Biology subjects

Pearce, P.

Publications and source records attributed to Pearce, P..

12 recordsLinked to original sources

bioRxiv2026

Clogging of particle suspensions in networks

In many biological, biomedical and industrial systems, particles are transported via fluids through confined networks, in which clogging can disrupt function. However, we lack a predictive theoretical framework that couples particle transport, suspension rheology and network flow resistance. Here, we develop a model and solution algorithm for particle suspension flow in networks based on vessel-level continuum modelling and particle distribution at nodes connecting vessels. We apply the model to study transport of dense particle suspensions in minimal and physiological biological networks. A key feature of our model is the coupling between particle volume fraction and particle flux: in line with the physics of dense suspensions, each network branch, or vessel, possesses a local carrying capacity for particle transport at an intermediate particle fraction between zero and the maximum packing fraction. If this flux capacity is reached, the vessel becomes flux-limited and particles can accumulate in upstream branches, causing them to enter a high-particle-fraction, high-resistance state that we refer to as 'clogged'. We show that these vessel flux limitations lead to network-level redistribution of particles, which can cause widespread clogging and emergent network-scale heterogeneity. By varying network topology, we find that in some regimes increasing network connectivity does not improve transport: paradoxically, additional pathways can promote clogging and reduce network-level particle flux, analogous to classic results in traffic flow networks. Our results provide a minimal mechanistic framework that links suspension physics, network topology, and transport failure in complex flow networks.

biophysics↗

bioRxiv2026

ChemoTrack: A comprehensive dataset linking single-cell migration trajectories to precisely defined chemotactic signals

Chemotaxis drives cell migration in processes ranging from wound healing to embryonic development and cancer metastasis, yet its quantitative understanding remains limited because responding cells change and degrade attractant gradients, and existing datasets are too small and imprecise to capture stochastic behaviour. We present ChemoTrack, a publicly accessible resource comprising 2 million measurements from 500,000 migration tracks, in which the chemoattractant gradient and concentration experienced by every cell at the time of observation are precisely determined. The dataset includes microscopy images and trajectories spanning a full range of biologically relevant chemotactic conditions. Analysis shows that cells steer according to absolute differences in active receptor number, not fractional receptor occupancy, and maximal chemotaxis is not predicted by half-maximal receptor occupancy. By combining scale, precision and accessibility, ChemoTrack shifts quantitative description of eukaryotic chemotaxis from experimental conditions to the instantaneous chemical signal experienced by individual cells, enabling future mathematical and mechanistic analyses.

Cell Biology↗

bioRxiv2025

Intermittent attachments form three-dimensional cell aggregates with emergent fluid properties

In living systems across developmental and cancer biology, populations of cells on surfaces organize themselves into aggregates that mediate function and disease. Recent experimental studies have identified that such aggregates can have emergent fluid-like properties such as surface tension, yet the physical origin of these properties is not clear. Here, we develop a minimal cell-based model in which cell-cell and cell-substrate interactions are governed by active intermittent attachments. We explain the transition of cells from a dilute population to a dense aggregate, and quantify the emergent material properties underpinning this transition. We use our model to interpret experiments on dewetting of aggregates of MDA-MDB-231 cancer cells and shape fluctuations of surface-associated OVCAR3 cell aggregates. Finally, we show how spatial heterogeneity in attachments governs collective chemotaxis of cell aggregates. Together, these results reveal how active intermittent attachments generate cell aggregates with emergent material properties, with broad implications for development and cancer.

biophysics↗

bioRxiv2025

Targeted disruption of phage liquid crystalline droplets abolishes antibiotic tolerance of bacterial biofilms

All bacterial biofilms contain an extracellular matrix rich in filamentous molecules 1 that self-associate 2-4, conferring emergent properties to bacteria 5, including antibiotic tolerance 6. Pseudomonas aeruginosa is a human pathogen that forms biofilms in diverse infectious settings 7,8, where the upregulation of a filamentous bacteriophage Pf4, has been shown to be a key virulence factor that protects bacteria from antibiotics 9,10. Here, we modelled biophysical characteristics of biofilm-linked liquid crystalline droplets formed by Pf4, which predicted that sub-stoichiometric phage binders had the ability to disrupt liquid crystals by changing the surface properties of the phage. We tested this prediction by developing nanobodies targeting the outer surface of the Pf4 phage, which disrupted in vitro reconstituted droplets, promoted antibiotic diffusion into bacteria, disrupted P. aeruginosa biofilm formation under a variety of conditions, and abolished antibiotic tolerance of biofilms. The inhibition strategy illustrated in this study could be extended to biofilms of other pathogenic bacteria, where filamentous molecules are pervasive in the extracellular matrix. Furthermore, our findings exemplify how targeting a biophysical mechanism, rather than a defined biochemical target, is a promising avenue for intervention, with the potential of applying this concept to other disease-related contexts.

microbiology↗

bioRxiv2025

Suspension physics govern the multiscale dynamics of blood flow in sickle cell disease

In diseases from diabetes to malaria, blood dynamics are significantly altered, resulting in poor clinical outcomes. However, the multiscale mechanisms that determine blood flow in the microcirculation in health and disease are undefined, largely owing to the difficulty in directly linking cell properties to whole-blood rheology. Here, we overcome these difficulties by developing a microfluidic platform to measure red blood cell properties and flow dynamics in the same blood samples from donors. We focus on sickle cell disease (SCD), a genetic disorder that causes red blood cells to stiffen in deoxygenated conditions, with disease pathology driven by oxygen-dependent blood rheology. Our linked cell and whole-blood measurements establish that increases in effective resistances in heterogeneous suspensions are driven by increases in the proportion of stiff cells, similar macroscopically to the behavior of rigid-particle suspensions. Furthermore, by combining simulations with spatially resolved measurements of cell dynamics, we show how the spatio-temporal organization of stiff and deformable cells determines blood rheology and drives disease pathophysiology. In the presence of deformable cells, the stiffened cells marginate towards channel walls, increasing effective wall friction. In fully deoxygenated conditions in which all cells are stiffened, significant heterogeneity in cell volume fraction along the direction of flow causes localized jamming, drastically increasing effective viscous flow resistance. Our work defines the relevant suspension physics required to understand pathological blood rheology in SCD and other diseases affecting red blood cell properties. More broadly, we reveal the multiscale processes that determine emergent rheology in heterogeneous particle suspensions.

physiology↗

bioRxiv2024

Pattern formation along signaling gradients driven by active droplet behaviour of cell groups

Gradients of extracellular signals organise cells in tissues. Although there are several models for how gradients can pattern cell behaviour, it is not clear how cells react to gradients when the population is undergoing 3D morphogenesis, in which cell-cell and cell-signal interactions are continually changing. Dictyostelium cells follow gradients of their nutritional source to feed and maintain their undifferentiated state. Using light sheet imaging to simultaneously monitor signaling, single cell and population dynamics, we show that the cells migrate towards nutritional gradients in swarms. As swarms advance, they deposit clumps of cells at the rear, triggering differentiation. Clump deposition is explained by a physical model in which cell swarms behave as active droplets: cells proliferate within the swarm, with clump shedding occurring at a critical population size, at which cells at the rear no longer perceive the gradient and are not retained by the emergent surface tension of the swarm. The droplet model predicts vortex motion of the cells within the swarm emerging from the local transfer of propulsion forces, a prediction validated by 3D tracking of single cells. This active fluid behaviour reveals a developmental mechanism we term "musical chairs" decision-making, in which the decision to proliferate or differentiate is determined by the position of a cell within the group as it bifurcates.

biophysics↗

bioRxiv2023

Motility-Induced Phase Separation Mediated by Bacterial Quorum Sensing

We study motility-induced phase separation (MIPS) in living active matter, in which cells interact through chemical signalling, or quorum sensing. In contrast to previous theories of MIPS, our multiscale continuum model accounts explicitly for genetic regulation of signal production and motility. Through analysis and simulations, we derive a new criterion for the onset of MIPS that depends on features of the genetic network. Furthermore, we identify and characterise a new type of oscillatory instability that occurs when gene regulation inside cells promotes motility in higher signal concentrations.

biophysics↗

bioRxiv2022

Biophysical basis of phage liquid crystalline droplet-mediated antibiotic tolerance in pathogenic bacteria

Inoviruses are abundant filamentous phages infecting numerous prokaryotic phyla, where they can symbiotically promote host fitness and increase bacterial virulence. Due to their unique properties, inoviruses have also been utilised in biotechnology for phage display and as models for studying phase behaviour of colloidal rods. Inoviral phages secreted by bacteria can self-assemble into liquid crystalline droplets that protect bacterial cells in biofilms from antibiotics, however, factors governing the formation of such droplets and the mechanism of antibiotic protection are poorly understood. Here, we investigate the structural, biophysical, and protective properties of liquid crystalline droplets formed by Pseudomonas aeruginosa and Escherichia coli inoviral phages. We report a cryo-EM structure of the capsid from the highly studied E. coli fd phage, revealing distinct biochemical properties of fd compared to Pf4 phage from P. aeruginosa. We show that fd and Pf4 form liquid crystalline droplets with diverse morphologies governed by the underlying phage particle geometry and biophysics, rather than their surface biochemical properties. Finally, we show that these morphologically diverse droplets made of either phage can protect rod-shaped bacteria from antibiotic treatment, despite differing modes of association with cells. This study advances our understanding of phage assembly into liquid crystalline droplets, and provides insights into how filamentous molecules protect bacteria from extraneous molecules under crowding conditions, which are found in biofilms or on infected host tissues.

microbiology↗

bioRxiv2022

Universal dynamics of biological pattern formation in spatio-temporal morphogen variations

In biological systems, chemical signals termed morphogens self-organise into patterns that are vital for many physiological processes. As observed by Turing in 1952, these patterns are in a state of continual development, and are usually transitioning from one pattern into another. How do cells robustly decode these spatio-temporal patterns into signals in the presence of confounding effects caused by unpredictable or heterogeneous environments? Here, we answer this question by developing a general theory of pattern formation in spatio-temporal variations of pre-pattern morphogens, which determine gene-regulatory network parameters. Through mathematical analysis, we identify universal dynamical regimes that apply to wide classes of biological systems. We apply our theory to two paradigmatic pattern-forming systems, and predict that they are robust with respect to non-physiological morphogen variations. More broadly, our theoretical framework provides a general approach to classify the emergent dynamics of pattern-forming systems based on how the bifurcations in their governing equations are traversed.

biophysics↗

bioRxiv2020

Emergent robustness of bacterial quorum sensing in fluid flow

Bacteria use intercellular signaling, or quorum sensing (QS), to share information and respond collectively to aspects of their surroundings. The autoinducers that carry this information are exposed to the external environment; consequently, they are affected by factors such as removal through fluid flow, a ubiquitous feature of bacterial habitats ranging from the gut and lungs to lakes and oceans. To understand how QS genetic architectures in cells promote appropriate populationlevel phenotypes throughout the bacterial life cycle requires knowledge of how these architectures determine the QS response in realistic spatiotemporally varying flow conditions. Here, we develop and apply a general theory that identifies and quantifies the conditions required for QS activation in fluid flow by systematically linking cell- and population-level genetic and physical processes. We predict that, when a subset of the population meets these conditions, cell-level positive feedback promotes a robust collective response by overcoming flow-induced autoinducer concentration gradients. By accounting for a dynamic flow in our theory, we predict that positive feedback in cells acts as a low-pass filter at the population level in oscillatory flow, allowing a population to respond only to changes in flow that occur over slow enough timescales. Our theory is readily extendable, and provides a framework for assessing the functional roles of diverse QS network architectures in realistic flow conditions.

biophysics↗

bioRxiv2019

Flow-induced symmetry breaking in growing bacterial biofilms

Bacterial biofilms represent a major form of microbial life on Earth and serve as a model active nematic system, in which activity results from growth of the rod-shaped bacterial cells. In their natural environments, ranging from human organs to industrial pipelines, biofilms have evolved to grow robustly under significant fluid shear. Despite intense practical and theoretical interest, it is unclear how strong fluid flow alters the local and global architectures of biofilms. Here, we combine highly time-resolved single-cell live imaging with 3D multi-scale modeling to investigate the mechanisms by which flow affects the dynamics of all individual cells in growing biofilms. Our experiments and cell-based simulations reveal three quantitatively different growth phases in strong external flow, and the transitions between them. In the initial stages of biofilm development, flow induces a downstream gradient in cell orientation, causing asymmetrical droplet-like biofilm shapes. In the later developmental stages, when the majority of cells are sheltered from the flow by the surrounding extracellular matrix, buckling-induced cell verticalization in the biofilm core restores radially symmetric biofilm growth, in agreement with predictions of a 3D continuum model.

biophysics↗

bioRxiv2018

Learning dynamical information from static protein and sequencing data

Many complex processes, from protein folding and virus evolution to brain activity and neuronal network dynamics, can be described as stochastic exploration of a high-dimensional energy landscape. While efficient algorithms for cluster detection and data completion in high-dimensional spaces have been developed and applied over the last two decades, considerably less is known about the reliable inference of state transition dynamics in such settings. Here, we introduce a flexible and robust numerical framework to infer Markovian transition networks directly from time-independent data sampled from stationary equilibrium distributions. Our approach combines Gaussian mixture approximations and self-consistent dimensionality reduction with minimal-energy path estimation and multi-dimensional transition-state theory. We demonstrate the practical potential of the inference scheme by reconstructing the network dynamics for several protein folding transitions, gene regulatory network motifs and HIV evolution pathways. The predicted network topologies and relative transition time scales agree well with direct estimates from time-dependent molecular dynamics data, stochastic simulations and phylogenetic trees, respectively. The underlying numerical protocol thus allows the recovery of relevant dynamical information from instantaneous ensemble measurements, effectively alleviating the need for time-dependent data in many situations. Owing to its generic structure, the framework introduced here will be applicable to high-throughput RNA and protein sequencing datasets and future cryo-electron-microscopy data, and can guide the design of new experimental approaches towards studying complex multiphase phenomena.

bioinformatics↗