Biology subjects

Edelstein-Keshet, L.

Publications and source records attributed to Edelstein-Keshet, L..

7 recordsLinked to original sources

bioRxiv2026

Mechanochemical Feedback Enables Efficient Navigation in Complex Chemical Gradients

Neutrophils move through narrow pores, convoluted channels, and tight spaces in tissue to find infection sites. Their ability to sense weak chemical gradients, undergo directed motion, and solve such path-finding problems rests on internal GTPase signaling circuits that control the front protrusion and rear retraction of the cell. Here we explore several variants of known core polarity circuits, with local and long-ranged negative feedback, including inhibitor downstream of Rac, Rac-Rho antagonism, and effects of membrane tension. The resulting reaction-diffusion (RD) equations for Rac and Rho are then used to simulate protrusion-retractions along the edge of a simulated motile cell. We visualize how cells navigate through narrow tracks with sharp corners and weak chemical gradients in 2D. Our metrics for cell performance include polarity initiation, wall-collision intensity, and track completion. In this way, we expose how Rac and Rho, together with their immediate down and upstream components can fine-tune neutrophil motility through complex environments. Author SummaryWhite blood cells, attracted to sites of infection, migrate through complex tissues to find their target. Such movement requires a balance between robust polarity in one direction versus flexibility in response to spatial cues such as obstacles and sharp turns. Here we use mathematical modeling to explore known intracellular circuits that regulate front protrusion and rear retraction in directed cell migration. We test several such circuits in simulations of cells moving along zigzag tracks with sharp turns. We demonstrate that a basic cell polarity circuit, on its own, has limited success, since cells tend to get trapped in sharp corners. Known modulators of this core, which add local negative feedback, mutual front-back antagonism, and long-range feedback from membrane tension, improve cell performance. A cell with the full front-back-membrane tension regulatory circuit avoids delays due to traps and obstacle collisions, and moves swiftly through a convoluted passage to its target site.

cell biology↗

bioRxiv2025

Predicting and Controlling Collective Fate in Multicellular Systems

Collective behavior is a defining property of multicellular systems, where coordinated outcomes emerge from local cell-cell interactions. Yet the quantitative rules linking single-cell decision-making to tissue-scale organization remain poorly resolved. Here, we develop a quantitative framework that defines an order parameter predicting when initially disordered colonies undergo a transition to ordered fate alignment and when minimal, localized inputs can redirect their collective state. This analysis reveals a distinct control regime in which multicellular assemblies become susceptible to a single engineered "guide" cell. We validate these predictions by introducing guide cells that integrate into unperturbed colonies and redirect fate patterns within the theoretically defined control windows. Together, these results connect single-cell decision rules to emergent tissue-level organization and establish a generalizable biological control strategy in which a minority engineered subset can reliably redirect the developmental trajectory of a much larger multicellular population. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=65 SRC="FIGDIR/small/693804v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@37baeforg.highwire.dtl.DTLVardef@d1d86aorg.highwire.dtl.DTLVardef@d7375eorg.highwire.dtl.DTLVardef@1741e9a_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOSummary figure:C_FLOATNO Emergence, Scaling, and Control of Multicellular Collective Order. (A) Cell-number-dependent collective order. Sparse colonies lack coordination and yield disordered fate distributions; dense colonies exhibit coordinated, ordered outcomes. (B) Collective order rises with colony compactness ({propto} cell density) and collapses across colony radii R onto a single curve. (C) Once coordination emerges, a single "guide cell" can steer fate-analogous to a sheepdog guiding a flock. The herding efficacy exhibits a biphasic window versus cell number. C_FIG

systems biology↗

bioRxiv2025

Investigating Local Negative Feedback of Rac Activity by Mathematical Models and Cell Motility Simulations

For polarization and directed migration, cells use a combination of local positive feedback and long-range inhibition. We have previously used mathematical models to show the ability of this core circuit to regulate directed cell movement. However, this wave pinning model lacks important additional feedback circuits, including the recently demonstrated local negative feedback from Town and Weiner. Here we extend our models to investigate the consequences of this additional link on cell physiology. We model responses of neutrophil-like HL-60 cells to spatially-controlled optogenetic stimulation of PI3K, leading (via PIP3) to Rac activity. We sequentially build up and investigate partial differential equation (PDE) models of the key Rac, Rac-Inhibitor, and PIP3-Rac-Inhibitor circuits. We fit model parameters to temporal and spatial (cell trajectory) data. Cell shapes, motility, and responses to stimuli are modeled in 2D cell-based simulations, with PDEs for Rac and the other regulatory components solved along the cell edge. We demonstrate that the ability of modeled cells to respond to temporal as well as spatial features of guidance cues depends on the addition of the local negative feedback circuit. Furthermore, the local Rac inhibitor improves the ability of modeled cells to respond to noisy or dynamic extracellular gradients. Our work demonstrates how local negative feedback enhances dynamic polarity and gradient sensing in migratory cells.

cell biology↗

bioRxiv2021

A Multiscale computational model of YAP signaling in epithelial fingering behaviour

In epithelial-mesenchymal transition (EMT), cells organized into sheets break away and become motile mesenchymal cells. EMT plays a crucial role in wound healing, embryonic development, and cancer metastasis. Intracellular signaling in response to mechanical, topographic, or chemical stimuli can promote EMT. We present a multiscale model for EMT downstream of the protein YAP, which suppresses the cell-cell adhesion protein E-cadherin and activates the GTPase Rac1 that enhances cell migration. We first propose an ODE model for YAP/Rac1/E-cadherin interactions. The model dynamics are bistable, accounting for motile loose cells as for adherent slower cells. We implement this model in a cellular Potts model simulation of 2D wound-healing using the open source platform Morpheus. We show that, under suitable stimuli (depicting topographic cues) the sheet exhibits finger-like projections and EMT. Morphological, as well as quantitative differences in YAP levels as well as cell speed across the sheet are consistent with preexisting experimental observations of epithelial sheets grown on topographic features in vitro. The simulation is also consistent with experiments that knockdown or over-express YAP, inhibit Rac1, or block E-cadherin. SIGNIFICANCEIn normal wound-healing, cell in an epithelium divide, grow, and migrate so as to seal a gap. In some pathological states, epithelial-mesenchymal transition (EMT) can lead to abnormal morphology, including fingering, breakage of single cells or multicellular clusters from the sheet edge. The mechanochemical control of this behaviour by cell signaling circuits (YAP, Rac1, and E-cadherin) reveals how the competition between cell adhesion and cell migration contributes to the process. We use the open-source computational platform Morpheus to investigate a multiscale model for the interactions of the proteins inside cells and the resulting morphology of the cell sheet. Results are consistent with experimental results in the literature.

cell biology↗

bioRxiv2019

A methodology for morphological feature extraction and unsupervised cell classification

Cell morphology is an important indicator of cell state, function, stage of development, and fate in both normal and pathological conditions. Cell shape is among key indicators used by pathologists to identify abnormalities or malignancies. With rapid advancements in the speed and amount of biological data acquisition, including images and movies of cells, computer-assisted identification and analysis of images becomes essential. Here, we report on techniques for recognition of cells in microscopic images and automated cell shape classification. We illustrate how our unsupervised machine-learning-based approach can be used to classify distinct cell shapes from a large number of microscopic images.\n\nTechnical AbstractWe develop a methodology to segment cells from microscopy images and compute quantitative descriptors that characterize their morphology. Using unsupervised techniques for dimensionality reduction and density-based clustering, we perform label-free cell shape classification. Cells are identified with minimal user input using mathematical morphology and region-growing segmentation methods. Physical quantities describing cell shape and size (including area, perimeter, Feret diameters, etc.) are computed along with other features including shape factors and Hus image moments.\n\nCorrelated features are combined to obtain a low-dimensional (2-D or 3-D) embedding of data points corresponding to individual segmented cell shapes. Finally, a hierarchical density-based clustering algorithm (HDBSCAN) is used to classify cells. We compare cell classification results obtained from different combinations of features to identify a feature set that delivers optimum classification performance for our test data consisting of phase-contrast microscopy images of a pancreatic-cancer cell line, MIA PaCa-2.

bioinformatics↗

bioRxiv2019

From Energy to Cellular Force in the Cellular Potts Model

Single and collective cell dynamics, cell shape changes, and cell migration can be conveniently represented by the Cellular Potts Model, a computational platform based on minimization of a Hamiltonian while permitting stochastic fluctuations. Using the fact that a force field is easily derived from a scalar energy (F = -{nabla}H), we develop a simple algorithm to associate effective forces with cell shapes in the CPM. We display the predicted forces for single cells of various shapes and sizes (relative to cell rest-area and cell rest-perimeter). While CPM forces are specified directly from the Hamiltonian on the cell perimeter, we infer internal forces using interpolation, and refine the results with smoothing. Predicted forces compare favorably with experimentally measured cellular traction forces. We show that a CPM model with internal signaling (such as Rho-GTPase-related contractility) can be associated with retraction-protrusion forces that accompany cell shape changes and migration. We adapt the computations to multicellular systems, showing, for example, the forces that a pair of swirling cells exert on one another, demonstrating that our algorithm works equally well for interacting cells. Finally, we show forces associated with the dynamics of classic cell-sorting experiments in larger clusters of model cells.\n\nAuthor summaryCells exert forces on their surroundings and on one another. In simulations of cell shape using the Cellular Potts Model (CPM), the dynamics of deforming cell shapes is traditionally represented by an energy-minimization method. We use this CPM energy, the Hamiltonian, to derive and visualize the corresponding forces exerted by the cells. We use the fact that force is the negative gradient of energy to assign forces to the CPM cell edges, and then extend the results to interior forces by interpolation. We show that this method works for single as well as multiple interacting model cells, both static and motile. Finally, we show favorable comparison between predicted forces and real forces measured experimentally.

cell biology↗

bioRxiv2017

A Rho-GTPase based model explains spontaneous collective migration of neural crest cell clusters

We propose a model to explain the spontaneous collective migration of neural crest cells in the absence of an external gradient of chemoattractants. The model is based on the dynamical interaction between Rac1 and RhoA that is known to regulate the polarization, contact inhibition and co-attraction of neural crest cells. Coupling the reaction-diffusion equations for active and inactive Rac1 and RhoA on the cell membrane with a mechanical model for the overdamped motion of membrane vertices, we show that co-attraction and contact inhibition cooperate to produce persistence of polarity in a cluster of neural crest cells by suppressing the random onset of Rac1 hotspots that may mature into new protrusion fronts. This produces persistent directional migration of cell clusters in corridors. Our model confirms a prior hypothesis that co-attraction and contact inhibition are key to spontaneous collective migration, and provides an explanation of their cooperative working mechanism in terms of Rho GTPase signaling. The model shows that the spontaneous migration is more robust for larger clusters, and is most efficient in a corridor of optimal confinement.

developmental biology↗