Biomechanics and Modeling in Mechanobiology

Coronary autoregulation maintains relatively constant myocardial flow over a wide range of perfusion pressures through myogenic, shear-dependent, and metabolic control mechanisms. Understanding this phenomenon is challenging due to the coupled nature of these mechanisms and their heterogeneous effects throughout the coronary tree. In this study we developed a novel microstructurally-motivated model of coronary autoregulation based on constrained mixture theory, with anatomical and structural parameters calibrated through a homeostatic optimization framework. Autoregulation was simulated at three myocardial depths (subepicardium, midwall, and subendocardium), with the calibrated model accurately reproducing baseline hemodynamics and autoregulatory responses. For changes in epicardial pressure, our model reproduced experimentally measured subendocardium-to-subepicardium flow ratios (ENDO/EPI) and changes in vessel diameter, demonstrating its predictive capability. Furthermore, we extended Womersleys theory to simulate phasic coronary hemodynamics with a time-varying intramyocardial pressure. This microstructurally-motivated framework provides a mechanistic foundation for investigating coronary autoregulation and long-term vascular growth and remodeling in pathphysiological conditions. SummaryO_LICoronary autoregulation is defined as the capability of the coronary circulation to maintain the blood supply to the heart over a range of perfusion pressures. This phenomenon is facilitated through intrinsic mechanisms that control the vascular resistance by regulating the mechanical function of smooth muscle cells. Understanding the mechanisms involved in coronary autoregulation is one of the most fundamental questions in coronary physiology. C_LIO_LIThis paper presents a structurally-motivated coronary autoregulation model that uses a nonlinear continuum mechanics approach to account for the morphometry and vessel wall composition in two coronary trees in the subepicardial and subendocardial layers. C_LIO_LIThe model is calibrated against diverse experimental data from literature and is used to study heterogeneous autoregulatory response in the coronary trees. This model drastically differs from previous models, which relied on lumped parameter model formulations, and is suited to the study of long-term pathophysiological growth and remodeling phenomena in coronary vessels. C_LI

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.

The human aorta undergoes complex morphologic changes that indicate the evolution of disease. Finite element analysis enables the prediction of aortic pathologic states, but the absence of a biomechanical understanding hinders the applicability of this computational tool. We incorporate geometric information from computed tomography angiography (CTA) imaging scans into finite element analysis (FEA) to predict a trajectory of future geometries for four aortic disease patients. Through defining a geometric correspondence between two patient scans separated in time, a patient-specific FEA model can recreate the deformation of the aorta between the two time points, showing pathologic growth drives morphologic heterogeneity. A shape-size geometric feature space plotting the variance of the shape index versus the inverse square root of aortic surface area ({delta}[S] vs. [Formula]) quantitatively demonstrates the simulated breakdown in aortic shape. An increase in {delta}[S] closely parallels the true geometric progression of aortic disease patients.

Coronary autoregulation is a short-term response manifested by a relatively constant flow over a wide range of perfusion pressures for a given metabolic state. This phenomenon is thought to be facilitated through a combination of mechanisms, including myogenic, shear dependent, and metabolic controls. The study of coronary autoregulation is challenging due to the coupled nature of the mechanisms and their differential effects through the coronary tree. In this paper, we developed a novel framework to study coronary autoregulation based on the constrained mixture theory. This structurally-motivated autoregulation model required calibration of anatomical and structural parameters of coronary trees via a homeostatic optimization approach using extensive literature data. Autoregulation was then simulated for two different coronary trees: subepicardial and subendocardial. The structurally calibrated model reproduced available baseline hemodynamics and autoregulation data for each coronary tree. The autoregulation analysis showed that the diameter of the intermediate and small arterioles varies the most in response to changes in perfusion pressure. Finally, we demonstrated the utility of the model in two application examples: 1) response to drops in epicardial pressure, and 2) response to drug infusion in the coronary arteries. The proposed structurally-motivated model could be extended to study long-term growth and remodeling in the coronary circulation in response to hypertension, atherosclerosis, etc. Key pointsO_LICoronary autoregulation is defined as the capability of the coronary circulation to maintain the blood supply to the heart over a range of perfusion pressures. This phenomenon is facilitated through intrinsic mechanisms that control the vascular resistance by regulating the mechanical function of smooth muscle cells. Understanding the mechanisms involved in coronary autoregulation is one of the most fundamental questions in coronary physiology. C_LIO_LIThis paper presents a structurally-motivated coronary autoregulation model that uses a nonlinear continuum mechanics approach to account for the morphometry and vessel wall composition in two coronary trees in the subepicardial and subendocardial layers. C_LIO_LIThe model is calibrated against diverse experimental data from literature and is used to study heterogeneous autoregulatory response in the coronary trees. This model drastically differs from previous models, which relied on lumped parameter model formulations, and is suited to the study of long-term pathophysiological growth and remodeling phenomena in coronary vessels. C_LI

Current models propose that osteogenesis occurs in regions of high mechanical stimuli such as strain, fluid velocity, or pore pressure. However, in vivo experiments on mouse tibiae under cantilever loading revealed new bone formation exclusively on the anterolateral side, despite the opposite posteromedial surface experiencing comparable magnitudes of these stimuli. This indicates that individual stimulus magnitude is insufficient and suggests an interactive mechanism among them. To investigate this, a poroelastic finite element model was developed to quantify the combined effects of load-induced fluid velocity and pore pressure. Tensile loading generated negative pore pressure, stretching osteocyte processes, while compressive loading produced positive pore pressure, compressing them. Since fluid flow exerts drag forces that also stretch osteocytes, the combined effect of flow and negative pressure on the tensile side was hypothesized to enhance mechanotransduction and trigger osteogenesis. Four potential stimuli were evaluated: dissipation energy density arising from (i) pore pressure, (ii) fluid velocity, (iii) their non-interactive sum, and (iv) their interaction. Comparison with in vivo data showed that only the interactive dissipation energy density accurately predicted both the spatial pattern and rate of new bone formation under high, low, and rest-inserted loading regimes. These results establish that the interaction between fluid velocity and pore pressure, rather than their independent contributions, governs load-induced osteogenesis. The proposed framework advances the mechanistic understanding of bone adaptation and offers a predictive basis for optimizing mechanical and clinical interventions to promote bone formation and mitigate bone loss.

Understanding the intertwining of biology and mechanics in tissue architecture is a challenging issue, especially when it comes to the 3D tissue organization. Addressing this challenge requires both a biological model allowing multiscale observations from the cell to the tissue, and theoretical and computational approaches allowing the generation of a synthetic model, relevant to the biological model, and allowing access to the mechanical constraints experienced by the tissue. Here, using human colon epithelium monolayer organoid as biological model, and combining vertex and FEM approaches, we generated a comprehensive elastic finite element model of the human colon organoid and demonstrated its flexibility. This FEM model provides a basis for relating cell shape, tissue deformation, and strain at the cellular level due to imposed stresses. In conclusion, we demonstrated that the combination of vertex and FEM approaches allows for better modeling of the alteration of organoid morphology over time and better assessment of the mechanical cues involved in establishing the architecture of the human colon epithelium.

The external environment is a regulator of cell activity. Its stiffness and microstructure can either facilitate or prevent 3D cell migration in both physiology and disease. 3D cell migration results from force feedbacks between the cell and the extracellular matrix (ECM). Adhesions regulate these force feedbacks by working as molecular clutches that dynamically bind and unbind the ECM. Because of the interdependency between ECM properties, adhesion dynamics, and cell contractility, how exactly 3D cell migration occurs in different environments is not fully understood. In order to elucidate the effect of ECM on 3D cell migration through force-sensitive molecular clutches, we developed a computational model based on a lattice point approach. Results from the model show that increases in ECM pore size reduce cell migration speed. In contrast, matrix porosity increases it, given a sufficient number of ligands for cell adhesions and limited crowding of the matrix from cell replication. Importantly, these effects are maintained across a range of ECM stiffnesses, demonstrating that mechanical factors are not responsible for how matrix microstructure regulates cell motility.

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.

BackgroundAortic microstructure-function relationships and the pathophysiology of how medial degeneration leads to aortic dissection remain poorly defined. We aimed to determine how degeneration of individual components of the extracellular matrix (ECM), namely elastin, collagen, and proteoglycans, influence biomechanical properties of aortic tissue through an improved, disease-motivated enzymatic digestion framework. MethodsPorcine aortic tissue was sectioned into 200 {micro}m thick samples in the media, and progressively digested with elastase or collagenase for selective degradation of these ECM components. Full thickness human aortic tissues were treated with chondroitinase, hyaluronidase, and heparinase to completely remove proteoglycans. Biomechanical characterization was performed using planar biaxial tensile testing, from which low- and high-strain modulus, transition-zone behaviour, strain-energy density, and energy loss were derived. Degree of elastin fiber degradation was analyzed using two photon excitation fluorescence imaging. Analysis of collagen degradation was performed using picrosirius red staining under brightfield and polarized light. Alcian blue staining was used to evaluate proteoglycan content. ResultsInduced fragmentation and disorganization of elastin fibers reduced low-strain load bearing capacity, evidenced by reduced low-strain modulus, strain-energy density, and transition zone stress, along with reduced energy loss. Targeted collagen disorganization similarly reduced strain-energy density and decreased strain at the onset of transition, consistent with premature collagen recruitment, and was accompanied by reductions in high strain modulus and energy loss with increasing collagen degradation. Proteoglycan removal decreased energy loss and was found to modulate low- and high-strain behaviour, including reduced strain-energy density and strain at onset of transition, and increased high strain modulus. ConclusionsThrough targeted modelling of ECM degenerative features on aortic tissue mechanics, we have identified distinct disease-associated biomechanical roles for major matrix constituents, with overlapping effects. These findings delineate mechanical consequences of component-specific matrix degeneration while underscoring the complex, multifactorial nature of structure-function relationships in aortic disease.

Both classical mechanics and quantum mechanics explain the Brownian motion. However, it is unclear whether they are compatible with each other as the physical and mathematical identity of the wavefunction in quantum mechanics has been elusive. Here, a continuum theory using grammars in classical mechanics modeling but compatible with the quantum wavefunction is introduced. The theory explains the confined Brownian motion of cell membrane inclusions interacting with extracellular matrices or cytoskeletons via elastic molecular crosslinkers. This crosslinker theory is combined into the Canham-Helfrich-Evans model for fluid membranes. Calculations through the provision of a finite element method for the combined theory reproduced measured data from adhesion molecular machineries and cell membranes. Overall, by providing physical and mathematical interpretations of the quantum wavefunction, the presented theoretical model provides improved capabilities for the realistic simulation of classical and quantum biomechanical aspects of cell membranes and membrane linker proteins.

Aortic aneurysm rupture is a sudden local event with high mortality. It is generally accepted that the adventitia acts as the final barrier protecting the aorta from over-expansion. Currently, the knowledge of microscopic structural determinants of the tissues mechanical response and failure is very limited. The purpose of this study is to provide data on the directional failure properties of the adventitia, combined with micro-structural imaging and structure based constitutive modeling to quantify fiber-scale rupture criteria. Eleven healthy porcine aortas were used in this study. Cylindrical portions of the abdominal section were excised, cut-open longitudinally, the medial and adventitial layers separated methodically. Picrosirius red staining was used to image the collagen fiber morphology via an optical microscope. Subsequently, dog-bone shaped specimens were subjected to uniaxial testing until failure while being recorded by a Nikon digital camera. A fiber-scale damage model was utilized to explain the tissue-scale failure. The ultimate tensile stress in the circumferential and longitudinal directions were recorded to be 0.96 {+/-} 0.29 MPa and 0.85 {+/-} 0.36 MPa respectively. Meanwhile, the ultimate stretch to failure in the circumferential and longitudinal directions were recorded to be 1.72 {+/-} 0.16 and 1.88 {+/-} 0.13 respectively. Further, correlation between the failure properties of the tissue and mean fiber orientation have been reported. Finally, the critical fiber stretch for damage initiation and eventual tissue failure were identified to be 1.19 {+/-} 0.07 and 1.24 {+/-} 0.05 for circumferential and longitudinal specimens respectively. Our approach provides valuable insight into the (patho)physiological mechanical role of collagen fibers at different loading states. This study is useful in enhancing the utilization of structurally motivated material models for predicting arterial tissue failure.

Rolling of a cell under a hydrodynamic flow like the blood flow and the mechanism for the adhesion of a cell to the blood vessel is one of the fundamental process in many pathological and biological processes. An important example of these processes is inflammatory response and moving of the leukocytes to the sites of inflammation. While the blood-borne cells travel with the blood flow, they can interact with the inner endotheliums wall, which is composed of a soft layer of endothelial cells. Not until recently, the effect of endothelial stiffness was poorly understood. Recent in-vitro and computational models, like modified Adhesive Dynamics, have shown that the elasticity of the underlying substrate can alter the rolling and adhesion of a cell. In this study, we investigate the effects of the substrate stiffness on the rolling and adhesion of a cell with neutrophil ligands by using the Adhesive Dynamic simulation. The vessel is modeled as an elastic surface coated with L-selectin molecules, which can form bonds with the ligands. In our simulation, the Young modulus of the surface ranges between 5 to 80 kPa. The results show that the softer substrate helps to capture the cell with neutrophil ligands. These results help us to understand how the state of adhesion changes for the neutrophil adhesion over L-selectin.

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.

Current clinical risk stratification for thoracic aortic aneurysms (TAA) relies primarily on maximum diameter, which is a poor predictor of rupture. Recent fluid-structure interaction studies have identified a dimensionless "flutter instability parameter" (N{omega} ) that accurately classifies abnormal aortic growth. However, this parameter currently serves as a static diagnostic snapshot. In this work, we propose a proof-of-concept computational framework that links flutter instability to microstructural tissue damage via a coupled system of ordinary differential equations (ODEs). We model a feedback loop where flutter-induced energy dissipation drives elastin degradation and collagen remodeling, which in turn reduces wall stiffness and amplifies the instability. To address the challenge of unobservable tissue properties, we implement a Bayesian inference engine to infer model parameters. We demonstrate feasibility on a synthetic patient cohort calibrated to published clinical growth rates and diameters. Our results show that this approach can infer hidden damage parameters and capture the qualitative bifurcation between stabilizing remodeling and runaway aneurysm expansion. While validation on real patient data remains essential, this work establishes the mathematical foundation for transforming a static physiomarker into a personalized prognostic trajectory.

Skeletal muscle displays remarkable plasticity, adapting its size and strength in response to mechanical loading, particularly, from exercise. This process, known as hypertrophy, is fundamental to athletic training and rehabilitation, but is challenging to quantitatively predict due to its multifactorial, multiscale nature. Specifically, skeletal muscle hypertrophy results from an integration of macroscopic mechanical stimuli with the intracellular signaling pathways that govern muscle growth. In this work, we present a multiscale computational model that mechanistically integrates these mechanical and biochemical stimuli and offers a framework for predicting the outcomes of different types of exercise on skeletal muscle growth. The framework couples a transversely isotropic hyperelastic model for tissue-level mechanics with a system of ordinary differential equations representing the IGF1-AKT-mTOR-FOXO signaling pathway, a key regulator of protein synthesis and degradation. We link these scales using a volumetric growth model, where the signaling dynamics inform a growth tensor that drives changes in muscle cross-sectional area. This approach enables the simulation of long-term muscle adaptation, providing a mechanistic tool to investigate how different exercise protocols lead to macroscopic hypertrophy. Simulations from our model capture the temporal dynamics of hypertrophy under varying load protocols and highlight how feedback between protein synthesis and muscle growth regulates the dose-response relationship to prevent unbounded growth. Using muscle geometries derived from the Visible Human dataset, we study how human variations in muscle geometry affect hypertrophy. Finally, we demonstrate that the mechanochemical coupling between muscle geometry and signaling not only predicts macroscopic shape changes but also provides buffering from local signaling heterogeneity. Ultimately, this framework offers a predictive computational tool for optimizing training regimens and understanding the multiscale determinants of muscle adaptations.

Myofibril arrangement is critical to cardiac muscle function in health and disease. Historically, analysis of the impact of myofibril organisation on force and cell contraction has relied on the assumption of uniaxial arrays. However, improvements in imaging indicate that myofibrils form complex networks, though how these networks modulate force has yet to be explored. Here, morphological analysis of sheep left-ventricular cardiomyocytes is utilised to inform a non-linear finite element model of cell contraction. Analysis of deep learning segmentations of Z-Discs demonstrate that myofibrils are oriented about the major axis (mean = 0.03{degrees}) but deviate locally by up to 30{degrees} (standard deviation = 6.56{degrees}). Simulations produce unique deformations for geometries informed by myofibril orientations, displaying internal rotation and off-axis deformations. Moreover, anisotropy generates shear stresses distinct from the uniaxial case, demonstrating spatial relationships that balance shear across the cell. These findings highlight the impact of myofibril networks on forces during cell contraction.

A popular way to model contact interactions in musculoskeletal simulations uses Hertz theory applied to contact spheres, with Hunt Crossley based dissipation. Suitable contact parameters for dynamic simulations will be highly dependent on the morphology, scale, materials, and movement in question. Inappropriate parameter choices can manifest in unpredictable ways during simulations, potentially resulting in misinterpretations or failed simulations. Here, I demonstrate that both the plane strain modulus and the dissipation parameters are not scale invariant. I derive equations to scale the contact parameters in dimensionless form, which allows accounting for differences in body size, number of legs, contact sphere radius, and number of spheres per foot. As a demonstration of this scaling approach, I scale the contact parameters of a 62 kg human to a 500 kg human, a mouse (0.02 kg), an emu (37.8 kg), a horse (545 kg), and a giraffe (1190 kg), and demonstrate that geometrically and dynamically similar contact behaviour is achieved in all cases. The scaling approach presented here can be used to scale parameters known to work for one model to a completely different model, which is particularly useful in studies that simulate the effects of allometric scaling. I also provide equations to estimate suitable contact parameters for a model directly, without using a different model as a starting point. The limitations of Hertz Hunt Crossley contact models in biomechanical simulations are discussed. Lastly, I derive dimensionless expressions and scaling guidelines for the smoothed contact force implementation "SmoothSphereHalfSpaceForce" in the popular biomechanical simulator OpenSim.

Adhering cells exert traction forces on the underlying substrate. We numerically investigate the intimate relation between traction forces, the structure of the actin cytoskeleton, and the shape of cells adhering to adhesive micropatterned substrates. By combining the Cellular Potts Model with a model of cytoskeletal contractility, we reproduce prominent anisotropic features in previously published experimental data on fibroblasts, endothelial cells, and epithelial cells on adhesive micropatterned substrates. Our work highlights the role of cytoskeletal anisotropy in the generation of cellular traction forces, and provides a computational strategy for investigating stress fiber anisotropy in dynamical and multicellular settings. Author summaryCells that make up multicellular life perform a variety of mechanical tasks such as pulling on surrounding tissue to close a wound. The mechanisms by which cells perform these tasks are, however, incompletely understood. In order to better understand how they generate forces on their environment, cells are often studied in vitro on compliant substrates, which deform under the so called "traction forces" exerted by the cells. Mathematical models complement these experimental approaches because they help to interpret the experimental data, but most models for traction forces on adhesive substrates assume that cells contract isotropically, i.e., they do not contract in a specific direction. However, many cell types contain organized structures of stress fibers - strong contracting cables inside the cell - that enable cells to exert forces on their environment in specific directions only. Here we present a computational model that predicts both the orientations of these stress fibers as well as the forces that cells exert on the substrates. Our model reproduces both the orientations and magnitudes of previously reported experimental traction forces, and could serve as a starting point for exploring mechanical interactions in multicellular settings.

Traumatic brain injuries are characterized by damage to axonal cytoskeletal proteins. Here, we present a mathematical model predicting the chemo-mechanical disruption of intra-axonal micro-tubule assembly in terms of hyperphosphorylation-led dysfunction of tubulin-binding tau proteins. Intracellular calcium accumulation following a trauma leads to calpain activation, disturbing the downstream kinase-phosphatase activity balance which causes tau hyperphosphorylation. We develop a computational framework, using finite element methods, predicting the spatiotemporal evolution of mechanical stress and ensuing tau hyperphosphorylation in the human brain after traumatic brain injury-inducing loads. We compare our predictions with previously reported experimental and clinical observations to validate the model. Our model provides important insights into the secondary effects of traumatic brain injuries and can be essential in their clinical management.

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.