Biomechanics and Modeling in Mechanobiology

The response of the brain to the explosion induced primary blast waves is actively sought. Over the past decade, reasonable progress has been made in the fundamental understanding of bTBI using head surrogates and animal models. Yet, the current understanding of how blast waves interact with the human is in nascent stages, primarily due to lack of data in humans. The biomechanical response in human is critically required so that connection to the aforementioned bTBI models can be faithfully established. Here, using a detailed, full-body human model, we elucidate the biomechanical cascade of the brain under a primary blast. The input to the model is incident overpressure as achieved by specifying charge mass and standoff distance through ConWep. The full-body model allows to holistically probe short- (<5 ms) and long-term (200 ms) brain biomechanical responses. The full-body model has been extensively validated against impact loading in the past. In this work, we validate the head model against blast loading. We also incorporate structural anisotropy of the brain white matter. Blast wave human interaction is modeled using a conventional weapon modeling approach. We demonstrate that the blast wave transmission, linear and rotational motion of the head are dominant pathways for the biomechanical loading of the brain, and these loading paradigms generate distinct biomechanical fields within the brain. Blast transmission and linear motion of the head govern the volumetric response, whereas the rotational motion of the head governs the deviatoric response. We also observe that blast induced head rotation alone produces a diffuse injury pattern in white matter fiber tracts. Lastly, we find that the biomechanical response under blast is comparable to the impact event. These insights will augment laboratory and clinical investigations of bTBI and help devise better blast mitigation strategies.

Residual stresses in arteries are commonly revealed by the opening of a ring after a radial cut. Glycosaminoglycans (GAGs) contribute to this response, but fixed-charge-density (FCD)-driven Donnan swelling alone does not fully explain the opening-angle reduction measured after enzymatic GAG depletion. We therefore tested whether the transmural FCD profile defines a removable preferred-stretch field superposed on a structural field retained after GAG depletion. A reduced analytical-computational axisymmetric closure framework was applied to regional-average measurements from the ascending aorta, arch, and descending porcine thoracic aorta. Each control state was fitted using its measured circumferential opening angle, geometry, material properties, and through-wall FCD profile. GAG depletion was represented by removing the FCD-linked preferred-stretch component. One coefficient governing this removable component was selected jointly from the three measured regional post-depletion angles. A one-layer wall was the primary parsimonious model; a two-layer wall tested anatomical robustness. The one-layer model fitted a shared coefficient of -1.4226 x 10-3 (mEq/L)-1 and predicted depleted angles of 82.639{degrees}, 44.032{degrees}, and 19.979{degrees}, compared with measured values of 85{degrees}, 41{degrees}, and 18{degrees} (three-region RMSE 2.50{degrees}). The two-layer model fitted -1.51746 x 10-3 (mEq/L)-1 and predicted 82.972{degrees}, 44.273{degrees}, and 18.534{degrees} (RMSE 2.24{degrees}). Thus, layer differentiation improved aggregate fit only modestly. These findings support a parsimonious mechanism in which GAG depletion removes an FCD-shaped circumferential preferred-stretch component while most residual-stress architecture remains in a structural field retained after depletion. Donnan swelling remains mechanically relevant, but it is insufficient alone to explain the measured regional response. Statement of SignificanceGlycosaminoglycans are charged extracellular-matrix constituents that influence arterial residual stresses through osmotic effects and interactions with the fibrous matrix. We tested whether the through-wall FCD pattern also plays a role in the development of residual stresses and can be modeled as a removable circumferential preferred-stretch component. One coefficient linking that pattern to the GAG depletion response explained the post-depletion opening angles of the ascending aorta, arch, and descending aorta within 3.3{degrees} using a one-layer model. A two-layer model improved aggregate error only modestly. The result provides a compact mechanistic link between extracellular-matrix composition and residual opening without requiring a separately fitted GAG effect in each region.

PurposeBiomaterial and stem cell delivery are promising approaches to treating myocardial infarction. However, the mechanical and biochemical mechanisms underlying the therapeutic benefits require further clarification. This study aimed to assess the deformation of stem cells injected with the biomaterial into the infarcted heart. MethodsA microstructural finite element model of a mid-wall infarcted myocardial region was developed from ex vivo microcomputed tomography data of a rat heart with left ventricular infarct and intramyocardial biomaterial injectate. Nine cells were numerically seeded in the injectate of the microstructural model. The microstructural and a previously developed biventricular finite element model of the same rat heart were used to quantify the deformation of the cells during a cardiac cycle for a biomaterial elastic modulus (Einj) ranging between 4.1 and 405,900 kPa. ResultsThe transplanted cells deformation was largest for Einj = 7.4 kPa, matching that of the cells, and decreased for an increase and decrease in Einj. The cell deformation was more sensitive to Einj changes for softer (Einj [&le;] 738 kPa) than stiffer biomaterials. ConclusionsCombining the microstructural and biventricular finite element models enables quantifying micromechanics and signalling of transplanted cells in the heart. The approach offers a broader scope for in silico investigations of biomaterial and cell therapies for myocardial infarction and other cardiac pathologies.

Our understanding of the process of formation of gyri (ridges) and sulci (furrows) in the cerebrum during development is moving beyond the role of neurons. Glial cells such as astrocytes, which are the most common cell type in the brain, are especially prominent under the gyri and have been shown to be essential for gyrification in ferrets. Their dysfunction has been linked to a host of neurodevelopmental diseases and disorders in humans, leading to abnormal folding patterns, hence, it is crucial to understand their role in the mechanics of folding. In this work, we propose two hypotheses of how astrocytes affect cortical folding. Our previous study demonstrated that astrocytes proliferate in the white matter (subcortex), and that inhibiting this process impairs gyrification. This leads to the pushing hypothesis, where astrocytes push up the cortex outwards, leading to formation of folds. On the other hand, ex vivo studies demonstrate areas of the cortex and subcortex that experience tension, due to the differential growth between materials in the brain tissue. This leads to the pulling hypothesis, where astrocytes experience tension from the surrounding tissue, leading to their proliferation, distribution of tensile stresses, and initiation of growth in those regions. Using the theory of finite growth, we implement these hypotheses via morphogenetic growth (pushing) and stress-driven (pulling) growth criteria. We find that morphological trends during development between the pushing and pulling effects are not dissimilar, with a comparable gyrification index. The stress distributions from both models also show common features, but the pulling effect shows tension in the subcortex, which matches trends observed in experiments. Therefore, it is more likely that the astrocytes affect gyrification by proliferating as a response to tensile cues and decrease the tension they experience, leading to deeper folds, rather than astrocytes independently proliferating under a gyri and then pushing the cortex up.

Constitutive models of myocardial mechanics form a cornerstone of personalized cardiac simulations and cardiac digital twins. Researchers traditionally prescribe these models a priori and calibrate them from ex vivo tissue experiments, even though tissue excision alters loading conditions, removes residual stresses, and eliminates important physiological interactions. Multimodal cardiac MRI now provides subject-specific ventricular geometry, deformation, and myocardial microstructure, yet current inverse approaches still rely on predefined constitutive laws. Here we present the first framework to discover constitutive models of passive myocardial mechanics directly from in vivo cardiac imaging data by embedding a constitutive artificial neural network within a nonlinear finite element model of ventricular filling. Using multimodal cardiac MRI that combines ventricular geometry, deformation, and microstructure from a representative healthy individual, the framework identifies sparse, mechanically admissible strain-energy functions without prescribing their form a priori. The best-performing model contains only two fiber- and two sheet-invariant terms, achieves a mean displacement error of 1.62 mm, and reduces the error of the widely used Guccione and Holzapfel models by 34.14% and 26.01%. The discovered models indicate that fiber- and sheet-related anisotropic mechanisms dominate the passive mechanical response during physiological ventricular filling. More broadly, this work establishes a non-invasive strategy for subject-specific constitutive discovery from cardiac imaging data and lays the foundation for personalized cardiac simulations and cardiac digital twins.

The interplay between cell-cell and cell-substrate interactions is complex yet necessary for the formation and well-functioning of tissues. The same mechanosensing mechanisms used by the cell to sense its extracellular matrix, also play a role in intercellular interactions. We used the discrete element method to develop a computational model of a deformable cell that includes subcellular components responsible for mechanosensing. We modeled a cell pair in 3D on a patterned substrate, a simple laboratory setup to study intercellular interactions. We explicitly modeled focal adhesions between the cells and the substrate, and adherens junctions between cells. These mechanosensing adhesions matured; their disassembly rate was dictated by the force they carry. We also modeled stress fibers which bind the discrete adhesions and contract. The mechanosensing fibers strengthened upon stalling and exerted higher forces. Traction exerted on the substrate was used to generate maps displaying the magnitude of the tractions along the cell-substrate interface. Simulated traction maps are compared to experimental maps obtained via traction force microscopy. The model recreates the dependence on substrate stiffness of the tractions spatial distribution across the cell-substrate interface, the contractile moment of the cell pair, the intercellular force, and the number of focal adhesions. It also recreates the phenomenon of cell decoupling, in which cells exert forces separately when substrate stiffness increases. More importantly, the model provides viable molecular explanations for decoupling. It shows that the implemented mechanosensing mechanisms are responsible for competition between different fiber-adhesion configurations present in the cell pair. The point at which an increasing substrate stiffness becomes as high as that of the cell-cell interface is the tipping point at which configurations that favor cell-substrate adhesion dominate over those favoring cell-cell adhesion. This competition is responsible for decoupling. Additionally, we learn that extent of decoupling is modulated by adherens junction maturation.\n\nStatement of SignificanceCells are sensitive to mechanical factors of their extracellular matrix while simultaneously in contact with other cells. This creates complex intercellular interactions that depend on substrate stiffness and play a role in processes such as development and diseases like cardiac arrhythmia, asthma, and cancer. The simplest cell collective system in vitro is a cell pair on a patterned substrate. We developed a computational model of this system which explains the role of molecular adhesions and contractile fibers in the dynamics of cell-cell interactions on substrates with different stiffness. It is one of the first models of a deformable cell collective based on mechanical principles. It recreates cellular decoupling, a phenomenon in which cells exert forces separately, when substrate stiffness increases.

The mechanical behavior of right ventricular (RV) myocardium is governed by its anisotropic microstructure, yet constitutive models that account for fiber dispersion and enable reliable parameter identification remain limited. In this study, we propose a physics-embedded constitutive neural network framework for automated discovery of strain energy functions and microstructural parameters from experimental data. The model is formulated within an incompressible, orthotropic hyperelastic setting using invariant-based representations. Fiber, sheet, and normal directions are incorporated through a rotated structural basis, and dispersion effects are modeled using a generalized structure tensor approach. The framework is trained on multi-axial mechanical data from ovine RV myocardium, including uniaxial tension-compression and simple shear tests. We investigate two training scenarios: (i) full datasets containing both tensile and compressive regimes and (ii) datasets restricted to tensile loading. In both cases, the model accurately reproduces the measured stress-strain responses and identifies sparse, interpretable constitutive models which involve isotropic, anisotropic, and coupling invariants. However, the identifiability of microstructural parameters strongly depends on the available loading conditions. While tensile-only data yield higher predictive accuracy, they result in non-unique or biased estimates of fiber dispersion. In contrast, inclusion of compressive data enables consistent identification of dispersion parameters by separating fiber and matrix contributions. These results highlight the importance of multi-axial loading data for robust parameter identification and demonstrate the capability of constitutive neural network-based approaches for data-driven modeling of anisotropic soft tissues.

Moderate to extreme preterm birth (<32 weeks gestation) affects cardiopulmonary structure and function and is associated with increased risk of heart failure through adulthood. The rat hyperoxia (Hx) model (term born; postnatal Hx exposure) captures biventricular changes, including at the cell- and organ-scale, and pulmonary vascular remodeling seen in preterm humans. However, synthesizing these measures across scales and organ systems is challenging. We hypothesized that in-silico modeling of biventricular mitochondrial, myofiber, and organ-scale function plus circulatory function could capture key features of cardiopulmonary abnormalities due to preterm birth. Therefore, we calibrated a multiscale model to subject-specific biventricular pressure-volume data previously obtained from Hx rats alongside normoxic (Nx) controls to investigate the abnormalities in cardiopulmonary function at multiple scales in this animal model of human preterm birth. The calibrated model demonstrates excellent agreement with the data and captures the expected pulmonary vascular changes and right ventricular dilation seen in preterm born children. Our multiscale modeling approach captures cardiopulmonary abnormalities across spatial scales and provides an innovative approach to explore the consequences of preterm birth beyond preclinical experimental data alone. This is a foundational step in understanding the impact of preterm birth on cardiopulmonary disease in childhood as well as adulthood.

A previously developed computational model for cardiovascular mechanics is here calibrated using typical values of 22 clinically observed hemodynamic properties in healthy human subjects. The model includes spatially resolved representations of the four heart chambers. The left ventricle is represented as a truncated thick-walled prolate spheroid, with three modes of deformation (short and long axis contraction and torsion). The other chambers are represented as full or partial thick-walled spherical shells. Wave propagation and reflection in the aorta are represented using a one-dimensional model. The closed-loop system is completed using lumped elements representing vascular resistances, compliances and inertances. The resulting system of coupled ordinary differential equations can be solved computationally in less than 0.1 s per cardiac cycle. In the present study, the values of 19 key input parameters to the model are estimated by minimizing the deviation of model predictions from the 22 observed hemodynamic properties. The resulting calibrated reference model provides a baseline for theoretical studies exploring the relationship between fundamental properties of the cardiovascular system, such as ventricular contractility and stiffness or vascular resistance and compliance, and clinically available measurements such as blood pressures, chamber volumes or valve flow waveforms.

Cells sense and respond to the mechanical properties of their environment, yet the minimal physical principles sufficient to reproduce mechanotransduction and durotaxis remain debated. This work introduces FraCeMM, a physics-first mechanochemical simulation framework coupling stochastic ligand-integrin-talin binding to a deformable soft-body cell model on an elastic substrate. Without imposed polarity, directional cues, or migration rules, the model reproduces hallmark mechanobiological behaviors including stiffness-dependent spreading, traction reinforcement, focal adhesion asymmetry, and directed durotaxis. A finite pool of adhesion molecules, mechanically coupled through elastic linkages, drives emergent force asymmetry and polarization via self-consistent feedback between stochastic binding, molecular availability, and substrate stiffness. Despite minimal assumptions and a coarse-grained molecular representation, resulting traction forces, adhesion loads, and migration speeds fall within experimentally reported ranges. These results support the view that local force balance, limited adhesion resources, and mechanically binding are sufficient to generate adaptive mechanosensing and directed migration, establishing a transparent and extensible foundation for computational mechanobiology.

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.

Nucleus deformation has been shown to play a key role in cell mechanotransduction and migration. Therefore, it is of wide interest to accurately characterize nucleus mechanical behavior. In this study we present the first computational investigation of the in-situ deformation of a heterogeneous cell nucleus. A novel methodology is developed to accurately reconstruct a three-dimensional finite element spatially heterogeneous model of a cell nucleus from confocal microscopy z-stack images of nuclei stained for nucleus DNA. The relationship between spatially heterogeneous distributions microscopic imaging-derived greyscale values, shear stiffness and resultant shear strain is explored through the incorporation of the reconstructed heterogeneous nucleus into a model of a chondrocyte embedded in a PCM and cartilage ECM. Externally applied shear deformation of the ECM is simulated and computed intra-nuclear strain distributions are directly compared to corresponding experimentally measured distributions. Simulations suggest that the nucleus is highly heterogeneous in terms of its mechanical behaviour, with a sigmoidal relationship between experimentally measure greyscale values and corresponding local shear moduli (n). Three distinct phases are identified within the nucleus: a low stiffness phase (0.17 kPa [&le;] n [&le;] 0.63 kPa) corresponding to mRNA rich interchromatin regions; an intermediate stiffness phase (1.48 kPa [&le;] n [&le;] 2.7 kPa) corresponding to euchromatin; a high stiffness phase (3.58 kPa [&le;] n [&le;] 4.0 kPa) corresponding to heterochromatin. Our simulations indicate that disruption of the nucleus envelope associated with lamin-A/C depletion significantly increases nucleus strain in regions of low DNA concentration. A phenotypic shift of chondrocytes to fibroblast-like cells, a signature for osteoarthritic cartilage, results in a 35% increase in peak nucleus strain compared to control. The findings of this study may have broad implications for the current understanding of the role of nucleus deformation in cell mechanotransduction.

The evolution of palliative surgical procedures for children born with congenital heart defects has proven remarkably successful in extending life, but the resulting non-physiological circulation predisposes to myriad sequelae that compromise quality of life and overall life span. Among these procedures, standard-of-care Fontan completion surgery bypasses the nonfunctional ventricle and provides steady flow of deoxygenated blood to the lungs via a synthetic conduit that typically connects the inferior vena cava to a pulmonary artery. This altered circulation reduces cardiac output, elevates central venous pressures, and possibly contributes to adverse remodeling of the pulmonary vessels. There is, therefore, strong motivation to develop a next generation Fontan conduit capable of serving as a sub-pulmonic pulsatile pump, and there are now several reports of initial attempts. None of these studies have been driven by biomechanical considerations, however, and none have achieved the desired functionality. We thus present a novel analytical framework to improve design and guide fabrication by focusing on the microstructure and material properties of the contractile myofibers and associated passive matrix. Our optimized designs simultaneously ensure desired levels of stroke volume, ejection fraction, and pressure generation given constraints on Frank-Starling myofiber contraction and the limited space within the thoracic cavity of a three-to four-year-old child. This analysis also highlights the need to minimize any associated axial force or torque generation that a pulsatile conduit could transmit to the host vessels at the requisite anastomoses.

The aorta shows significant regional variation in geometry and composition. This complexity makes numerical modeling challenging, as it requires identifying material parameters. Typically, the Holzapfel-Gasser-Ogden model is used. However, it suffers from nonuniqueness and sensitivity to outliers, which can obscure biological variation. In addition, standard compressible formulations with a volumetric-isochoric split fail to couple volumetric and anisotropic responses. To address these issues, a regularized dual-estimation framework was introduced. This framework combines a global baseline estimator with local refinement while maintaining structural material continuity. Furthermore, it uses a Modified Anisotropic model to improve the representation of compressibility physics. For validation, the approach included uniaxial extension and protein quantification from Wistar rats. The results show that the proximal ascending/aortic-arch segment is most compliant at low stretch, whereas the abdominal aorta stiffens earlier and becomes fiber-dominated at lower stretch levels. Notably, these trends align directionally with regional composition. However, the fitted stress components are model-based descriptors rather than direct measurements of individual constituents.

The corpus callosum is the largest white matter structure connecting the two cerebral hemispheres and is anatomically divided into three major subregions along the anteroposterior axis: the genu, midbody, and splenium. The splenium is frequently affected in traumatic head impacts, yet the biomechanical basis for this selective vulnerability remains poorly understood. Clinical studies have long hypothesized that the falx cerebri contributes to the splenial susceptibility because of its close anatomical relationship with the posterior corpus callosum, although direct verification is lacking. To address this, a high-resolution finite element head model with explicit representations of the genu, midbody, and splenium was employed. Two model variants, differing only in the presence or absence of an anatomically and mechanically detailed falx, were used to simulate ten head impacts covering a range of loading directions and severities. Peak strain, strain rate, and shear stress were quantified in each corpus callosum subregion and compared using linear mixed-effects models. The results showed that inclusion of the falx altered the regional distribution of mechanical responses within the corpus callosum. Across the simulated impacts, the splenium consistently exhibited greater strain, strain rate, and shear stress than the genu and midbody when the falx was present. In contrast, these preferentially larger splenial deformation were not consistently observed when the falx was absent. Statistical analyses demonstrated significant region-dependent effects of the falx, with falx-induced increases in strain, strain rate, and shear stress being significantly greater in the splenium than in the genu and midbody (p < 0.05). These findings verified the hypothesis that the falx selectively amplified mechanical loading within the splenium, thereby contributing to its heightened vulnerability to injury. This work provides a plausible biomechanical explanation for the frequent involvement of the splenium in brain trauma patients and highlights the heterogeneous influence of the falx on mechanical responses across corpus callosum subregions.

Mechanical loading regulates chondrocyte health in articular cartilage. While physiological stimuli maintain homeostasis, supra-physiological stimuli from joint injuries disrupt it, leading to osteoarthritis (OA). OA is a prevalent degenerative joint disease affecting millions worldwide. OA progression involves complex mechanical and biochemical interactions across multiple length scales, which are challenging to investigate experimentally. In silico models provide an effective framework to explore these mechanisms. This study developed an integrated multiscale modeling framework for articular cartilage. It combined finite element (FE) models at tissue and cellular scales with an intracellular gene/protein regulatory network. The network incorporated key chondrocyte mechanotransduction and inflammatory pathways. A Hills function was used to link cellular forces from the FE model to a mechanical loading input to the regulatory network. Hills function constants were calibrated using a genetic algorithm approach. Calibration was performed by matching experimental and simulated expressions of COL-II and ADAMTS5 of cartilage explants under 20% dynamic compression. As a validation step, model simulations were performed at 10% dynamic compression of cartilage explants. COL-II and ACAN were overestimated, and ADAMTS5 was underestimated compared with experimental data. Furthermore, predicted sGAG loss matched the trend of experimental data. Simulated chondrocyte responses for varying spatial locations revealed spatial heterogeneity of chondrocyte activity. Over-all, the multiscale modeling workflow developed in this study provides a first step towards a powerful tool to increase the understanding of the complex interplay of mechanics and inflammation in articular cartilage. By integrating tissue, cellular, and intracellular scales, it offers a comprehensive framework for studying cartilage mechanobiology and guiding future therapeutic strategies. HighlightsO_LIDeveloped an integrated multiscale model linking tissue, cellular mechanics, and gene regulation in articular cartilage. C_LIO_LICoupled cellular mechanical forces to gene regulatory networks using Hills function approach. C_LIO_LICalibrated Hills function parameters via genetic algorithm using experimental cartilage explant compression data. C_LIO_LIPredicted spatial heterogeneity of chondrocyte activity and cartilage biomarker expression under dynamic compression C_LIO_LIEstablished computational framework can be used for studying cartilage mechanobiology and osteoarthritis therapeutic strategies C_LI

PurposeComputational models of the uterus during pregnancy enable analysis of electro-chemo-mechanical pathways to predict labor timing and guide treatment planning. We aim to develop a robust image-based modeling pipeline to investigate uterine passive mechanics during late pregnancy. MethodsA parametric model of the uterus and cervix was created using a patients MRI measurements at 38 weeks of gestation. Inspired by advances in cardiac mechanics models, we created Laplace-Dirichlet solutions to inform tissue domains, fiber structure within the uterus and cervix, and spatially varying Robin boundary conditions. Prior imaging and mechanical testing data were used to fit material parameters. Boundary condition parameters were tuned to match the displacements of a previously established approach that employed contact with surrounding tissue. The tissue mechanical response to a physiologic load was assessed across varying material properties and fiber architectures. ResultsDiscrepancies in nodal displacements between the current approach and the contact-based model were limited to 3.4 {+/-} 1.8 mm, yielding nearly 90 % computational savings. Uterine tensile strains were more sensitive to ground substance elastic modulus (E) compared to fiber properties. Reduced E and fiber stiffness increased cervical strains and compression. Fiber dispersion and architecture modulated the opening of the cervical internal ostium but had a reduced impact on compression. ConclusionWe developed a novel workflow for modeling passive uterine mechanics, informed by patient-specific measurements and in vitro mechanical tests. The robust workflow may prove useful for studying labor progression and conducting longitudinal studies to enhance our understanding of normal and pathological pregnancies.

Many congenital conditions and surgical interventions perturb the hemodynamics experienced by proximal pulmonary arteries during early postnatal development, thus leading to differential gene expression and associated changes in vascular structure and function. Among these, pathologic conditions include patent ductus arteriosus, pulmonary atresia and stenosis, and hypoxemia-induced pulmonary hypertension while surgical interventions include the placement of a Blalock-Taussig shunt as well as Glenn, Fontan, and Norwood procedures. Despite the significant morbidity associated with these diverse conditions, there has been little attention directed towards understanding natural postnatal development of pulmonary arteries from both biological and mechanical perspectives. With-out such information, we cannot truly understand the phenotype of the affected pulmonary artery, which is fundamental to improving diagnosis, treatment, and prognosis. In this paper, we present novel data from wild-type mice that document normal postnatal changes in select gene expression, vascular wall composition, and biomechanical properties of proximal pulmonary arteries. These findings enabled the establishment of a novel, data-informed computational model of pulmonary artery development capable of simulating outcomes in response to perturbations in the pulmonary artery hemodynamic environment.

Tumor spheroid mechanics arise from both the mechanical properties of individual cells and the adhesive interactions that organize them into tissues. The relative contribution of these two factors to the bulk mechanical behavior, however, remains difficult to disentangle experimentally. Here, we develop a computational model of micropipette aspiration to compare the mechanical response of isolated cells and multicellular spheroids within a common computational framework. By independently varying single-cell stiffness and cell-cell adhesion, we quantify their effects on aspiration dynamics, effective elastic modulus, and viscoelastic relaxation. Our results show that increasing single-cell stiffness substantially alters the mechanics of isolated cells but has limited influence on the effective elastic modulus of multicellular spheroids. In contrast, changes in cell-cell adhesion produce pronounced effects on spheroid effective elastic modulus. Nevertheless, both parameters increase the retardation time governing the transition from the initial elastic response to long-time viscous deformation. These findings suggest that multicellular elasticity is governed primarily by intercellular mechanical coupling, whereas the dynamical response to applied stress depends jointly on cell-scale mechanics and cell-cell adhesion.

Mechanical forces applied to cells are known to regulate a wide variety of biological processes. Recent studies have supported that mechanical forces can cause nuclear deformation, leading to significant alterations in the gene expression and chromatin landscape of the cell. While the stresses and strains applied to cells is it is often known or controlled experimentally on a macroscopic length scale, it is often unclear what the actual forces and displacements are at the microscopic level of the cell. In this work, we created a model of cell deformation during application of mechanical stretch to cultured cells growth on a flexible membrane. This configuration is commonly used is in experimental studies as a means to apply controlled mechanical strains to adherent cultured cells. The parameters used in the study were used for application of strain to a mesenchymal stem cell stretched on a membrane. computational model was created to simulate the stresses and strains within the cell under a variety of stain amplitudes, waveforms and frequencies of mechanical loading with the range of commonly used experimental systems. The results demonstrate the connection between mechanical loading parameters applied through the flexible membrane and the resulting stresses and strains within the cell and nucleus. Using a viscoelastic model of chromatin, we connected the results provide to a rough model of resulting deformation within chromatin from the forces applied to the nucleus. Overall, the model is useful in providing insight between experimentally applied mechanical forces and the actual forces within the cell to better interpret the results of experimental studies. Statement of SignificanceIn this work, we created a computational model of the mechanical stretching of cell on a flexible membrane under cyclic mechanical loading. This model provides insight into the forces and displacements inside of cell that result from that application of stretch. As many experiments use this set up, our work is relevant to interpreting many studies that use mechanical stretch to stimulate mechanotransduction.