Frontiers in Physics

In tissue engineering, it is important to conceive and construct artificial bio-mimetic scaffolds able to foster cell migration as this is a fundamental process in wound healing and tissue regeneration. In order to do that, cubically symmetric and triply periodic porous structures have been identified as promising candidates for instance for the reconstruction of artificial cartilages and bones, also due to their tunable mechanical characteristics and highly inter-connected porous architectures that mimic the trabecular bone hyperboloidal topography. We propose here a mathematical approach that might be helpful to identify what are the best geometrical characteristics of such scaffolds, in order to promote cell migration into the porous structures and speed-up their re-population. The method is based on the observation that cell nucleus deformations should be avoided, yet assuring a good possibility for the cell to reach the wall of the porous structure. Mathematically speaking, this leads to the problem of identifying the size of the largest sphere that can pass, without being stuck, through the pores of the bio-mimetic scaffold.

Infectious disease surveillance systems in tropical countries show that respiratory disease incidence generally manifests as year-round activity with weak fluctuations and irregular seasonality. Previously, using a ten-year time series of influenza-like illness (ILI) collected from outpatient clinics in Ho Chi Minh City (HCMC), Vietnam, we found a combination of nonannual and annual signals driving these dynamics, but with unknown mechanisms. In this study, we use seven stochastic dynamical models incorporating humidity, temperature, and school term to investigate plausible mechanisms behind these annual and nonannual incidence trends. We use iterated filtering to fit the models and evaluate the models by comparing how well they replicate the combination of annual and nonannual signals. We find that a model including specific humidity, temperature, and school term best fits our observed data from HCMC and partially reproduces the irregular seasonality. The estimated effects from specific humidity and temperature on transmission are nonlinearly negative but weak. School dismissal is associated with decreased transmission, but also with low magnitude. Under these weak external drivers, we hypothesize that stochasticity makes a strong sub-annual cycle more likely to be observed in ILI disease dynamics. Our study shows a possible mechanism for respiratory disease dynamics in the tropics. When the external drivers are weak, the seasonality of respiratory disease dynamics is prone to the influence of stochasticity.

Mainland China experienced multiple waves of COVID19 pandemic during 2020 2022, driven by emerging variants and changes in public health and social measures (PHSMs). We developed a hypergraph-based Susceptible Vaccinated Exposed Infectious Recovered Susceptible (SVEIRS) model to reconstruct epidemic dynamics across 31 provinces, capturing transmission heterogeneity associated with clustered contacts. We assessed key characteristics of transmission at national and provincial levels during four outbreak periods: initial, localized predelta, Delta, and widespread Omicron, which accounted for 96.7% of all infections. We found significant diversity in transmission contributions across cluster sizes, with a small fraction of larger clusters responsible for a disproportionate share of infections. Counterfactual analyses showed that reducing clustersize heterogeneity, while holding overall exposure constant, could have lowered national infections by 11.70 to 30.79%, with the largest effects during Omicron period. Ascertainment rates increased over time but remained spatially heterogeneous with a range: (14.40, 71.93)%. Population susceptibility declined following mass vaccination (to 42.49% in Aug 2021, nationally) and rebounded (to 89.89% in Nov 2022) due to waning immunity with variations across the provinces. Effective reproduction numbers displayed marked temporal and spatial variability, with higher estimates during Omicron. Overall, these results highlight critical role of group contact heterogeneity in shaping epidemic dynamics.

One of the most critical steps in human reproduction is the selection of the dominant follicle when a single follicle is chosen from a large group of follicles to ovulate. Although this process involves complex hormonal regulation, the complete microscopic picture of unique selectivity remains unclear. We propose a novel stochastic mechanism for dominant follicle selection that incorporates the actions of the most relevant hormones, follicle-stimulating hormone (FSH) and estradiol. Our theoretical picture suggests the following sequence of events. As soon as the FSH concentration reaches the critical threshold, one of the available follicles is randomly selected, which immediately stimulates the production of estradiol, which, via a negative feedback mechanism, suppresses further FSH production, lowering its concentration below the critical threshold. This suppression limits the time window for the possible second follicle selection event, allowing only a single follicle to be selected. Based on this picture, a minimal quantitative theoretical model of dominant follicle selection is developed and analyzed using analytical calculations and computer simulations. Theoretical analysis shows how the interplay between different parameters that govern follicle selection leads to high selectivity. Our theoretical approach can explain some key known observations, providing a quantitative tool for analyzing biological reproduction phenomena.

This work presents the development of a novel approach to model the dynamics of cancer cells in microcirculation. We investigate the role the membrane elasticity, and cancer cell shape on deformation dynamics under the shear and pressure forces in a micro-channel. The proposed numerical model is based on a hybrid continuum-particle approach. The cancer cell model includes the cell membrane, nucleus, cytoplasm and the cytoskeleton. The Dissipative Particle Dynamics method was employed to simulate the mechanical components. The blood plasma is modeled as a Newtonian incompressible fluid. A Fluid-Structure Interaction coupling, leveraging the Immersed Boundary Method is developed to simulate the cells response to flow dynamics. We quantify how subtle variations in these biophysical properties alter deformation indices such as sphericity and aspect ratio, and stress distributions on the membrane of the cancer cell. Our findings align well with existing computational and experimental studies. Results reveal that increased membrane stiffness reduces overall deformation as well as the total distance traveled. Similarly, cell geometry strongly influences flow-structure interactions: near-spherical morphologies exhibit stable deformation with minimal sensitivity to shear variations, whereas elongated geometries show pronounced orientation and stretching effects. Collectively, these findings highlight the critical importance of cell-specific heterogeneity in governing cell dynamics in microvascular flows. Furthermore, the intracellular and extracellular dynamics response of the cancer cell are intrinsically linked to their shape, in which certain morphologies displayed strong resistance to the fluid-induced forces and the ability to migrate in various directions. The insights obtained provide a mechanistic framework for understanding circulating tumor cell transport in shear-dominated environments during metastasis. Our work may inform the design of biomimetic microfluidic systems and therapeutic strategies targeting cancer cell detection and cancer prognosis.

Although there has been considerable progress in understanding the factors that determine the invasiveness of plasmodium falciparum merozoites, the collective role of the biophysical characteristics of erythrocyte deformability in the invasion process is poorly understood. Cell shape, cytoplasmic viscosity, and membrane stability are the main determinants of erythrocyte deformability, but it remains unknown how these properties affect the merozoite invasiveness. This study aimed to investigate computationally (i) the role of erythrocyte morphology and merozoite-induced erythrocyte membrane damage in merozoite invasion and (ii) the suitability of mechanical markers of merozoite-induced erythrocyte membrane damage for screening of invasion-blocking antimalarial drugs. Finite element models were developed to represent a human erythrocyte and a spherocyte, their invasion by a malaria merozoite, and erythrocyte compression and nanoindentation as mechanical assays for membrane damage. Smoothed particle hydrodynamics represented the erythrocyte cytoplasm, and merozoite-induced erythrocyte membrane damage was implemented with a constitutive model. The invasiveness of the merozoite decreases with increased erythrocyte sphericity associated with genetic disorders such as hereditary spherocytosis. The invasiveness is larger when membrane damage is induced in the erythrocyte at an early invasion stage than throughout the invasion process. The minimum force required for a malaria merozoite to invade a human erythrocyte was predicted to be 11 pN. The findings on the invasion mechanics can guide future studies into the invasiveness of the merozoite. The nanoindentation simulations point to the potential of nanoindentation to determine erythrocyte membrane damage for screening novel invasion-blocking anti-malaria drugs.

The interactions of droplets and filaments can lead to mutual deformations and complex combined behavior. Such interactions also occur within the cell, where biomolecular condensates, distinct liquid phases often composed of proteins, have been observed to structure and affect the organization of the cytoskeleton. In particular, biomolecular condensates have been shown to undergo characteristic deformations when cytoskeletal filaments are fully embedded within them. However, a full understanding of the underlying physical mechanisms is still missing. Here, we combine experiments with coarse-grained molecular dynamics simulations and analytical models to uncover the physical mechanisms that define emerging shapes of droplets containing filaments. We find that the surface tension of the liquid phase and the bending energy of the filament(s) suffice to accurately capture emerging shapes if the length of the filament is small compared to the liquid volume. As the volume fraction of filament(s) increases, wetting effects become increasingly important, setting physical constraints within which surface and bending energies compete to define the droplet shapes. We find that mutual deformations of condensate and filament extend accessible shapes beyond classical stability considerations, leading to structuring and entrapment of contained filaments. Shape deformations may further affect ripening dynamics that favor certain geometries. Our findings provide a physical framework for a better understanding of the possible roles of biomolecular condensates in cytoskeletal organization.

The COVID-19 pandemic has presented severe challenges in understanding and predicting the spread of infectious diseases, necessitating innovative approaches beyond traditional epidemiological models. This study introduces an advanced method for automated model discovery using the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm, leveraging a dataset from the COVID-19 outbreak in Thuringia, Germany, encompassing over 400,000 patient records and vaccination data. By analysing this dataset, we develop a flexible, data-driven model that captures many aspects of the complex dynamics of the pandemics spread. Our approach incorporates external factors and interventions into the mathematical framework, leading to more accurate modelling of the pandemics behaviour. The fixed coefficient values of the differential equation as globally determined by the SINDy were not found to be accurate for locally modelling the measured data. We therefore refined our technique based on the differential equations as found by SINDy, by investigating three modifications that account for recent local data. In a first approach, we re-optimized the coefficient values using seven days of past data, without changing the globally determined differential equation. In a second approach, we allowed a temporal dependence of the coefficient values fitted using all previous data in combination with regularization. As a last method, we kept the coefficients fixed to the original values but augmented the differential equation with a small neural network, locally optimized to the data of the past week. Our findings reveal the critical role of vaccination and public health measures in the pandemics trajectory. The proposed model offers a robust tool for policymakers and health professionals to mitigate future outbreaks, providing insights into the efficacy of intervention strategies and vaccination campaigns. This study advances the understanding of COVID-19 dynamics and lays the groundwork for future research in epidemic modelling, emphasising the importance of adaptive, data-informed approaches in public health planning.

Understanding how cells migrate through confined environments is crucial for elucidating fundamental biological processes, including cancer invasion, immune surveillance, and tissue morphogenesis. The nucleus, as the largest and stiffest cellular organelle, often limits cellular deformability, making it a key factor in migration through narrow pores or highly constrained spaces. In this work, we introduce a geometric surface partial differential equation (GS-PDE) model in which the cell plasma membrane and nuclear envelope are described as evolving energetic closed surfaces governed by force-balance equations. We replicate the results of a biophysical experiment, where a microfluidic device is used to impose compressive stresses on cells by driving them through narrow microchannels under a controlled pressure gradient. The model is validated by reproducing cell entry into the microchannels. A parametric sensitivity analysis highlights the dominant influence of specific parameters, whose accurate estimation is essential for faithfully capturing the experimental setup. We found that surface tension and confinement geometry emerge as key determinants of translocation efficiency. Although tailored to this specific setup for validation purposes, the framework is sufficiently general to be applied to a broad range of cell mechanics scenarios, providing a robust and flexible tool for investigating the interplay between cell mechanics and confinement. It also offers a solid foundation for future extensions integrating more complex biochemical processes such as active confined migration.

Infectious disease dynamics are strongly shaped by human mobility, social structure, and heterogeneous contact patterns, yet many epidemic models do not jointly capture these features. This study develops a spatial metapopulation epidemic model incorporating recurrent group-switch interactions to represent real-world transmission processes. Building on the Movement-Interaction-Return framework, the model integrates household structure, age-stratified contacts, and mobility between locations within a single SEIR framework. Using UK demographic, mobility, and social contact data, the model quantifies how within- and between-group interactions, mobility rates, and location connectivity influence epidemic spread. Both deterministic and stochastic simulations are implemented to analyse outbreak dynamics, variability, and fade-out probabilities for COVID-19-like and Ebola-like infections. Results shows that highly connected locations drive faster transmission, earlier epidemic peaks, and greater difficulty in containment, whereas larger but less connected locations tend to produce slower, more localised outbreaks despite their population size. Comparative analysis reveals that COVID-19-like infections spread rapidly and remain difficult to control even under interventions, while Ebola-like infections exhibit slower dynamics and are more effectively contained, particularly under targeted measures. Non-pharmaceutical interventions, particularly widespread closures, substantially reduce infections, hospitalisations, and deaths, although effectiveness depends on timing and pathogen characteristics. These findings highlight the importance of integrating mobility, clustering, and demographic heterogeneity to inform targeted and effective epidemic control strategies.

It has been broadly recognized that the crosstalk between cells and their extracellular matrix (ECM) is crucial for the proper function of biological tissues. Relatively recently the role of ECM came in focus in the context of neuronal development and regeneration, where the effects of the ECM mechanics on the migration of neurons and neurite growth are still incompletely understood. Here we present an in silico twin framework for neurite growth focusing on its biophysical interactions with the ECM. This coarsegrained model accounts for viscoelastic liquid- and solid-like ECMs and neurite growth by ECM-mediated traction forces. Resulting growth trajectories can be rationalized based on the theory of random walks and polymer physics. To critically assess models predictive power, we performed experiments on neurites of hippocampal rat neurons growing in 3D collagen gels and observed a more persistent axon outgrowth in denser matricies. The model fully recapitulated the effect, thereby underpinning the central role of mechanical interactions with ECM as guiding principle of axonal growth. We argue that a combination our model with optical microscopy may provide an is silico twin helping to disentangle the contributions of "passive" physics from more complex effects of chemical queues or an apparent mechanosensing.

PurposeThis work investigates the role of the cancer cell morphology and elasticity on the deformation patterns under shear-flow in a micro-channel. MethodsA novel hybrid continuum-particle framework is developed to simulate cancer-cell dynamics. Cell membrane and nucleus geometries are reconstructed from microscopic images and modeled using Dissipative Particle Dynamics, while the surrounding blood plasma is treated as an incompressible Newtonian fluid. Cell-flow interactions are captured via an immersed boundary method. ResultsAll cancer-cell models exhibited a rapid deformation response within the first 1-2 ms, followed by morphology- and stiffness-dependent shape evolution. The compact morphologies showed strong recovery, whereas the other models evolved toward folded/lobed states with only intermittent partial recovery during shape transitions. Membrane stiffening dominated elongation and compactness loss, while nuclear stiffening modulated deformation excursions and partial recovery. These shape transitions were accompanied by near-field vortex reorganization and traction localization. Similar to deformation response the net membrane force exhibited a common start-up rise within 0-0.5 ms followed by relaxation. Compact morphologies produce lower and steadier forces. They show minimal stiffness dependence. Deformation-prone morphologies show stronger unsteadiness and clearer stiffness modulation. Cross-sectional velocity and vorticity fields showed a dominant x-directed hydrodynamic imbalance and lateral migration. ConclusionOur results demonstrate that morphology sets the stiffness modulated deformation patterns which effects the extracellular flow dynamics and traction. In turn, the resulting flow field and traction distribution feed back to influence subsequent deformation and migration. This mechanistic link provides a framework for interpreting circulating tumor cell transport in shear-dominated metastatic environments.

Cerebrospinal fluid (CSF) circulates around and through the brain, supporting neural homeostasis by regulating the extracellular chemical environment. Yet the physical mechanisms governing CSF-driven solute transport remain poorly understood, limiting the design of diagnostic and therapeutic strategies targeting brain clearance and drug delivery. Pulsatile CSF flow in the cranial subarachnoid space (cSAS), is driven by cardiac, respiratory, and sleep-related vasomotion. Over longer timescales weaker steady flows, such as inertial steady streaming, Stokes drift, and production-drainage flow, may contribute to solute transport, but their role and relative importance remain unclear. Here, we develop a simplified two-dimensional model of CSF flow and solute transport in the cSAS using lubrication theory. Through multiple-timescale and asymptotic analyses, we derive a reduced long-time transport equation in which advection is governed by the Lagrangian mean velocity, incorporating steady streaming, production-drainage flow, and Stokes drift. Analysing three physiologically relevant case studies, we show that steady flows can substantially reshape concentration profiles, enhance dispersion, and alter clearance efficiency. Our results clarify the mechanisms underlying CSF-mediated transport, predict distinct regimes in humans and mice, and highlight the importance of subject-specific physiological parameters when interpreting contrast-agent and intrathecal drug-delivery studies.

Understanding the dynamical mechanisms underlying epidemic wave formation remains a central problem in mathematical epidemiology. Population-level epidemic waves are commonly interpreted as emergent consequences of nonlinear transmission feedback between susceptible and infectious individuals. However, epidemic time series from different regions often display markedly different waveform regimes, ranging from sharply peaked epidemics with rapid post-peak decline to more prolonged plateau-like dynamics. Here we propose the SEVA (Seasonal/Environmental Viral Activity) framework as a parsimonious alternative dynamical interpretation of epidemic wave formation. In this formulation, epidemic waveforms arise from depletion of a finite vulnerable population under a temporally structured viral activity field. The activity function is represented by a monotonic logistic hazard describing the temporal evolution of viral activity. With activation timing and steepness held constant across regions, daily incidence emerges as the product of activity intensity and the remaining vulnerable population. The framework is applied to first-wave COVID-19 hospitalization and mortality data from selected European countries and U.S. states during spring 2020. With fixed activation parameters and region-specific activity intensity, the model provides a simple dynamical explanation for diverse epidemic waveform regimes--including sharply peaked waves and plateau-like dynamics--without modification of the underlying dynamical structure. When epidemic trajectories are expressed in normalized form, curves from regions with very different mortality burdens display closely similar temporal structures. Within the SEVA formulation, this behaviour arises naturally from the interaction between a common temporal activation profile and regionally varying activity intensity. In this perspective, sharply peaked epidemics and plateau-like trajectories represent different dynamical regimes of the same activity-driven depletion process.

Sectioning techniques based on patterned illumination have been widely used to obtain well-contrasted images of thick samples using widefield imaging setups. While their application to fluorescence microscopy has been extensively demonstrated and studied, their application to reflection imaging is scarcer and their performance has only been partly characterized. In this paper, we study numerically and analytically two such sectioning techniques, line confocal (LC) and structured illumination (SI), in the context of their application to reflection interference contrast microscopy (RICM), an imaging technique widely use in soft matter and biophysics studies to monitor object-surface interactions, or quantify surface functionalization. Our derivation, however, should provide insight into their use with other reflection methods such as optical coherence tomography (OCT) or scanning laser ophtalmoscope (SLO). We derive approximate analytical equations to relate the performance of sectioning to the optical setup parameters, allowing straightforward understanding of their influence on the achieved image intensity and depth of focus, and we systematically compare our prediction with experimental data. Finally, we quantify the precision and accuracy of each method in typical practical cases, providing guidelines to choose the most appropriate (LC, SI, or a simple background subtraction on a widefield image) for the sample under study.

In this research, we create a new fractional-order SEIHRD framework to examine how the Nipah virus moves from one species to another (zoonotic spillover) and how it later spreads throughout a community (via contact with one another) or in a hospital or isolation situation (via entering into a hospital or being placed under quarantine). We used the fractional-derivative formulation of the SEIHRD model to demonstrate memory-based effects related to the progression of an infection and also reflect time-distributed effects associated with surveillance and control measures placed on an infected patient. We first demonstrated that the basic epidemiologic properties of the model were consistent by showing that the solutions of the SEIHRD differential equations will always yield positive and bounded solutions within biologically relevant parameter ranges. We then established the well-posedness of this model by transforming the SEIHRD differential equations into an equivalent integral operator and applying various fixed-point arguments to demonstrate that there will always be unique solution(s) to the SEIHRD differential equations. To evaluate the threshold parameter for the transmission of Nipah virus within a given population we calculated the threshold level through the next generation method to determine the expected number of secondary infections from a new or chronically infected host. One of the main contributions of this work is to include an analysis of the robustness of a given solution to all potential perturbations (i.e., Ulam-Hyers and generalized Ulam-Hyers stability). In addition, we provide analytic results guaranteeing that small perturbations due to approximate modeling, numerical approximation (discretization), or the lack of data fidelity will produce controlled deviations in the solutions. To finish this project, we perform a global sensitivity analysis on uncertain coefficients to evaluate their contribution to the uncertainty of each coefficient and to find out the coefficients that most strongly influence major outcome metrics. This will allow us to develop a priority order for prioritizing spillover control (reduction of human contact and/or isolation), contact reduction, and expenditure of resources towards isolation-related interventions. The resulting framework converts fractional epidemic modeling from a descriptive simulation to a replicable method with robustly defined behavior and equal response prediction.

Many respiratory pathogens co-circulate within human populations. Yet, how pathogen community structure shapes the dynamics of infectious diseases remains poorly understood. At the population level, investigating polymicrobial dynamics, with potential underlying competitive or cooperative interactions, is challenging, because of confounding factors such as differing seasonality. This is particularly true for endemic pathogens which typically exhibit stable periodic dynamics. Their disruption due to the implementation of non-pharmaceutical interventions during the COVID-19 pandemic thus represents a unique large-scale natural experiment that can be leveraged to provide valuable insights into the complex interplay between respiratory pathogens. Here, we focus on the population dynamics of human rhinovirus (common cold) and on the potential viral interference of influenza A virus (flu A), which is hypothesized to account for their asynchronous circulation. Using a Bayesian framework, we first show based on simulations that exogenous perturbations can be a powerful tool to disentangle the contribution of pathogen interaction from other epidemiological factors. We then apply our framework to long surveillance time series from the US and Canada spanning the COVID-19 pandemic. We estimate key parameters of rhinovirus but find no conclusive support for an influence of influenza A virus at the population level.

Sylvatic Yellow Fever (YF) is an infectious mosquito-borne disease with significant epidemiological relevance due to its widespread distribution and high lethality for human and non-human primates, particularly in tropical regions of the planet such as in Brazil. Identifying regions and periods of high environmental suitability for the occurrence of YF is essential for preventing or mitigating its burden, as it enables the efficient allocation of surveillance efforts, prevention, and implementation of control measures. Environmental modeling of YF occurrence has proven to be an effective approach toward this goal; however, its effectiveness strongly depends on the modeling framework's capabilities as well as the spatial and temporal precision of all associated data. We propose a fine-scale geospatial modeling of YF environmental suitability that is based on a generative machine-learning ensemble method built on a large set of high-resolution environmental covariates. First, we take the spatiotemporal statistical description of the environment of each of the 545 YF cases from 2019--2024 up to 30 m/monthly resolution at three buffer scales: 100 m, 500 m, and 1000 m ratios. Then, we perform a feature selection and train hundreds of One-Class Support Vector Machine submodels to form a robust ensemble model, whose predictions are projected to a 1x1 km resolution grid of Brazil under several metrics, exceeding seven million ensemble evaluations. The predictions ranked the Southern Brazil region with the highest mean suitability for YF, with a level of 0.64; Southeast comes next with 0.46, followed closely by Central-West region (0.44), North (0.39), and finally Northeast (0.28). The model exhibited high uncertainty for the North region, indicating that data collection efforts are much needed in this region. As for the environmental covariates, a feature analysis pointed out that Land use and cover accounts for the largest influence in the model output.

Patient-derived tumour spheroids are increasingly used as engineered three-dimensional tissue models for studying tumour growth, nutrient limitation, and therapeutic response. However, extracting quantitative, mechanistically interpretable information from longitudinal imaging data remains challenging. Here, we present a three-dimensional phase-field framework for modelling patient-derived tumour spheroids as continuum, self-organising tissues. The model captures the coupled evolution of viable and necrotic cell fractions through nutrient-limited growth, death, and mechanically and thermodynamically mediated motion, using seven biologically interpretable effective parameters. Key experimental observables emerge naturally from nutrient-growth coupling, without imposing explicit species interfaces or quiescent layers. The framework was quantitatively calibrated against longitudinal imaging data from melanoma spheroids across two cell lines and three initial seeding densities. Across all conditions, simulations reproduced the temporal evolution of all measured observables with low relative error ({approx} 3{sigma} of experimental data), and direct comparison with an established Greenspan-type ODE model demonstrated comparable or improved predictive accuracy. Parameter identifiability analysis revealed weak individual parameter constraints, yet model predictions remained robust, a profile consistent with biological models. We demonstrate that a general PDE-based growth framework can match or outperform a dedicated spheroid model while remaining fully biologically interpretable. Beyond predictive accuracy, the phase-field formulation naturally resolves internal mechanical structure, providing access to quantities that are not directly experimentally observable. These results establish that mechanistically grounded continuum models can be quantitatively calibrated to routine spheroid imaging data, offering a foundation for integrating spatial and mechanical information into the interpretation of organoid-based assays. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=77 SRC="FIGDIR/small/717345v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@1cb3b45org.highwire.dtl.DTLVardef@1a053d5org.highwire.dtl.DTLVardef@dffe34org.highwire.dtl.DTLVardef@1aa0b72_HPS_FORMAT_FIGEXP M_FIG C_FIG

Colorectal cancer (CRC) is the second most common cause of cancer-related mortality. At the molecular level, CRC is associated with genetic mutations and epigenetic modifications that dysregulate various signaling networks. From the biophysical viewpoint, invasive and metastatic cell migration need to be empowered by mechanical forces. In this study, we analyze the dynamic deformation of patient-derived CRC organoids in Fourier space and demonstrate how organoids with protooncogene BRAF mutation exhibit deformation phenotypes at an early stage. The organoids with BRAFmut have significantly lower elasticity and higher viscosity than those with BRAFWT, which mathematically indicated as the weakening of cell-cell adhesion. Immunohistochemical images, qRT-PCR, and TCGA data analysis confirm the downregulation of E-cadherin (CDH1) in BRAFmut organoids as well as in BRAFmut CRC, suggesting that the decrease in cell-cell adhesion in BRAFmut CRC facilitates invasive and metastatic migration. Notably, the recovery of CDH1 expression by pharmacological inhibition of DNA methylation can quantitatively be detected as the change in mechanical properties, suggesting that the complementary combination of dynamic phenotyping, mathematical modelling, and molecular-level analyses has a potential to unravel the mechanistic causality of the critical gene mutation and CRCs prognosis and the response to therapeutic interventions.