which are influenced by the mutual coupling among the network elements. The model has been introduced in the deterministic limit of huge amounts of coupled elements in complete neglect of the intrinsic mesoscopic noise and became immensely popular with the years [13], being used e.g. to describe neuronal oscillations in visual cortex within a mean-field approximation [1,14]. Recently, the previously neglected mesoscopic noise effects were incorporated in this model for a finite-size network [15,16]. Such a noise has been shown to be very important, in particular, for the occurrence of the critical avalanche dynamics [15] absent in the deterministic Wilson-Cowan model and also for the emergence of oscillatory noisy dynamics [16]. At first glance, such a noisy dynamics can look like a chaotic deterministic one. Deterministic chaos influenced by the noise can also be a natural feature of a higher-dimensional dynamics, beyond the original two-dimensional (2D) Wilson-Cowan mean field model. Indeed, deterministic chaos has been found in the brain dynamics some time ago [1,17]. However, it cannot be described within the memoryless Wilson-Cowan model because the...

From proteins to chromosomes, polymers fold into specific conformations that control their biological function. Polymer folding has long been studied with equilibrium thermodynamics, yet intracellular organization and regulation involve energy-consuming, active processes. Signatures of activity have been measured in the context of chromatin motion, which shows spatial correlations and enhanced subdiffusion only in the presence of adenosine triphosphate (ATP). Moreover, chromatin motion varies with genomic coordinate, pointing towards a heterogeneous pattern of active processes along the sequence. How do such patterns of activity affect the conformation of a polymer such as chromatin? We address this question by combining analytical theory and simulations to study a polymer subjected to sequence-dependent correlated active forces. Our analysis shows that a local increase in activity (larger active forces) can cause the polymer backbone to bend and expand, while less active segments straighten out and condense. Our simulations further predict that modest activity differences can drive compartmentalization of the polymer consistent with the patterns observed in chromosome conformation capture experiments. Moreover, segments of the polymer that show correlated active (sub)diffusion attract each other through effective long-ranged harmonic interactions, whereas anticorrelations lead to effective repulsions. Thus, our theory offers non-equilibrium mechanisms for forming genomic compartments, which cannot be distinguished from affinity-based folding using structural data alone. As a first step toward disentangling active and passive mechanisms of folding, we discuss a data-driven approach to discern if and how active processes affect genome organization.

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.