One goal of Scientific Machine Learning (SciML) is to advance traditional scientific computing frameworks with modern machine learning tools. This includes extending established approaches, such as the finite element method, to simulate cardiac function due to its complexity and need for very rapid execution times for real time clinical use. In this work, we present an advanced form of the Neural Network Finite Element (NNFE) method specialized for cardiac simulations, termed CARDIAX-NNFE. The NNFE method learns the parameter-to-displacement field map by training over the residual of the hyperelastic material PDE, using the domain represented by finite elements. The implementation is developed in Python using JAX to leverage its automatic differentiation, highly parallel GPU, and JIT-compilation capabilities. To demonstrate CARDIAX-NNFE effectiveness, we trained full cardiac pressure-volume responses using a simplified heart model, spanning the entire cardiac physiological functional range. Results indicated the ability to simulate a family of pressure-volume solutions with average nodal positional error of 0.023 mm and maximal error of 0.054 mm, with a single complete PV loop evaluated in 0.002 seconds. The CARDIAX-NNFE software platform thus provides for a robust platform for cardiac functional simulations. Moreover, it provides the structure for residual-based SciML methods, which can apply to a variety of physics-based biomedical problems that require high execution speed for clinical applications.

We have recently documented significant compressible behaviors in hydrogels implemented in 3D traction force microscopy (TFM). Therefore, here we have developed a new computational pipeline that accounts for this observation. Additionally, the new method accurately recovers large ranges and spatial heterogeneity of hydrogel moduli induced by cellular remodeling associated with enhanced extracellular matrix secretion and MMP-degradation. The algorithm sought best fit of the 3D displacement field with a multi-stage approach, wherein the Tikhonov regularization parameter in L-BFGS was progressively lowered. Forward simulations were performed in FEniCS, with gradients computed with FEniCS-adjoint and MOOLA to weight degrees of freedom according to hydrogel volume affected. Once developed, we conducted a series of synthetic test cases applying actual cell geometries, experimentally matched compressibility, and realistic displacements with experimental noise levels. Employing an incompressible material model resulted in predicted moduli with over 415% mean relative error and predicted strain energies 5-fold greater than the prescribed values. Moreover, errors in predicted traction forces were amplified by a factor of 10. Thus, accounting for hydrogel compressibility was critical for accurate hydrogel moduli and strain energy recovery. To demonstrate the utility of our approach, we applied it to TFM data of human mitral valve interstitial cells embedded in PEG hydrogels with pre-altered moduli of 54 Pa. We determined that J∈[0.45,1.66] and local hydrogel moduli exhibited large variations, 3.6 Pa to 2.4 MPa. This study underscores the need for correct handling of hydrogel compressibility for accurate estimation of local hydrogel moduli and traction forces.

The mechanical properties of the extracellular matrix (ECM), particularly stiffness, regulate endothelial progenitor responses during vascular development, yet their behavior in physiologically compliant matrices (<1 kPa) remains underexplored. Using norbornene-modified hyaluronic acid (NorHA) hydrogels with tunable stiffness (190-884 Pa), we investigated how hydrogel stiffness influences cell morphology, endothelial maturation, mechanotransduction, and microvascular network formation in human induced pluripotent stem cell-derived endothelial progenitors (hiPSC-EPs). Our findings reveal a stiffness-dependent tradeoff between mechanotransduction and vascular network formation. At intermediate stiffness (551 Pa), cells exhibited the greatest increase in endothelial marker CD31 expression and Yes-associated protein (YAP)/ transcriptional coactivator with PDZ-binding motif (TAZ) nuclear translocation, indicating enhanced mechanotransduction and endothelial maturation. However, this did not translate to superior plexus formation. Instead, the most compliant matrix (190 Pa) supported greater vascular connectivity, characterized by longer branches (∼0.03/volume vs. 0.015 at 551 Pa) and enhanced actin remodeling. 3D cell contraction measurements revealed a 15.6-fold higher basal displacement in compliant hydrogels, suggesting that cell-generated forces and matrix deformability collectively drive vascular morphogenesis. Unlike prior studies focusing on pathological stiffness ranges (>10 kPa), our results emphasize that vascularization is not solely driven by the most mechanotransductive environment but rather by a balance of compliance, contractility, and cell-induced remodeling. These findings underscore the need to design hydrogels that provide sufficient mechanotransduction for endothelial maturation while maintaining compliance to support dynamic vascular morphogenesis. This work provides a mechanically tuned framework for optimizing microenvironments to balance endothelial differentiation and vascular network formation in tissue engineering and regenerative medicine.

Cardiac fibrosis results from persistent cardiac fibroblast activation and is heavily dependent on the interplay of extracellular matrix mechanics and proinflammatory cytokines. Studying this interplay using in vitro disease models is of interest for developing strategies to treat cardiac fibrosis. However, current metrics for quantifying myofibroblast activation rely heavily on the presence of α-SMA stress fibers, which works well for two-dimensional (2D) culture systems but not 3D cell scaffolds. Here, we investigate how contractility and extracellular matrix secretions, which are two phenotypic markers of cardiac myofibroblasts, correlate with 3D matrix stiffness and TGF-β concentration (a proinflammatory cytokine). Cardiac fibroblasts encapsulated in soft, degradable hydrogels were larger and more contractile and secreted more extracellular matrix than cells encapsulated in stiff, degradable hydrogels or nondegradable hydrogels. The addition of TGF-β to the soft, degradable hydrogels increased the volume, contractility, and extracellular matrix secretions, indicating myofibroblast activation. In addition, the presence of α-SMA increased, but α-SMA stress fibers were not detected. These results highlight the importance of local degradability in 3D hydrogels for cellular contractility and remodeling of the extracellular matrix. They also suggest the use of additional phenotypic markers to probe myofibroblast activation in 3D cellular scaffolds.

Transcatheter edge-to-edge repair (TEER) is a promising minimally invasive approach for the treatment of mitral valve (MV) regurgitation (MR). However, long-term outcomes have been suboptimal in several recent clinical trials, likely complicated by the substantial heterogeneity of MR presentations and the combinatorial nature of the procedure itself. Moreover, the long-term consequences of MV TEER on the leaflet tissue itself have never been studied, and represent a key target for procedural optimization. As a first step in addressing these deficiencies, we conducted a novel study to investigate the effects of TEER-induced MV shape and strain at several months after implantation. We first acquired and analyzed longitudinal echocardiographic imaging data from five patients pre, post, and at 3-month follow-up post-TEER. We then quantified TEER-induced time-evolving changes in MV diastolic shape and systolic leaflet strains using a computational in vivo geometry recovery method. In the ED state we found evidence of substantial MV leaflet \textit{plasticity} (defined here as permanent changes MV leaflet geometry) at the 3 month time point. Though the patterns of plasticity were generally heterogeneous, the highest regions of plasticity consistently corresponded to the position of the TEER device. Moreover, the first principal direction field consistently converged onto the position(s) of the clip(s) in all cases, further suggesting that the clip is the primary driver of the observed plasticity. This result emphasized the fact that the MV leaflets are not dimensionally stable post-TEER, and instead continue to remodel. To evaluate the potential confounding effects of concomitant changes in ventricular shape, we also quantified changes in annular and left ventricular dimensions and found no significant changes over time. These observations support our hypothesis that the focal stress concentrations induced by the clip drive MV leaflet tissue remodeling at 3 months. In summary these novel results confirm that TEER has effects beyond those observed immediately post-operation. These unique findings underscore a mechanism for long-term repair failure and constitute a potential key target for patient-specific procedural optimization.

Human induced pluripotent stem cells (hiPSCs) offer patient-specific and immune-evasive sources for generating diverse cell types; yet lack of vascularization in hiPSC-derived tissues remains a major limitation for both therapeutic applications and disease modeling. Elucidating the mechanisms underlying vascular network formation in hiPSC-derived cells is therefore imperative. We and others have previously demonstrated that hiPSC-derived endothelial progenitor cells (hiPSC-EPs) self-assemble into lumenized microvascular networks when cultured in 3D norbornene-functionalized hyaluronic acid-based hydrogels. Herein we investigated the early period of culturing to characterize contractility of hiPSC-EPs. We hypothesized that multi-cell cooperativity would increase over time and would be dependent on the original hydrogel storage modulus. To quantify cellular contractility either 4 or 7 days after encapsulation, 3D kinematic analysis was performed on single and small multi-cell clusters of hiPSC-EPs embedded in NorHA-based hydrogels. Contractile responses were significantly and non-linearly influenced by multicellularity, culture duration, and hydrogel stiffness. Novel to this study was the observation that NorHA hydrogels exhibited compressible behaviors, with greater compressibility occurring in NorHA hydrogels with lower stiffness. Hence, the kinematic analysis was modified to incorporate separate deviatoric and volumetric strain indices. At day 7, multicellularity synergistically increased both strain components. These findings indicated that hiPSC-EP contractility and mechanical interactions with the hydrogel are governed by culture duration, multicellularity, and hydrogel stiffness; providing mechanical insight on hiPSC-EP self-assembly into microvasculature networks, a critical step towards development of functional vascular tissues for regenerative medicine and disease models.

The use of patient-specific computational modeling of cardiovascular diseases has become increasingly popular to improve patient standard of care. Most simulation approaches currently utilize the finite element method (FEM), which is very well established and succeeds in producing high-fidelity results. However, it remains too slow for use in clinical settings, especially when many-query solutions are required to determine optimal therapeutic approaches. As a step toward addressing these demands, we have developed a Neural Network Finite Element (NNFE) approach that greatly accelerates simulations of soft tissue organ function. While the NNFE method utilizes conventional FEM meshes to define the problem geometry, it leverages advancements in neural network architecture design in new GPU-based software tools to solve the governing hyperelastic material PDEs. The NNFE method has recently captured physical contact between a deformable body and a frictionless symmetry plane. In the present work, we extended the NNFE approach to simulate trileaflet heart valve closure as a critical step in moving toward patient-specific applications. Our approach addressed two critical aspects of heart valve simulations: the use of 3D solid leaflet models as opposed to shell-based leaflet models and multi-body contact between the leaflets. We verified the approach by comparing displacements of NNFE simulated closure of a single heart valve leaflet against a frictionless symmetry plane with an identical simulation in tIGAr, the open-source isogeometric analysis extension of FEniCS. The average nodal displacement error was 0.020 mm (0.47% of the maximum displacement). We further evaluated our implementation by varying leaflet collagen fiber directions to mimic physiologically accurate deformation modes. Results of the approach indicated that the observed leaflet deformation patterns agreed well with previous trileaflet simulations. Significant variations in stress were observed transmurally, underscoring the need for solid elements to model leaflet geometry. Computational speed improvements produced an approximately 100-fold speedup, with the NNFE simulations of single leaflet closure taking 0.28 s while its FE counterpart took 61 s. Full trileaflet valve models with multi-body contact simulations took approximately 5 s, whereas equivalent FEM simulations take several hours. Training the full trileaflet model took approximately 16 h and was trained over the full functional range of pressure, so that training was only required once for all subsequent simulations. We conclude that the NNFE method can be successfully used to perform rapid simulations of complex 3D soft organ systems, such as the trileaflet heart valve, that involve large deformations, 3D geometries, and multi-body contact. Moreover, the ability to perform post-trained simulations in dramatically shorter time periods underscores the promise of machine learning-based computational mechanics approaches in patient-specific predictive computational models.

The anisotropic mechanical properties of fiber-embedded biological tissues are essential for understanding their development, aging, disease progression, and response to therapy. However, accurate and fast assessment of mechanical anisotropy in vivo using elastography remains challenging. To address the dilemma of achieving both accuracy and efficiency in this inverse problem involving complex wave equations, we propose a computational framework that utilizes the traveling wave expansion model. This framework leverages the unique wave characteristics of transversely isotropic material and physically meaningful operator combinations. The analytical solutions for inversion are derived and engineering optimization is made to adapt to actual scenarios. Measurement results using simulations, ex vivo muscle tissue, and in vivo human white matter validate the framework in determining in vivo anisotropic biomechanical properties, highlighting its potential for measurement of a variety of fiber-embedded biological tissues.

Mitral valve (MV) regurgitation (MR) is a condition in which the valve between the heart's left atrium and left ventricle fails to close properly, causing backflow of blood into the atrium. MV transcatheter edge-to-edge repair (TEER) is a minimally invasive procedure for treating MR. During the TEER procedure, the MV leaflet flaps are clipped together to improve closure and reduce leakage without the need for open heart surgery. However, the effects of TEER configuration on MV function, hemodynamics, and long-term outcomes remain largely uncharacterized. To better understand post-TEER hemodynamic responses, we develop a computational fluid-structure interaction model that demonstrates the interaction between blood flow behavior and a patient-specific MV in the left heart. The developed model evaluates post-operative hemodynamics, such as a significant reduction in the effective orifice area and an increase in transvalvular gradients. This study provides a better understanding of how pre- and post-operative MV conditions, geometry, mechanical behaviors, and flow influence the post-TEER functional state.

In this review, we explore the development of the structural constitutive models for soft biological tissues and biomaterials, focusing on the role of mechanical interactions. Soft tissues, such as myocardium, heart valves, skin, and other fibrous biological structures, display complex, non-linear mechanical behaviors driven by their fibrous composition. While traditional constitutive models have been widely adopted to describe the anisotropic and hyperelastic nature of these tissues, they often fall short in capturing the detailed mechanical interactions between fibers, the ground matrix, and other tissue components under diverse loading conditions. To address these gaps, we focus on the development of meso-structural models that account for mechanism-driven fiber-matrix and fiber-fiber interactions. We delve into the kinematics of these interactions, highlighting the roles of fiber rotation, extension, and shear, and how these mechanisms differ across various tissues and biomaterials. Our review draws on experimental evidence from studies on exogenously crosslinked tissues, myocardium and electrospun polycarbonate urethane biomaterials, which reveal the presence of significant mechanical interactions that influence the overall stress response. In addition, we also discuss recent advancements in computational methods for implementing these models, particularly neural network-based approaches that mitigate the computational challenges typically associated with structural models while maintaining high predictive accuracy. Finally, we propose future directions for multiscale modeling to enhance our understanding of fiber network topologies and their mechanical roles in both native tissues and biomaterials.

Mitral valve (MV) regurgitation is a highly prevalent and deadly cardiac disease affecting over 2

Myocardial infarction (MI) remains a leading cause of death, effecting nearly 1 million people annually. Cardiac simulations of MI in a clinical setting can aid in our understanding of the disease and guide optimal therapies. While the accuracy of current cardiac computational models of MI have improved significantly, execution times remain prohibitively slow for clinical use. In recent years computational technology has made major advances, in large part due to the rise of machine learning (ML). Using JAX, an open source ML-library, we developed a high speed cardiac simulation platform. This platform included an inverse modeling framework for estimation of active myofiber stress. The framework was developed using experimental and modeling data from a comprehensive dataset from a single ovine heart, including pressure volume loops and diffusion tensor magnetic resonance imaging data. We also conducted simulations to incorporate heart wall compressibility based on the levels of active contraction. When using a comparable mesh to our previous ABAQUS based approach, we showed a 100x speed up in simulation time. The substantial speed improvement also facilitates more sophisticated multiphysics cardiac simulations for both research and clinical settings. The simulation time was also compared to the FEniCS opensource code and showed considerable gains as well. Ongoing work includes enabling intrinsic electrophysiology capability to enable true electromechanical based simulations.

There is a lack of understanding how human breast tissue internal structure connects to its bulk level 3D mechanical behaviors. An attractive method to quantify tissue structure is diffusion tensor imaging (DTMRI), which produces compact, local, quantitative information in the form of a second rank symmetric tensor D. As D contains rich information about local 3D tissue structure, we developed a novel constitutive model form for human fibroglandular (FG) and adipose (AD) breast tissues that directly utilized the complete D. Our modeling approach included separate extensional/compression and shear-like interactions terms. To develop the necessary mathematical forms we utilized a neural network modeling approach trained using pure-shear loading paths from the extant triaxial data for the AD and FG groups. A final model form was formulated and model parameters determined using the same data set. The resultant constitutive model was able to simulate the unique anisotropic tension/compression behaviors, including directionally dependent non-linearities for the FG tissue group. The constitutive model was validated in two steps. First, we used the model to predict D and compared it to D as measured directly by DTMRI on excised breast tissue, which compared very well. Secondly, validation of the predictive capabilities of the model were demonstrated by accurate predictions of breast tissue in simple compression for both AD and FG tissue groups. The present modeling approach was able to predict human breast tissue 3D mechanical behavior accurately, as well as shed insight into connections to the underlying tissue structure via the use of D.

We have recently documented significant compressible behaviors in hydrogels implemented in 3D traction force microscopy (TFM). Therefore, here we have developed a new computational pipeline that accounts for this observation. Additionally, the new method accurately recovers large ranges and spatial heterogeneity of hydrogel moduli induced by cellular remodeling associated with enhanced extracellular matrix secretion and MMP-degradation. The algorithm sought best fit of the 3D displacement field with a multi-stage approach, wherein the Tikhonov regularization parameter in L-BFGS was progressively lowered. Forward simulations were performed in FEniCS, with gradients computed with FEniCS-adjoint and MOOLA to weight degrees of freedom according to hydrogel volume affected. Once developed, we conducted a series of synthetic test cases applying actual cell geometries, experimentally-matched compressibility, and realistic displacements with experimental noise levels. Employing an incompressible material model resulted in predicted moduli with over 415% mean relative error and predicted strain energies 5-fold greater than the prescribed values. Moreover, errors in predicted traction forces were amplified by a factor of 10. Thus, accounting for hydrogel compressibility was critical for accurate hydrogel moduli and strain energy recovery. To demonstrate the utility of our approach, we applied it to TFM data of human mitral valve interstitial cells embedded in PEG hydrogels with pre-altered moduli of 54 Pa. We determined that J ∈ [ 0.45 , 1.66 ] and local hydrogel moduli exhibited large variations, 3.6 Pa to 2.4 MPa. This study underscores the need for correct handling of hydrogel compressibility for accurate estimation of local hydrogel moduli and traction forces.

Computational biomechanical models of organ systems utilizing the finite element (FE) method have been extensively applied to the study of normal and pathophysiological function. While a robust method and providing for many unique pathophysiological insights, traditional FE methods remain prohibitively slow for real-time many-query clinical applications. To meet these demanding computational requirements, we have developed a general neural network finite element (NNFE) approach for hyperelastic soft tissue organ simulations that can produce equivalent accuracy within clinically relevant time frames. However, many organ systems involve contact between structural elements (e.g. heart valve leaflets), which has not been previously addressed in the NNFE and related approaches. In the present work, we developed a NNFE-based method for contact between elastic deformable bodies. We exploited the fact that stable equilibrium solutions in nonlinear hyperelasticity minimize the potential energy to train the neural network, with contact incorporated through an potential energy penalty. This penalty was discretized and computationally implemented in JAX (Google, Inc.) in a way that could be statically allocated and jit compiled for rapid computation on modern GPU hardware. Following our previous NNFE formulation, we represented the problem domain geometry and enforced the necessary boundary conditions using a Non-Uniform Rational B-Splines (NURBS) based geometry scheme. NURBS were chosen for their ability to smoothly represent body and surface geometries. To demonstrate the method, we developed and verified two basic contact problems using a hyperelastic material model: indentation and flap. Both problems were simulated with a fully connected neural network with a parallel linear layer. Training continued until the gradient of the energy with respect to network parameters dropped below 10−2, taking less than six hours in each case studied. Once trained, the NNFE approach was capable of accurately predicting the equilibrium configurations of representative contact problems. Moreover, the NNFE contact approach required nearly six order of magnitude less time to evaluate than an equivalent FEniCSx implementation that utilized a similar mesh of cubic order Lagrange elements (0.01 s compared to 1715 seconds, respectively). Moreover, the solutions were trained over full ranges of loading and were thus able to faithfully represent families of solutions without requiring retraining. While addressing basic problems in contact, this study serves as crucial stepping stone to generate NNFE organ simulations for rapid prediction of patient specific solutions that involve contact.

Aortic valve (AV) disease is a common valvular lesion in the United States, present in about 5

This study was undertaken to develop a mathematical model of the long-term in vivo remodeling processes in postimplanted pulmonary artery (PA) conduits. Experimental results from two extant ovine in vivo studies, wherein polyglycolic-acid (PGA)/poly-L-lactic acid tubular conduits were constructed, cell seeded, incubated for 4 weeks, and then implanted in mature sheep to obtain the remodeling data for up to two years. Explanted conduit analysis included detailed novel structural and mechanical studies. Results in both studies indicated that the in vivo conduits remained dimensionally stable up to 80 weeks, so that the conduits maintained a constant in vivo stress and deformation state. In contrast, continued remodeling of the constituent collagen fiber network as evidenced by an increase in effective tissue uniaxial tangent modulus, which then stabilized by one year postimplant. A mesostructural constitute model was then applied to extant planar biaxial mechanical data and revealed several interesting features, including an initial pronounced increase in effective collagen fiber modulus, paralleled by a simultaneous shift toward longer, more uniformly length-distributed collagen fibers. Thus, while the conduit remained dimensionally stable, its internal collagen fibrous structure and resultant mechanical behaviors underwent continued remodeling that stabilized by one year. A time-evolving structural mixture-based mathematical model specialized for this unique form of tissue remodeling was developed, with a focus on time-evolving collagen fiber stiffness as the driver for tissue-level remodeling. The remodeling model was able to fully reproduce (1) the observed tissue-level increases in stiffness by time-evolving simultaneous increases in collagen fiber modulus and lengths, (2) maintenance of the constant collagen fiber angular dispersion, and (3) stabilization of the remodeling processes at one year. Collagen fiber remodeling geometry was directly verified experimentally by histological analysis of the time-evolving collagen fiber crimp, which matches model predictions very closely. Interestingly, the remodeling model indicated that the basis for tissue homeostasis was maintenance of the collagen fiber ensemble stress for all orientations, and not individual collagen fiber stresses. Unlike other growth and remodeling models that traditionally treat changes in the external boundary conditions (e.g., changes in blood pressure) as the primary input stimuli, the driver herein is changes to the internal constituent collagen fiber themselves due to cellular mediated cross-linking.

An estimated 6.8 million people in the United States have an unruptured intracranial aneurysms, with approximately 30,000 people suffering from intracranial aneurysms rupture each year. Despite the development of population-based scores to evaluate the risk of rupture, retrospective analyses have suggested the limited usage of these scores in guiding clinical decision-making. With recent advancements in imaging technologies, artery wall motion has emerged as a promising biomarker for the general study of neurovascular mechanics and in assessing the risk of intracranial aneurysms. However, measuring arterial wall deformations in vivo itself poses several challenges, including how to image local wall motion and deriving the anisotropic wall strains over the cardiac cycle. To overcome these difficulties, we first developed a novel in vivo MRI-based imaging method to acquire cardiac gated images of the human basilar artery (BA) over the cardiac cycle. Next, complete BA endoluminal surfaces from each frame were segmented, producing high-resolution point clouds of the endoluminal surfaces. From these point clouds we developed a novel B-spline-based surface representation, then exploited the local support nature of B-splines to determine the local endoluminal surface strains. Results indicated distinct regional and temporal variations in BA wall deformation, highlighting the heterogeneous nature BA function. These included large circumferential strains (up to ∼ 20 % ), and small longitudinal strains, which were often contractile and out of phase with the circumferential strains patterns. Of particular interest was the temporal phase lag in the maximum circumferential perimeter length, which indicated that the BA deforms asynchronously over the cardiac cycle. In summary, the proposed method enabled local deformation analysis, allowing for the successful reproduction of local features of the BA, such as regional principal stretches, areal changes, and pulsatile motion. Integrating the proposed method into existing population-based scores has the potential to improve our understanding of mechanical properties of human BA and enhance clinical decision-making.

Ischemic mitral regurgitation (IMR) occurs from incomplete coaptation of the mitral valve (MV) after myocardial infarction (MI), typically worsened by continued remodeling of the left ventricular (LV). The importance of LV remodeling is clear as IMR is induced by the post-MI dual mechanisms of mitral annular dilation and leaflet tethering from papillary muscle (PM) distension via the MV chordae tendineae (MVCT). However, the detailed etiology of IMR remains poorly understood, in large part due to the complex interactions of the MV and the post-MI LV remodeling processes. Given the patient-specific anatomical complexities of the IMR disease processes, simulation-based approaches represent an ideal approach to improve our understanding of this deadly disease. However, development of patient-specific models of left ventricle-mitral valve (LV-MV) interactions in IMR are complicated by the substantial variability and complexity of the MR etiology itself, making it difficult to extract underlying mechanisms from clinical data alone. To address these shortcomings, we developed a detailed ovine LV-MV finite element (FE) model based on extant comprehensive ovine experimental data. First, an extant ovine LV FE model (Sci. Rep. 2021 Jun 29;11(1):13466) was extended to incorporate the MV using a high fidelity ovine in vivo derived MV leaflet geometry. As it is not currently possible to image the MVCT in vivo, a functionally equivalent MVCT network was developed to create the final LV-MV model. Interestingly, in pilot studies, the MV leaflet strains did not agree well with known in vivo MV leaflet strain fields. We then incorporated previously reported MV leaflet prestrains (J. Biomech. Eng. 2023 Nov 1;145(11):111002) in the simulations. The resulting LV-MV model produced excellent agreement with the known in vivo ovine MV leaflet strains and deformed shapes in the normal state. We then simulated the effects of regional acute infarctions of varying sizes and anatomical locations by shutting down the local myocardial contractility. The remaining healthy (noninfarcted) myocardium mechanical behaviors were maintained, but allowed to adjust their active contractile patterns to maintain the prescribed pressure-volume loop behaviors in the acute post-MI state. For all cases studied, the LV-MV simulation demonstrated excellent agreement with known LV and MV in vivo strains and MV regurgitation orifice areas. Infarct location was shown to play a critical role in resultant MV leaflet strain fields. Specifically, extensional deformations of the posterior leaflets occurred in the posterobasal and laterobasal infarcts, while compressive deformations of the anterior leaflet were observed in the anterobasal infarct. Moreover, the simulated posterobasal infarct induced the largest MV regurgitation orifice area, consistent with experimental observations. The present study is the first detailed LV-MV simulation that reveals the important role of MV leaflet prestrain and functionally equivalent MVCT for accurate predictions of LV-MV interactions. Importantly, the current study further underscored simulation-based methods in understanding MV function as an integral part of the LV.