Accurate quantification of the structural features of subcutaneous (SC) tissue is essential for understanding its physiology and developing predictive computational models of skin biomechanics and biotransport. Image analysis for tissue characterization, with varying feature sizes, shapes, and distribution, typically involves manual segmentation, which is labor-intensive, user-dependent, and irreproducible. Yet robust automated algorithms for these tissue structures are currently unavailable. While supervised Machine learning (ML) methods offer the potential to address this challenge, those require extensive training datasets and labeling, which are unavailable for SC tissues. In this paper, we present a new methodology that uses unsupervised machine learning to identify the structural features of subcutaneous tissue from stained histology slides. We demonstrate our method using porcine skin SC tissue samples. A novel two-dimensional (2D) image transformation generates maps of radial intensity values for each pixel, referred to as a proximal intensity map, which is subsequently reduced into a lower-dimensional feature vector space. Unsupervised learning using K-means clustering is employed to classify pixels based on their computed feature vectors. By designing the proximal intensity map and the feature space reduction, we show that the clustering step can automatically and robustly classify and identify the complex collagenous network within adipose tissue spaces. We also present an objective basis for selecting the optimal search radius based on the rationale of noise minimization from spurious segmented pixels and maximum separation of pixel intensity distribution of different features. This work provides a novel method for automatically identifying subcutaneous tissue structures, advancing the understanding of skin physiology, and developing improved in vitro tissue models.

Reduced-order models (ROMs) are essential for rapid simulation of complex biomechanical systems and for bridging the gap between high fidelity models and clinical application. However, ROMs for tissue growth and remodeling (G R) remain largely unexplored. Here, we present a Neural Ordinary Differential Equation (NODE) ROM framework that learns latent dynamics of coupled mechanical deformation and tissue growth, demonstrated in the context of skin growth during tissue expansion (TE). TE is a challenging problem involving nonlinear contact, history-dependent material behavior, and mechanobiology driven growth. The displacement field is compressed via Proper Orthogonal Decomposition (POD) into a low-dimensional latent space, and a NODE learns the resulting dynamics conditioned on patient-specific parameters. To address long-horizon error accumulation, a key challenge in autoregressive latent dynamical models, we propose a closed-loop architecture in which encoded features of the evolving growth field are fed back into the dynamics at each step. We compare feedback representations of increasing expressiveness: scalar, linear POD-based, and nonlinear CNN-based. The CNN-based growth feature feedback substantially stabilizes long-horizon rollouts. The best model captures 90.3

SUMMARY Breast-conserving surgery (BCS; lumpectomy) is widely used to treat early-stage breast cancer, yet the complexity and patient-specific variability of postoperative cavity remodeling make healing trajectories and physical outcomes difficult to predict. Although inflammatory and vascular processes are central to these outcomes, mathematical models of tissue healing do not capture their coupled interactions or calibrate them against experimental data. Here, we extend a computational model of breast cavity healing to incorporate coupled inflammatory and vascular dynamics, including angiogenesis, oxygen transport, and inflammatory cell activity. The model is calibrated using preclinical porcine lumpectomy histology and literature data. Model parameters are inferred using a multi-task Gaussian process surrogate within a Bayesian inference frame-work to align predictions with experimental observations and quantify uncertainty. Thus, this work provides a mechanistically grounded framework for inflammatory and vascular remodeling during breast cavity healing and provides a foundation for patient-specific prediction of healing and physical outcomes following lumpectomy.

We present a physics-informed surface autoencoder (PISA) framework for Kirchhoff-Love thin shell analysis. The method constructs global C1 surface parameterizations directly from unstructured point clouds for both single-patch surfaces homeomorphic to disks, and multi-patch parameterizations for closed genus-zero surfaces. In the multi-patch case, a classification network assigns probabilistic labels to points, and the autoencoder learns overlapping charts with smooth transitions, ensuring global C1 continuity. With the learned parameterizations, we introduce a decoder for the displacement field and compute differential geometric quantities such as the metric and second fundamental form in the reference and deformed surfaces. Then, we enforce equilibrium by minimizing the total potential energy. The approach is validated on classical shell benchmarks, including the Scordelis-Lo roof, pinched cylinder, and hemisphere under pressure. We showcase the flexibility of the framework with complex geometries such as the Stanford Bunny and dura mater. Compared with traditional spline-based parameterizations and existing machine learning approaches, PISA offers a pipeline for generating smooth surface maps for complex geometries and integrates the surface representation into the physics-informed solver. Importantly, the thin shell analysis pipeline proposed works directly with unstructured point cloud data. Thus, this PISA framework’s potential applications range from engineering structures to biological membranes such as heart valves, skin, and dura mater.

Calibrating thermomechanical material models from experiments is challenging because deformation, temperature, and force responses are strongly coupled, while measurements are usually restricted to specimen surfaces. We present a full-field calibration framework for coupled finite-strain thermomechanical material models using boundary displacement, reaction-force data, and temperature. The forward model is formulated as a near-incompressible thermo-hyperelastic problem with thermomechanical coupling derived from a Helmholtz free energy, and the inverse problem is posed as a PDE-constrained optimization problem with weighted observation terms for the available data streams. Reduced gradients are computed with adjoint sensitivities that are obtained by automatic differentiation, enabling gradient-based calibration of nonlinear transient thermomechanical systems. The formulation is first verified on synthetic examples involving uniform thermal preconditioning and localized transient rod contact, where the ground-truth parameters are recovered from full-field measurements and force observations. The same workflow is then applied to experimental thermomechanical data by first calibrating a hyperelastic mechanical baseline from cyclic equibiaxial loading and subsequently identifying thermal expansion and directional shrinkage parameters from surface-temperature and boundary-force histories. The results demonstrate that coupled thermomechanical parameters can be inferred from experimentally accessible surface data without requiring volumetric observations.

Digital materials fabricated by multi-material 3D printing are designed as controlled mixtures of stiff and compliant constituents, yielding effective responses that span more than an order of magnitude in apparent stiffness and exhibit strongly nonlinear, composition-dependent, and rate-dependent dissipative behavior. Classical finite-strain viscoelastic models represent such behavior with closed-form strain energy functions for equilibrium and non-equilibrium stresses as well as evolution of internal variables, which may limit flexibility when a single constitutive model is expected to generalize across materials and loading rates. Here, we present a data-driven multi-material constitutive modeling framework that generalizes a formulation by Bergström and Boyce. The proposed framework retains the structure of the classical model, namely multiplicative kinematics, invariant-based strain-energy functions, and a scalar dissipative evolution law directed along the normalized nonequilibrium deviatoric stress. For the equilibrium branch, the data-driven discovery framework either directly predicts closed-form model parameters as functions of composition or automatically constructs a polyconvex strain-energy function using neural ordinary differential equations (NODEs). The nonequilibrium branch kinetics are learned similarly, either by directly identifying closed-form parameters across compositions or by using appropriately constrained artificial neural networks. Using multi-rate uniaxial compression data across multiple material compositions, we show that the proposed formulation captures rate-dependent stiffness and hysteresis across compositions while preserving thermodynamic consistency.

Soft materials such as rubbers, hydrogels, and biological tissues undergo damage in the form of stiffness degradation without apparent changes in their stress-free geometry. Accurate simulation of this behavior is critical in applications ranging from soft robotics to the design of medical devices, yet two persistent challenges are the difficulty of constructing flexible, thermodynamically consistent constitutive models, and the mesh dependence of finite element solutions caused by strain softening. Here we address both challenges simultaneously by combining physics-augmented neural network constitutive models with a gradient-enhanced damage formulation implemented within the differentiable finite element framework JAX-FEM. The elastic strain energy and the damage yield function are each parameterized by input-convex neural networks (ICNNs), which enforce polyconvexity and satisfaction of the Clausius–Duhem inequality by design. The gradient-enhanced formulation introduces a non-local damage field governed by an additional partial differential equation, regularizing the spatial distribution of damage and eliminating mesh dependence. The implementation is validated through local stress–strain fits, single-element parametric studies, a mesh and solution strategy study for a uniform deformation case, and a notched plate simulation. The results demonstrate that the proposed framework enables flexible, data-driven, mesh-independent damage simulation for a broad class of soft materials. We anticipate that the open-source implementation will lower the barrier to adopting physics-augmented neural network constitutive models.

Skin growth and remodeling underlies health, disease, and treatments such as tissue expansion (TE). The mechanotransduction pathways in dermal fibroblasts are increasingly well characterized, and tissue-level growth has been described phenomenologically, but coupling between cell-level signaling and tissue-level growth remains poorly understood. We develop a dermal fibroblast signaling network through extensive literature data curation, comprising 151 reactions among 96 nodes. The inputs are mechanical stretch and eight ligands (TGF β , PDGF, FGF, IL1, IL6, TNF α , AngII, ET1); outputs of interest span ECM-enzymes (proMMP1/2/9, MMP1/2/9), ECM proteins (CImRNA, collagen I, fibronectin), and fibroblast activity ( α SMA, proliferation). Implemented as a logic-based ODE system, the network reproduces 82% of the calibration dataset and agrees with independent validation data. Sensitivity analysis reveals a tension-dependent regulation of signaling: at baseline tension, outputs are governed by many boosters (nodes that positively influence downstream targets) and one dominant brake, LATS1/2, whereas at high tension control consolidates and new, tension-specific regulators such as integrin (ITGB1) emerge. Multiple pathway axes converge on a few central regulators, producing pronounced crosstalk, most notably between TGF β and mechanical tension. Finally, linking the collagen outputs to a tissue-level growth formulation yields a bidirectional mechanical-biochemical coupling that reproduces tension-induced skin growth measured in a porcine TE model. This framework establishes a comprehensively calibrated dermal fibroblast signaling network coupled to tissue-level growth, opening opportunities for targeted TE interventions.

Accurately identifying the mechanical behavior of heterogeneous materials is a central challenge in materials science, with implications for the design of composites, metamaterials, and engineered biological tissue. Conventional inverse methods require closed-form constitutive models and are often restricted to simplified geometries or homogeneous properties, limiting their ability to capture complex, spatially varying material responses. Here, we introduce a fully data-driven framework for inverse characterization that recovers the complete constitutive behavior of heterogeneous solids directly from full-field displacement data, without prescribing a specific material law. Our approach combines neural ordinary differential equation (NODE) constitutive models, which inherently satisfy key thermodynamic and mathematical constraints, with a hyper-network that maps each material point to its local NODE, enabling continuous representation of arbitrary spatial variation in material properties. The loss function at the center of the method includes the strong form of equilibrium and traction boundary conditions. We demonstrate the method’s robustness on synthetic datasets, including heterogeneous isotropic and anisotropic materials, noise-contaminated measurements, and complex geometries, and validate it with digital image correlation experiments on 3D-printed elastomers. This framework provides a general, physically consistent route to inferring heterogeneous constitutive behavior from experimental data, offering new opportunities for accurate mechanical characterization across a broad range of material systems.

Accurate in vivo characterization of skin mechanical properties is essential for diagnostics and treatment planning across dermatological and surgical applications. Existing noninvasive techniques are limited in capturing the nonlinear and anisotropic behavior of skin. In this work, we propose a Bayesian inference framework that leverages active membranes to induce desired deformations and infer patient-specific skin properties from a measured strain field. A finite element model of skin-membrane interaction, parameterized using the Holzapfel-Gasser-Ogden model, is used to generate strain field data under various membrane actuation conditions. To overcome the computational cost of repeated simulations required for Bayesian sampling, we construct a data-driven surrogate using principal component analysis for dimensionality reduction and Gaussian process regression for rapid evaluation. Our approach enables probabilistic inference of key skin parameters, including shear modulus, fiber stiffness, dispersion, and orientation. An advatange of the proposed method is that inference of skin biomechanics does not require direct force measurements. Rather, the method relies on known properties of active membranes (which can be tested ahead of time). The method does require strain field measurements. Through synthetic studies, we demonstrate that our method accurately recovers most model parameters even under moderate levels of spatially correlated noise, and that multi-frame or multi-membrane observations significantly enhance identifiability. These results establish the potential of active membranes as a viable platform for noninvasive, in vivo skin biomechanics assessment.

We propose a data-driven constitutive framework for anisotropic damage mechanics based on the second-order damage tensor approach for both compressible and incompressible materials. The formulation is thermodynamically consistent and satisfies the Clausius-Duhem inequality. The strain energy density potentials are expressed as isotropic functions of the right Cauchy-Green deformation tensor, along with structural tensors that encode anisotropy either present in the virgin material or resulting from damage. To guarantee the polyconvexity condition, non-decreasing convex neural networks with inputs that ensure polyconvexity are used to parameterize the strain energy density potentials. The model vanishes in the undeformed state, fulfilling the normality condition. In contrast to classical [1-d] damage models, the expressiveness of the new data-driven model is enhanced by employing a family of nonlinear, convex, decreasing functions to capture the effect of damage. Damage evolution is governed through a damage potential, where the corresponding threshold is defined in terms of the damage conjugate forces. As a special case of the general formulation, a new anisotropic generic format is introduced to predict constitutive responses under damage-induced anisotropy in initially isotropic materials. To reduce the computational burden during training, a decoupled training scheme is introduced, and its accuracy is demonstrated in all numerical examples. These include benchmarks for incompressible isotropic, transversely isotropic, and compressible orthotropic materials. The framework is also validated against experimental data capturing anisotropic Mullins-type damage.

PURPOSE:Breast-conserving surgery (BCS) is the standard of care for early-stage breast cancer, offering recurrence and survival rates comparable to mastectomy while preserving healthy breast tissue. However, surgical cavity healing post-BCS often leads to highly variable tissue remodeling, including scar tissue formation and contracture, leading to visible breast deformation or asymmetry. These outcomes significantly impact patient quality of life but are difficult to predict due to the complex interplay between biologic healing processes and individual patient variability. To address this challenge, we extended our calibrated computational mechanobiological model of post-BCS healing by incorporating diagnostic imaging data to evaluate how patient-specific breast and tumor characteristics influence healing trajectories and deformation. METHODS:The model captured multi-scale biologic and biomechanical processes, including fibroblast activity, collagen remodeling, and nonlinear tissue mechanics, to simulate time-dependent tissue remodeling. Patient-specific breast and tumor geometries from preoperative magnetic resonance imaging (MRI) were integrated into finite element simulations of cavity healing, whose outputs trained Gaussian process surrogate models for rapid prediction of healing dynamics and breast surface deformation across diverse patient profiles. RESULTS:These models revealed how factors including breast density, cavity volume, breast volume, and cavity depth influence post-surgical cavity contraction and measures of breast surface deformation. CONCLUSION:This framework has the potential to provide a personalized, predictive tool for surgical planning and decision-making, enabling clinicians and patients to anticipate healing trajectories and cosmetic outcomes, with the goal of optimizing surgical results and enhancing patient quality of life.

We propose a new framework for identifying mechanical properties of heterogeneous materials without a closed-form constitutive equation. Given a full-field measurement of the displacement field, for instance as obtained from digital image correlation (DIC), a continuous approximation of the strain field is obtained by training a neural network that incorporates Fourier features to effectively capture sharp gradients in the data. A physics-based data-driven method built upon ordinary neural differential equations (NODEs) is employed to discover constitutive equations. The NODE framework can represent arbitrary materials while satisfying constraints in the theory of constitutive equations by default. To account for heterogeneity, a hyper-network is defined, where the input is the material coordinate system, and the output is the NODE-based constitutive equation. The parameters of the hyper-network are optimized by minimizing a multi-objective loss function that includes penalty terms for violations of the strong form of the equilibrium equations of elasticity and the associated Neumann boundary conditions. We showcase the framework with several numerical examples, including heterogeneity arising from variations in material parameters, spatial transitions from isotropy to anisotropy, material identification in the presence of noise, and, ultimately, application to experimental data. As the numerical results suggest, the proposed approach is robust and general in identifying the mechanical properties of heterogeneous materials with very few assumptions, making it a suitable alternative to classical inverse methods.

Accurate constitutive models of soft materials are crucial for understanding their mechanical behavior and ensuring reliable predictions in the design process. To this end, scientific machine learning research has produced flexible and general material model architectures that can capture the behavior of a wide range of materials, reducing the need for expert-constructed closed-form models. The focus has gradually shifted towards embedding physical constraints in the network architecture to regularize these over-parameterized models. Two popular approaches are input convex neural networks (ICNN) and neural ordinary differential equations (NODE). A related alternative has been the generalization of closed-form models, such as sparse regression from a large library. Remarkably, all prior work using ICNN or NODE uses the invariants of the Cauchy-Green tensor and none uses the principal stretches. In this work, we construct general polyconvex functions of the principal stretches in a physics-aware deep-learning framework and offer insights and comparisons to invariant-based formulations. The framework is based on recent developments to characterize polyconvex functions in terms of convex functions of the right stretch tensor U, its cofactor cofU, and its determinant J. Any convex function of a symmetric second-order tensor can be described with a convex and symmetric function of its eigenvalues. Thus, we first describe convex functions of U and cofU in terms of their respective eigenvalues using deep Holder sets composed with ICNN functions. A third ICNN takes as input J and the two convex functions of U and cofU, and returns the strain energy as output. The ability of the model to capture arbitrary materials is demonstrated using synthetic and experimental data.

Tissue expansion (TE), an essential technique in reconstructive surgery, leverages the growth of skin in response to stretch. However, human skin growth dynamics have not been evaluated in vivo. Previously, we quantified this process in a porcine model and developed a calibrated computational framework. Here, we create patient-specific finite element (FE) models of skin growth in TE using longitudinal 3D photos collected during TE treatment. These geometries enable Bayesian model calibration, accounting for uncertainties in boundary conditions, mechanical properties, and biological parameters. The framework incorporates prior knowledge from the porcine model as well as literature information on human skin mechanics. The likelihood function assesses alignment between predicted and observed geometries, and predicted and observed skin growth. To efficiently sample the posterior distribution, we use Markov Chain Monte Carlo (MCMC) with Gaussian process surrogates, reducing computational cost. This pipeline is demonstrated in five TE cases. Post-calibration, FE models closely match 3D photos, with errors below 2 mm on average. Notably, Bayesian calibration collapses the critical stretch parameter posterior distribution. This study presents the first in vivo measurement of human skin growth, confirming that FE models accurately capture TE in the clinical setting, and that porcine-derived parameters provide a strong prior for Bayesian calibration in the clinical case. These findings support the development of personalized digital twins for TE, enhancing surgical planning and outcomes. Statement of significance Tissue expansion (TE) is widely used in reconstructive surgery, particularly for breast reconstruction and pediatric defect repair. While skin growth has been quantified in animal models, this work provides the first clinical measurement of human skin growth during TE. We employ a Bayesian calibration framework to create personalized finite element (FE) simulations for five TE cases. The initial FE model is constructed from a patient's 3D photo taken at the start of treatment. Then, uncertainties in mechanical and biological parameters as well as boundary conditions are sampled and the model run. We use Gaussian process surrogates to replace the FE model. Calibration of parameters is done with 3D photos taken longitudinally during TE. This pipeline for skin digital twins can enhance personalized TE procedures, optimizing outcomes and reducing complications.

Fluctuations and propagation of cytosolic calcium levels at both the cellular and tissue levels show complex patterns, referred to as calcium signatures, that regulate growth, organ development, damage responses, and survival. The quantitative analysis of calcium signatures at the cellular level is essential for identifying unique patterns that coordinate biological processes. However, a versatile framework applicable to multiple tissue types, allowing researchers to compare, measure, and validate diverse responses and recognize conserved patterns across model organisms, is missing. Here, we present a post-processing tool, CalciumInsights, which leverages the R packages Shiny and Golem. This tool has a graphical user interface and does not require software programming experience to perform calcium signal analysis. The open-source software has a modular framework with standardized functionalities that can be tailored for various research approaches. CalciumInsights provides descriptive statistical analysis through various metrics extracted from dynamic calcium transients and oscillations, such as peak amplitude, area under the curve, frequency, among others. The tool was evaluated with fluorescence imaging data from three model organisms: Danio rerio , Arabidopsis thaliana , and Drosophila melanogaster , demonstrating its ability to analyze diverse biological responses and models. Finally, the open-source nature of CalciumInsights enables community-driven improvements and developments for enabling new applications. Author Summary This manuscript introduces CalciumInsights, an open-source tool for calcium signature analysis. Designed to be a versatile tool that works with various tissue types and biological systems, CalciumInsights has an easy-to-use graphical user interface. Our program simplifies metrics extraction while maintaining the quality of the analysis by integrating several algorithms. CalciumInsights stands out for its user-friendliness, ease of use, and robust data exploration features, such as tunable filters for improved accuracy. These features promote inclusivity and lower barriers to scientific research by making calcium signature analysis accessible to users of all programming skill levels. ### Competing Interest Statement The authors have declared no competing interest. NSF, 2120200

Breast reconstruction using tissue expanders is the primary treatment option following mastectomy. Although skin growth in response to chronic supra-physiological stretch is well-established, individual patient factors such as breast shape, volume, skin prestrain, and mechanical properties, create unique deformation and growth patterns. The inability to predict skin growth and deformation prior to treatment often leads to complications and suboptimal esthetic outcomes. Personalized predictive simulations offer a promising solution to these challenges. We present a pipeline for predictive computational models of skin growth in tissue expansion. At the start of treatment, we collect three-dimensional (3D) photos and create an initial finite element model. Our framework accounts for uncertainties in treatment protocols, mechanical properties, and biological parameters. These uncertainties are informed by surgeon input, existing literature on mechanical properties, and prior research on porcine models for biological parameters. By collecting 3D photos longitudinally during treatment, and integrating the data through a Bayesian framework, we can systematically reduce uncertainty in the predictions. Calibrated personalized models are sampled using Monte Carlo methods, which require thousands of model evaluations. To overcome the computational limitations of directly evaluating the finite element model, we use Gaussian process surrogate models. We anticipate that this pipeline can be used to guide patient treatment in the near future.

An unsupervised machine learning method is introduced to align medical images in the context of the large deformation elasticity coupled with growth and remodeling biophysics. The technique, which stems from the principle of minimum potential energy in solid mechanics, consists of two steps: Firstly, in the predictor step, the geometric registration is achieved by minimizing a loss function composed of a dissimilarity measure and a regularizing term. Secondly, the physics of the problem, including the equilibrium equations along with growth mechanics, are enforced in a corrector step by minimizing the potential energy corresponding to a Dirichlet problem, where the predictor solution defines the boundary condition and is maintained by distance functions. The features of the new solution procedure, as well as the nature of the registration problem, are highlighted by considering several examples. In particular, registration problems containing large non-uniform deformations caused by extension, shearing, and bending of multiply-connected regions are used as benchmarks. In addition, we analyzed a benchmark biological example (registration for brain data) to showcase that the new deep learning method competes with available methods in the literature. We then applied the method to various datasets. First, we analyze the regrowth of the zebrafish embryonic fin from confocal imaging data. Next, we evaluate the quality of the solution procedure for two examples related to the brain. For one, we apply the new method for 3D image registration of longitudinal magnetic resonance images of the brain to assess cerebral atrophy, where a first-order ODE describes the volume loss mechanism. For the other, we explore cortical expansion during early fetal brain development by coupling the elastic deformation with morphogenetic growth dynamics. The method and examples show the ability of our framework to attain high-quality registration and, concurrently, solve large deformation elasticity balance equations and growth and remodeling dynamics.

Cerebrospinal flow dynamics (CSF) plays a critical role in structural disorders of the central nervous system (CNS) and in the design of effective procedures for intrathecal drug delivery. Medical imaging techniques have only partially characterized CSF dynamics. Computational models have the potential to offer a high-resolution description of CSF flow and advance our mechanistic understanding. However, anatomically-accurate computational models of CSF dynamics in the spinal canal have largely ignored the compliance of the spinal tissues, which is critical to understand the pulse wave velocity and the craniocaudal decay of CSF pulsations. Here, we propose a mixed-dimensional fluid-structure interaction method that enables high-fidelity simulations of CSF dynamics on anatomically-accurate models of the spinal canal, considering the tissue compliance effects emerging from the dura mater and epidural fat. Our mixed-dimensional approach bypasses a critical computational bottleneck that emerges from the multiscale geometry of spinal tissues. Our results show that accurate modeling of tissue compliance is critical to capture key elements of CSF dynamics. This work opens new possibilities to control and optimize intrathecal drug delivery and to understand structural abnormalities of the CNS.

Reactive oxygen species (ROS) are second messengers that drive wound closure. However, the mechanism by which ROS regulate wound contraction to facilitate wound healing remains unclear. Here, we report that ROS counteract wound contraction by inhibiting the phosphorylation of myosin regulatory light chain. Acute ROS inhibition, through pharmacological perturbations, disturbs wound relaxation, delays wound closure, and impairs regrowth after amputation. Moreover, actomyosin inhibition relaxes tailfin contraction without impairing wound closure or regrowth. Overcontraction, on the other hand, impedes wound closure. Meanwhile, chronic depletion of epithelial ROS during embryonic development, achieved through morpholino-mediated knockdown of the duox gene, alters tissue stiffness, as measured using atomic force microscopy-based nanoindentation. Despite a reduced contraction force, the wound also appears to be overcontracted, with delayed healing and regrowth. An in silico linear elasticity simulation to calculate the second principal stress based on node-wise prescribed displacement recapitulated the contraction dynamics during acute and chronic ROS inhibition. Together, our results provide a novel understanding of how ROS facilitate wound closure, a process instrumental in restoring tissue integrity and maintaining homeostasis.