A self-propelled particle model is introduced to study cell sorting occurring in some living organisms. This allows us to evaluate the influence of intrinsic cell motility separately from differential adhesion with fluctuations, a mechanism previously shown to be sufficient to explain a variety of cell rearrangement processes. We find that the tendency of cells to actively follow their neighbors greatly reduces segregation time scales. A finite-size analysis of the sorting process reveals clear algebraic growth laws as in physical phase-ordering processes, albeit with unusual scaling exponents. DOI: 10.1103/PhysRevLett.100.248702 PACS numbers: 87.17.ÿd, 05.65.+b, 64.75.ÿg Regeneration after tissue dissociation and reaggregation of some species of sponges, sea urchins, and hydras has been investigated in a series of experiments [1]. These phenomena involve cell sorting: Initially mixed cells form clusters, similarly to domain growth processes in physics. To explain cell sorting, Steinberg [2] proposed the differential adhesion hypothesis (DAH), which postulates that local rearrangements depend on the adhesion and motility properties of the different types of cells involved.Experiments have verified some of the DAH postulates. Surface tension measurements in chicken embryonic tissues [3] and determination of adhesive forces between pairs of Hydra cells [4] confirmed the relative magnitudes necessary to favor tissue envelopment: Ectodermic cells are less cohesive than endodermic cells. It was also shown that random membrane ruffling induces effective cell motion [5]. However, the coherent component of cellular flow must also be considered, as in viscoelastic fluids [6]. Remarkable experiments on Hydra performed in two spatial dimensions by Rieu et al. [7,8] discriminate between random and coherent motion contributions to cell segregation, showing that, during aggregate rounding or sorting, endodermic cell dynamics is dominated by the coherent behavior of ectodermic cells. Also, short-range spatial correlations corresponding to parallel displacement of adjacent cells were observed. The diffusive properties of cells were also measured. In all cases, diffusion was found to be normal only beyond a trapping time during which cells keep the same neighbors. This time is smaller for endodermic cells immersed in the ectoderm.Cell-sorting phenomena, such as Hydra regeneration from random cellular aggregates, have been simulated in the seminal work by Graner and Glazier in 1992 [9], later extended by Hogeweg and co-workers [10] (hereafter the GGH model). In the GGH model, cells are represented on a site-labeled lattice. A connected group of sites with the same label stands for a cell, and an energy function accounts for surface tension between adjacent cells while keeping the cell sizes fluctuating around specified targets.Monte Carlo simulations showed that sorting may occur, with less adhesive cells engulfing the more adhesive ones. However, this type of approach cannot easily account for locally coherent active cell mo...

Self-propelled particles are used to simulate cell aggregates in a model considering homogeneous adhesion forces between cells and using only motility differences as segregation drivers. The tendency of cells to follow their neighbors is also included in the formulation. Three model variants are explored, and the conditions on which motility differences may produce segregation are mapped in parameter diagrams. The evolution of the order parameter measuring cell segregation is similar to those found by models based on differential adhesion. It is also found that, considering only velocity differences, the faster cells envelope the slower ones, which is opposite to the ordering observed in early experiments by Jones and co-workers [Jones, Evans, and Lee, Exp.

The evolutionary stability of cooperative traits, that are beneficial to other individuals but costly to their carrier, is considered possible only through the establishment of a sufficient degree of assortment between cooperators. Chimeric microbial populations, characterized by simple interactions between unrelated individuals, restrain the applicability of standard mechanisms generating such assortment, in particular when cells disperse between successive reproductive events such as happens in Dicyostelids and Myxobacteria. In this paper, we address the evolutionary dynamics of a costly trait that enhances attachment to others as well as group cohesion. By modeling cells as self-propelled particles moving on a plane according to local interaction forces and undergoing cycles of aggregation, reproduction and dispersal, we show that blind differential adhesion provides a basis for assortment in the process of group formation. When reproductive performance depends on the social context of players, evolution by natural selection can lead to the success of the social trait, and to the concomitant emergence of sizeable groups. We point out the conditions on the microscopic properties of motion and interaction that make such evolutionary outcome possible, stressing that the advent of sociality by differential adhesion is restricted to specific ecological contexts. Moreover, we show that the aggregation process naturally implies the existence of non-aggregated particles, and highlight their crucial evolutionary role despite being largely neglected in theoretical models for the evolution of sociality.

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Analysis of genome-wide expression data poses a challenge to extract relevant information. The usual approaches compare cellular expression levels relative to a pre-established control and genes are clustered based on the correlation of their expression levels. This implies that cluster definitions are dependent on the cellular metabolic state, eventually varying from one experiment to another. We present here a computational method that order genes on a line and clusters genes by the probability that their products interact. Protein–protein association information can be obtained from large data bases as STRING. The genome organization obtained this way is independent from specific experiments, and defines functional modules that are associated with gene ontology terms. The starting point is a gene list and a matrix specifying interactions. Considering the Saccharomyces cerevisiae genome, we projected on the ordering gene expression data, producing plots of transcription levels for two different experiments, whose data are available at Gene Expression Omnibus database. These plots discriminate metabolic cellular states, point to additional conclusions, and may be regarded as the first versions of ‘transcriptograms’. This method is useful for extracting information from cell stimuli/responses experiments, and may be applied with diagnostic purposes to different organisms.

We investigate the dynamical states of a two-dimensional network of Hindmarsh-Rose spiking neurons, in the vicinity of the current threshold where the single neuron becomes active. Each neuron is electrically coupled with neurons in its close neighborhood. The existence of multistable synchronization states is established and discussed. We also show that, provided adequate initial conditions, the collective behavior is able to keep the network in activity, even for current values far below the activity threshold of the single neuron. A phase diagram of the different network states is presented for a large interval of the coupling-current parameter space.

Cell migration is essential to cell segregation, playing a central role in tissue formation, wound healing, and tumor evolution. Considering random mixtures of two cell types, it is still not clear which cell characteristics define clustering time scales. The mass of diffusing clusters merging with one another is expected to grow as t^{d/d+2} when the diffusion constant scales with the inverse of the cluster mass. Cell segregation experiments deviate from that behavior. Explanations for that could arise from specific microscopic mechanisms or from collective effects, typical of active matter. Here we consider a power law connecting diffusion constant and cluster mass to propose an analytic approach to model cell segregation where we explicitly take into account finite-size corrections. The results are compared with active matter model simulations and experiments available in the literature. To investigate the role played by different mechanisms we considered different hypotheses describing cell-cell interaction: differential adhesion hypothesis and different velocities hypothesis. We find that the simulations yield normal diffusion for long time intervals. Analytic and simulation results show that (i) cluster evolution clearly tends to a scaling regime, disrupted only at finite-size limits; (ii) cluster diffusion is greatly enhanced by cell collective behavior, such that for high enough tendency to follow the neighbors, cluster diffusion may become independent of cluster size; (iii) the scaling exponent for cluster growth depends only on the mass-diffusion relation, not on the detailed local segregation mechanism. These results apply for active matter systems in general and, in particular, the mechanisms found underlying the increase in cell sorting speed certainly have deep implications in biological evolution as a selection mechanism.

Assuming the selectivity filter of KcsA potassium ion channel may exhibit quantum coherence, we extend a previous model by Vaziri and Plenio (2010 New J. Phys. 12 085001) to take into account Coulomb repulsion between potassium ions. We show that typical ion transit timescales are determined by this interaction, which imposes optimal input/output parameter ranges. Also, as observed in other examples of quantum tunneling in biological systems, the addition of moderate noise helps coherent ion transport.