ALT Neuroinflammation and glial scar formation in the central nervous system (CNS) emerge from coordinated signaling between neurons, astrocytes, oligodendrocytes and microglia. Conventional embryonic neuron-astrocyte-oligodendrocyte (NAO) TriCultures lack microglia and cannot model innate immune responses. Here, we introduce postnatal neuron-astrocyte-oligodendrocyte-microglia (NAOM) TetraCultures, in which resident microglia are retained from the original brain tissue and co-develop with other CNS cell populations from the onset of culture. Hippocampal and cortical cultures were maintained for 14 days in vitro and challenged with LPS, poly I or scratch injury. Only NAOM TetraCultures mounted robust, region- and stimulus-specific inflammatory responses. LPS induced marked TNF-αand IL-1βsecretion and increased IBA1 in hippocampal microglia, whereas poly I elicited IFN-βrelease without increasing IBA1. CD68 regulation differed between hippocampus and cortex, and microglial morphology showed

ALT Confined cell migration is governed by nuclear and cellular deformation. In this work, we formulate a two-dimensional mathematical framework in which the cell and nuclear membranes are represented as evolving closed surfaces governed by force-balance laws incorporating surface tension, bending elasticity, and external mechanical interactions. Nuclear mechanics depends on several factors, including chromatin organization and compaction, which are regulated by epigenetic modifications. Molecular regulation at the nuclear periphery is described by a surface reaction-diffusion system posed on the evolving nuclear surface. We apply this coupled framework to an experimentally characterized lung cancer cell model in which the balance between the enzymes SETDB1 and SUV39H1 regulates the peripheral deposition of H3K9me3. Using experimentally measured mechanical parameters, we represent control (Ctrl) and SETDB1 knockout (SETDB1 KO) conditions, which display distinct H3K9me3 distributions, espec

ALT Efferocytosis, the clearance of dying cells typically by macrophages, is essential for tissue homeostasis and the resolution of inflammation. Previous experiments by Ford et al. (Proc. R. Soc. B, 2019) showed that cannibalistic efferocytosis redistributes endogenous lipid from dying macrophages into the surviving population, but existing mathematical models do not reproduce the observed population dynamics and lipid distributions. Here, fifteen candidate models are compared, combining three mechanisms of apoptotic material uptake with five forms of the macrophage death rate. Model comparison is guided by the Akaike Information Criterion and qualitative agreement with the observed lipid distributions. Numerical solutions show that whole-cell uptake models predict internal maxima that are absent from the data, whereas nibbling uptake produces distributions that are too concentrated about their means. By contrast, intermediate "fragmented" uptake models provide substantially improved agre

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

ALT Agent-based models (ABMs) provide a natural framework for representing cell-level rules and spatial heterogeneity in bacterial infections, but their computational cost limits their use for macroscopic tissue-scale simulations and broad parameter exploration. We derive a deterministic coarse-grained description for a class of bacterial-infection ABMs in which immune cells and extracellular bacteria diffuse, immune cells ingest nearby bacteria, and intracellular bacterial loads evolve through prescribed birth and clearance processes. The first coarse-grained model is a semidiscrete reaction–diffusion system that retains a discrete internal state for each immune-cell bacterial load while representing cell and bacterial populations by continuum concentration fields. The key technical step is the derivation of state-dependent effective ingestion rates from the microscopic ABM parameters: these rates are obtained by solving an auxiliary diffusion problem around a single bacterium and computi

ALT The extracellular matrix (ECM) is a fibrous and dynamic network that plays a critical role in development, homeostasis, and disease. Cells both respond to and remodel the ECM, engaging in a mechanical reciprocity that shapes tissues. To study these interactions, computational models have been developed that simulate either ECM mechanics or cell behavior. The Cellular Potts Model (CPM) is a flexible cell-based framework that has been extended to many biological processes, including coupling to a discrete deformable fiber network. So far, however, this extension has only been applied in two dimensions, even though a three-dimensional setting is biologically more relevant. Here, we present a 3D hybrid framework that couples the CPM with a discrete and deformable representation of the ECM. This model enables explicit simulation of cell-induced ECM remodeling, including fiber reorientation and matrix densification. By incorporating contractile forces through static adhesion points, the mode

ALT Biased migration up chemical gradients and proliferation are fundamental drivers of collective invasion in several biological processes ranging from embryonic morphogenesis to cancer. Nonetheless, our understanding of how their interplay yields distinct invasion patterns remains incomplete. In this work, we propose a multiscale framework to systematically derive macroscopic hydrodynamic theories of cell invasion from a mesoscopic description of cells as biased self-propelled, interacting particles that proliferate. Our framework reveals how clump and stream invasion patterns emerge from the same kinetic equation under different asymptotic regimes of cell proliferation. Stream invasion is characteristic of cell populations in which proliferation balances cell motion. In contrast, clump invasion requires a separation of the hydrodynamic timescale of motion and the slower timescale of proliferation. By means of a multiple-scale approach, our analysis reveals that clump invasion is describ

ALT In bioreactor experiments, budding yeast can manifest stable metabolic oscillations. In some instances these oscillations involve the cell cycle and are associated with a dynamical phenomenon called temporal clustering. Since yeast divide asymmetrically, mother cells may be able to divide sooner than the smaller daughter cells. We show that asymmetric division plus positive feedback can result in stable temporal clustering. In this scenario, the cells self-organize into p clusters of mother cells and q clusters of daughter cells, p ≤ q. In simple numerical simulations of a population model with asymmetric division and positive feedback, we show that p:q periodic arrangements form spontaneously from random populations of cells. Our main result is that these structures can be stable.

ALT We develop a Bayesian framework for model comparison of second-order Langevin dynamics from position-only trajectories. While approximate increment likelihoods for nonlinear position-only inference have been formulated previously, a unified evidence-based framework for comparing multiple second-order models under positional observation has remained lacking. Here we address this problem by combining exact increment likelihoods for linear Gaussian models with a previously proposed approximate likelihood for nonlinear dynamics. Synthetic-data benchmarks show reliable recovery of the generating model at fine sampling intervals and progressive loss of identifiability under coarse temporal sampling. Application to Dictyostelium discoideum trajectories demonstrates that the statistically supported model depends strongly on temporal resolution. Moreover, the selected models reproduce key statistical properties of the experimental trajectories, providing additional support for the model-compari

ALT Long-range intercellular calcium (Ca2+) signaling coordinates biological processes ranging from fertilization to contraction and cell death. The classical model attributes this long-range propagation to rapid diffusion of inositol 1,4,5-trisphosphate (IP3) through gap junctions. However, recent evidence that IP3 diffuses far more slowly than previously believed, and that Ca2+ oscillations persist even when gap junctions are disassembled, indicates that an alternative mechanism must sustain long-range communication. Here we develop a computational model showing that local coupling between neighboring cells is sufficient to generate and propagate regenerative Ca2+ oscillations across a cell population without fast molecular diffusion. Each cell is treated as an oscillator whose intrinsic frequency is set by its local IP3 concentration through an IP3-dependent refractory period, and neighboring cells are coupled using a Kuramoto nearest-neighbor framework. In a dual-stiffness regime, cell

ALT Collective migration of epithelial monolayers emerges from the interplay between mechanical interactions and biochemical signalling. Here, we present a phenomenological mechanobiological framework linking cell-scale orientational interactions to tissue-scale mechanics. We distinguish reversible and irreversible head-on and glancing collisions, showing that reversible interactions store orientational mechanical energy while preserving collision geometry, whereas irreversible interactions dissipate energy and alter cell orientation. The balance between energy storage and dissipation governs collective migration, mechanical feedback, and density-dependent processes including cell jamming and live cell extrusion. These interactions regulate cell elasticity, contractility, and adhesion, thereby modifying epithelial surface tension and the effective viscoelastic response of the monolayer. We quantify these effects using orientational interaction potentials, an effective second virial coeffic

ALT Cancer progression is driven by the ability of cells with identical driver mutations to adopt biologically distinct adaptive phenotypes. Yet the population dynamics implied by intratumour phenotypic heterogeneity is poorly understood. Melanoma, a highly aggressive skin cancer, represents an excellent model to explore phenotype-switching, in part because phenotypic identity is conferred by melanocyte-associated transcription factor (MITF) activity. Here we develop and analyse a multiscale phenotype-structured PDE model for melanoma cell populations in the epidermis, progressing from subcellular MITF dynamics to well-mixed and radially resolved population models. Numerical solutions revealed that the model admits three distinct and stable long-term population behaviours: a slow-growing melanoma of proliferative cells and non-cycling, differentiated cells without invasive potential; a faster-propagating melanoma with an invasive core; and a rapidly growing melanoma with oscillatory core d

ALT Chemotaxis underlies the collective migration of cell populations in developmental processes and immune responses. While the theoretical investigation of single-cell-type collective chemotaxis has received considerable attention, heterogeneous chemotaxis involving multiple interacting cell types remains poorly understood. Here, we generalise a model of heterogeneous self-generated chemotaxis and analyse the resulting collective migration patterns. We show that coupled migration between two cell types gives rise to propagating terraces – coupled travelling fronts moving at different speeds. While a sensor-only population leads the migrating collective, a slower mixed sensor-consumer population follows. Our analysis reveals that these fronts are coupled via the dynamics of the self-generated chemoattractant gradients. We derive analytical expressions for the migration speeds of the two fronts just in term of model parameters and experimentally measurable quantities. Our analytical result

ALT Antibody productivity and glycosylation quality in CHO cultures arise from a dynamically evolving metabolic environment, yet models often work in isolation or at a single scale. Here, we present a multiscale mechanistic framework linking molecular, cellular, and process levels to predict how inputs shape bioprocess trajectories. The framework is grounded on a single-cell kinetic model that couples metabolic and glycosylation networks governing yield and critical quality attributes (CQAs). A stochastic single-cell model describes environment-dependent transitions among growth, production, and decline, capturing population heterogeneity. We further introduce cumulative variation in the oxygen uptake rate, integrating total metabolic adjustment over time, as a compact biomarker for predicting metabolic shifts. Unlike population-averaged approaches, the model propagates cell-resolved metabolic states (including ammonia-regulated Golgi pH, nucleotide sugar availability, manganese cofactors,

ALT Collective migration of heterogeneous cell populations is central to many biological and physiological processes, including development and immune response. Recent experimental and theoretical advances have shown how asymmetric interactions with self-generated chemical gradients shape the spatial distribution of distinct cell types within migrating collectives. However, the principles governing robust spatial organisation of heterogeneous cell populations remain poorly understood. Here, we use asymptotic analysis to systematically derive a nonlinear analytical theory for heterogeneous cell collectives guided by self-generated chemotaxis. Our theory disentangles how heterogeneity in cell diffusivity, chemoattractant consumption, and chemotactic sensitivity shape the density profiles of migrating heterogeneous collectives, revealing four distinct dynamical behaviours that together capture all possible regimes. We calibrate our framework to experimental data on the co-migration of dendrit

ALT We study the mean first-passage time of a random walker to a small absorbing target at the center of a two-dimensional annulus with a specularly reflecting outer boundary. The problem is motivated by natural killer cell migration toward a target cancer cell, where the goal is to quantify how long it takes immune cells to reach the target and how search efficiency depends on directional persistence and chemotactic bias. Cell motion is modeled as a velocity-jump process. We first consider a correlated random walk with a von Mises turning kernel, with a concentration parameter controlling directional persistence. We then extend the model to a biased correlated random walk using a phase-shifted turning kernel that represents preferential motion, for example following a concentration gradient. Our analysis combines closed-form benchmarks for simple and biased random walks, Fourier-mode reductions of the transport equations for the correlated and biased correlated models, and a fast-turning

ALT Hereditary stomatocytosis (HS) comprises red blood cell (RBC) disorders characterized by cup-shaped erythrocytes that respond oppositely to splenectomy: curative in overhydrated HS (OHS) but potentially thrombogenic in dehydrated HS (DHS/xerocytosis). This paradox persists because RBC biomechanics is governed by partly independent parameters–shear modulus, bending rigidity, surface-to-volume ratio (S/V), and cytoplasmic viscosity–that existing assays capture only piecemeal. Here we combine dissipative particle dynamics (DPD) simulations with microfluidic imaging to construct a control discocyte and three stomatocyte models (ST-RBC1-3) at fixed membrane area and decreasing volume (109.7, 101.5, 89.8 fL), spanning the OHS-to-DHS range. Tracing this parameter set through five mechanically orthogonal assays, we find that interendothelial-slit (IES) traversal is geometry-dominated: overhydrated ST-RBC1 requires an order of magnitude higher critical pressure than healthy RBCs, whereas dehydr

ALT Single-cell-resolution spatial transcriptomics profiles gene expression at cellular locations in native tissues, yet accurate cell-type annotation remains challenging: imaging-based platforms are constrained by targeted gene panels, whereas sequencing-based platforms often suffer from sparse molecular capture and dropout. Reliable transfer of cell-type labels from single-cell RNA sequencing references is therefore critical for interpreting targeted and sparse spatial datasets. Here, we present SpCAST, a Kolmogorov–Arnold network-based framework for reference-guided spatial transcriptomics analysis. SpCAST captures nonlinear mappings between reference and spatial expression profiles and uses feature attribution to prioritize genes supporting cell-type predictions. Within a unified framework, SpCAST performs cell-type label transfer, spatial gene-expression reconstruction and marker-gene candidate prioritization. We benchmarked SpCAST on 53 datasets comprising 413,376 spatial cells acros

ALT Turing patterns are a cornerstone of biological self-organization, yet their emergence typically requires finely tuned parameters occupying narrow regions of high-dimensional space. This poses a fundamental challenge: how can evolving biological systems reliably find and exploit such rare conditions? In this work, we propose that common biochemical limit cycles, such as those arising from genetic feedback loops, can act as natural explorers of Turing space. By coupling a reaction-diffusion system to an orbit that modulates some of its parameters, we show that the system can dynamically sweep through Turing-permissive regimes and generate transient spatial patterns. We use an entropy-based measure in Fourier space to quantify pattern formation and demonstrate how cycles enhance the detectability and robustness of Turing islands. We further explore how coupling to positional gradients increases reproducibility, suggesting a route from oscillatory dynamics to stable developmental programs

ALT The striking variety of macroscopic morphologies displayed by bacterial colonies depends on microscopic environmental and behavioural details in a manner that is currently not well understood. A surprising example is sibling inhibition, whereby isogenic bacterial colonies spreading in soft agar hydrogels tend to avoid each other and form sharp demarcation lines when growing nearby. Here we investigate this effect with the common pathogen Pseudomonas aeruginosa, by combining quantitative density measurements with a minimal biophysical model. Our results show that the phenomenon does not depend on gel compression, lethal inhibition or quorum sensing-dependent cell communication. Instead, colony separation is driven by localised nutrient depletion through a dynamic feedback between growth and motility. The model, which is calibrated using experimental data, captures key observations including the dependence of inhibition strength on the initial nutrient concentration. This work establishe