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Background and Objective: Data from electro-anatomical mapping (EAM) systems are playing an increasingly important role in computational modeling studies for the patient-specific calibration of digital twin models. However, data exported from commercial EAM systems are challenging to access and parse. Converting to data formats that are easily amenable to be viewed and analyzed with commonly used cardiac simulation software tools such as openCARP remains challenging. We therefore developed an open-source platform, pyCEPS, for parsing and converting clinical EAM data conveniently to standard formats widely adopted within the cardiac modeling community. Methods and Results: pyCEPS is an open-source Python-based platform providing the following functions: (i) access and interrogate the EAM data exported from clinical mapping systems; (ii) efficient browsing of EAM data to preview mapping procedures, electrograms (EGMs), and electro-cardiograms (ECGs); (iii) conversion to modeling formats according to the openCARP standard, to be amenable to analysis with standard tools and advanced workflows as used for in silico EAM data. Documentation and training material to facilitate access to this complementary research tool for new users is provided. We describe the technological underpinnings and demonstrate the capabilities of pyCEPS first, and showcase its use in an exemplary modeling application where we use clinical imaging data to build a patient-specific anatomical model. Conclusion: With pyCEPS we offer an open-source framework for accessing EAM data, and converting these to cardiac modeling standard formats. pyCEPS provides the core functionality needed to integrate EAM data in cardiac modeling research. We detail how pyCEPS could be integrated into model calibration workflows facilitating the calibration of a computational model based on EAM data.

The development of biophysical models for clinical applications is rapidly advancing in the research community, thanks to their predictive nature and their ability to assist the interpretation of clinical data. However, high-resolution and accurate multi-physics computational models are computationally expensive and their personalisation involves fine calibration of a large number of parameters, which may be space-dependent, challenging their clinical translation. In this work, we propose a new approach which relies on the combination of physics-informed neural networks (PINNs) with three-dimensional soft tissue nonlinear biomechanical models, capable of reconstructing displacement fields and estimating heterogeneous patient-specific biophysical properties. The proposed learning algorithm encodes information from a limited amount of displacement and, in some cases, strain data, that can be routinely acquired in the clinical setting, and combines it with the physics of the problem, represented by a mathematical model based on partial differential equations, to regularise the problem and improve its convergence properties. Several benchmarks are presented to show the accuracy and robustness of the proposed method and its great potential to enable the robust and effective identification of patient-specific, heterogeneous physical properties, s.a. tissue stiffness properties. In particular, we demonstrate the capability of the PINN to detect the presence, location and severity of scar tissue, which is beneficial to develop personalised simulation models for disease diagnosis, especially for cardiac applications., Comment: Accepted for publication in Computational Mechanics

The impact of increased stiffness and pulsatile load on the circulation and their influence on heart performance have been documented not only for cardiovascular events but also for ventricular dysfunctions. For this reason, computer models of cardiac electromechanics (EM) have to integrate effects of the circulatory system on heart function to be relevant for clinical applications. Currently it is not feasible to consider three-dimensional (3D) models of the entire circulation. Instead, simplified representations of the circulation are used, ensuring a satisfactory trade-off between accuracy and computational cost. In this work, we propose a novel and stable strategy to couple a 3D EM model of the heart to a one-dimensional (1D) model of blood flow in the arterial system. A personalised coupled 3D-1D model of LV and arterial system is built and used in a numerical benchmark to demonstrate robustness and accuracy of our scheme over a range of time steps. Validation of the coupled model is performed by investigating the coupled system's physiological response to variations in the arterial system affecting pulse wave propagation, comprising aortic stiffening, aortic stenosis or bifurcations causing wave reflections. Our results show that the coupled 3D-1D model is robust, stable and correctly replicates known physiology. In comparison with standard coupled 3D-0D models, additional computational costs are negligible, thus facilitating the use of our coupled 3D-1D model as a key methodology in studies where wave propagation effects are under investigation.

Fiber-reinforced soft biological tissues are typically modeled as hyperelastic, anisotropic, and nearly incompressible materials. To enforce incompressibility a multiplicative split of the deformation gradient into a volumetric and an isochoric part is a very common approach. However, due to the high stiffness of anisotropic materials in the preferred directions, the finite element analysis of such problems often suffers from severe locking effects and numerical instabilities. In this paper, we present novel methods to overcome locking phenomena for anisotropic materials using stabilized P1-P1 elements. We introduce different stabilization techniques and demonstrate the high robustness and computational efficiency of the chosen methods. In several benchmark problems we compare the approach to standard linear elements and show the accuracy and versatility of the methods to simulate anisotropic, nearly and fully incompressible materials. We are convinced that this numerical framework offers the possibility to accelerate accurate simulations of biological tissues, enabling patient-specfic parameterization studies, which require numerous forward simulations., Comment: This research has received funding from the European Union's Horizon 2020 research and innovation programme under the ERA-NET co-fund action No. 680969 (ERA-CVD SICVALVES) funded by the Austrian Science Fund (FWF), Grant I 4652-B

Image-based computational models of the heart represent a powerful tool to shed new light on the mechanisms underlying physiological and pathological conditions in cardiac function and to improve diagnosis and therapy planning. However, in order to enable the clinical translation of such models, it is crucial to develop personalized models that are able to reproduce the physiological reality of a given patient. There have been numerous contributions in experimental and computational biomechanics to characterize the passive behavior of the myocardium. However, most of these studies suffer from severe limitations and are not applicable to high-resolution geometries. In this work, we present a novel methodology to perform an automated identification of in vivo properties of passive cardiac biomechanics. The highly-efficient algorithm fits material parameters against the shape of a patient-specific approximation of the end-diastolic pressure-volume relation (EDPVR). Simultaneously, a stress-free reference configuration is generated, where a novel fail-safe feature to improve convergence and robustness is implemented. Only clinical image data or previously generated meshes at one time point during diastole and one measured data point of the EDPVR are required as an input. The proposed method can be straightforwardly coupled to existing finite element (FE) software packages and is applicable to different constitutive laws and FE formulations. Sensitivity analysis demonstrates that the algorithm is robust with respect to initial input parameters., Comment: This research has received funding from the European Union's Horizon 2020 research and innovation programme under the ERA-NET co-fund action No. 680969 (ERA-CVD SICVALVES) funded by the Austrian Science Fund (FWF), Grant I 4652-B

Computer models of cardiac electro-mechanics (EM) show promise as an effective means for quantitative analysis of clinical data and, potentially, for predicting therapeutic responses.realize such advanced applications methodological key challenges must be addressed. Enhanced computational efficiency and robustness is crucial to facilitate, within tractable time frames, model personalization, the simulation of prolonged observation periods under a broad range of conditions, and physiological completeness encompassing therapy-relevant mechanisms is needed to endow models with predictive capabilities beyond the mere replication of observations. Here, we introduce a universal feature-complete cardiac EM modeling framework that builds on a flexible method for coupling a 3D model of bi-ventricular EM to the physiologically comprehensive 0D CircAdapt model representing atrial mechanics and closed-loop circulation. A detailed mathematical description is given and efficiency, robustness, and accuracy of numerical scheme and solver implementation are evaluated. After parameterization and stabilization of the coupled 3D-0D model to a limit cycle under baseline conditions, the model's ability to replicate physiological behaviors is demonstrated, by simulating the transient response to alterations in loading conditions and contractility, as induced by experimental protocols used for assessing systolic and diastolic ventricular properties. Mechanistic completeness and computational efficiency of this novel model render advanced applications geared towards predicting acute outcomes of EM therapies feasible., Comment: This project has received funding from the EU's H2020 programme under the Marie Sklodowska-Curie Action InsiliCardio, GA No. 750835 and under the ERA-NET co-fund action No. 680969 (ERA-CVD SICVALVES) funded by the Austrian Science Fund (FWF), GA I 4652-B to CMA. The research was supported by the Grants F3210-N18 and I2760-B30 from the FWF and a BioTechMed Graz flagship award ILearnHeart to GP

Cardiac growth and remodeling (G&R) refers to structural changes in myocardial tissue in response to chronic alterations in loading conditions. One such condition is pressure overload where elevated wall stresses stimulate the growth in cardiomyocyte thickness, associated with a phenotype of concentric hypertrophy at the organ scale, and promote fibrosis. The initial hypertrophic response can be considered adaptive and beneficial by favoring myocyte survival, but over time if pressure overload conditions persist, maladaptive mechanisms favoring cell death and fibrosis start to dominate, ultimately mediating the transition towards an overt heart failure phenotype. The underlying mechanisms linking biological factors at the myocyte level to biomechanical factors at the systemic and organ level remain poorly understood. Computational models of G&R show high promise as a unique framework for providing a quantitative link between myocardial stresses and strains at the organ scale to biological regulatory processes at the cellular level which govern the hypertrophic response. However, microstructurally motivated, rigorously validated computational models of G&R are still in their infancy. This article provides an overview of the current state-of-the-art of computational models to study cardiac G&R. The microstructure and mechanosensing/mechanotransduction within cells of the myocardium is discussed and quantitative data from previous experimental and clinical studies is summarized. We conclude with a discussion of major challenges and possible directions of future research that can advance the current state of cardiac G&R computational modeling., Comment: The research was supported by the Grants F3210-N18 and I2760-B30 from the Austrian Science Fund (FWF), and a BioTechMed-Graz award (Grant No. Flagship Project: ILearnHeart) to GP. Additionally, this project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Action H2020-MSCA-IF-2016 InsiliCardio, GA No. 750835 to CMA

Cardiovascular diseases (CVDs) are the leading causes of mortality worldwide. The contraction and relaxation of left ventricle (LV) is the main driving force of blood circulation. Altered LV hemodynamics is believed to be associated with the initiation and progression of many CVDs. Thus, understanding and evaluating the flow pattern inside a patient LV is thought to be essential to capture, and subsequently treat, cardiovascular dysfunction at early stages to reduce the mortality and morbidity rates. Computational fluid dynamics (CFD) models, often derived from patient-specific medical imaging, have been used to provide a more fundamental understanding of individual LV flow patterns and pressure fields. Such image-based modeling may advance diagnostic capabilities, treatment protocols and help guide clinicians to choose the most effective therapy of CVDs. Most prior ventricular flow studies obtained LV wall geometries from in vivo ultrasound-based or cardiac magnetic resonance imaging (MRI) images with limited resolution. The model geometries were often highly simplified and usually lacked the papillary muscles (PM) and the corrugated trabecular structures of the LV. Since the LV flow pattern is sensitive to geometry, it is important to understand the effect of this simplification on modeling intraventricular flow and pressure. Here we apply CFD modeling to a subject-specific porcine LV model with detailed ventricular structures and motion obtained from previous solid mechanics finite-element (FE) simulations based on high-resolution image data. We simplified the detailed LV endocardial surfaces to remove PM and trabecular structures and built a smoothed model that resembles the resolution of in vivo MRI images. We then compare the simulated LV flow pattern and pressure of the simplified models to those of the complex model., Comment: This work was supported by the National Science Foundation SI2-SSI #1663671 and this project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Action H2020-MSCA-IF-2016 InsiliCardio, GA No. 750835 to CMA

Computational formulations for large strain, polyconvex, nearly incompressible elasticity have been extensively studied, but research on enhancing solution schemes that offer better tradeoffs between accuracy, robustness, and computational efficiency remains to be highly relevant. In this paper, we present two methods to overcome locking phenomena, one based on a displacement-pressure formulation using a stable finite element pairing with bubble functions, and another one using a simple pressure-projection stabilized P1-P1 finite element pair. A key advantage is the versatility of the proposed methods: with minor adjustments they are applicable to all kinds of finite elements and generalize easily to transient dynamics. The proposed methods are compared to and verified with standard benchmarks previously reported in the literature. Benchmark results demonstrate that both approaches provide a robust and computationally efficient way of simulating nearly and fully incompressible materials., Comment: This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Action H2020-MSCA-IF-2016 InsiliCardio, GA No. 750835 to CMA. Additionally, the research was supported by the Grants F3210-N18 and I2760-B30 from the Austrian Science Fund (FWF) and BioTechMed-Graz (Grant No. Flagship Project: ILearnHeart)

Saccharomyces cerevisiae target of rapamycin (TOR) complex 2 (TORC2) is an essential regulator of plasma membrane lipid and protein homeostasis. How TORC2 activity is modulated in response to changes in the status of the cell envelope is unclear. Here we document that TORC2 subunit Avo2 is a direct target of Slt2, the mitogen-activated protein kinase (MAPK) of the cell wall integrity pathway. Activation of Slt2 by overexpression of a constitutively active allele of an upstream Slt2 activator (Pkc1) or by auxin-induced degradation of a negative Slt2 regulator (Sln1) caused hyperphosphorylation of Avo2 at its MAPK phosphoacceptor sites in a Slt2-dependent manner and diminished TORC2-mediated phosphorylation of its major downstream effector, protein kinase Ypk1. Deletion of Avo2 or expression of a phosphomimetic Avo2 allele rendered cells sensitive to two stresses (myriocin treatment and elevated exogenous acetic acid) that the cell requires Ypk1 activation by TORC2 to survive. Thus, Avo2 is necessary for optimal TORC2 activity, and Slt2-mediated phosphorylation of Avo2 down-regulates TORC2 signaling. Compared with wild-type Avo2, phosphomimetic Avo2 shows significant displacement from the plasma membrane, suggesting that Slt2 inhibits TORC2 by promoting Avo2 dissociation. Our findings are the first demonstration that TORC2 function is regulated by MAPK-mediated phosphorylation., Comment: This work was supported by National Institutes of Health (NIH) Predoctoral Traineeship GM07232 and a University of California at Berkeley MacArthur and Lakhan-Pal Graduate Fellowship to K.L.L., Erwin Schroedinger Fellowship J3787-B21 from the Austrian Science Fund to AE-A, Marie Sklodowska-Curie Action H2020-MSCA-IF-2016 InsiliCardio, GA 75083 to CMA, and NIH R01 research grant GM21841 to JT

To observe internalization of the yeast pheromone receptor Ste2 by fluorescence microscopy in live cells in real time, we visualized only those molecules present at the cell surface at the time of agonist engagement (rather than the total cellular pool) by tagging this receptor at its N-terminus with an exocellular fluorogen-activating protein (FAP). A FAP is a single-chain antibody engineered to bind tightly a nonfluorescent, cell-impermeable dye (fluorogen), thereby generating a fluorescent complex. The utility of FAP tagging to study trafficking of integral membrane proteins in yeast, which possesses a cell wall, had not been examined previously. A diverse set of signal peptides and propeptide sequences were explored to maximize expression. Maintenance of the optimal FAP-Ste2 chimera intact required deletion of two, paralogous, glycosylphosphatidylinositol (GPI)-anchored extracellular aspartyl proteases (Yps1 and Mkc7). FAP-Ste2 exhibited a much brighter and distinct plasma membrane signal than Ste2-GFP or Ste2-mCherry yet behaved quite similarly. Using FAP-Ste2, new information was obtained about the mechanism of its internalization, including novel insights about the roles of the cargo-selective endocytic adaptors Ldb19/Art1, Rod1/Art4, and Rog3/Art7., Comment: This work was supported by Erwin Schroedinger Fellowship J3787-B21 from the Austrian Science Fund and by National Institutes of Health (NIH) R01 Research Grant GM21841. Additionally, this project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Action H2020-MSCA-IF-2016 InsiliCardio, GA No. 750835

FEniCS Mechanics is a Python package to facilitate computational mechanics simulations. The Python library dolfin, from the FEniCS Project, is used to formulate and numerically solve the problem in variational form. The general balance laws from continuum mechanics are used to enable rapid prototyping of different material laws. In addition to its generality, FEniCS Mechanics also checks the input provided by users to ensure that problem definitions are physically consistent. In turn, this code enables simulations of custom mechanics problems to be more accessible to those with limited programming or mechanics knowledge., Comment: This work was supported in part from the NSF award 1663747 and the NSF GRFP. Additionally, this project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Action H2020-MSCA-IF-2016 InsiliCardio, GA No. 750835

Computational fluid dynamics (CFD) models of blood flow in the left ventricle (LV) and aorta are important tools for analyzing the mechanistic links between myocardial deformation and flow patterns. Typically, the use of image-based kinematic CFD models prevails in applications such as predicting the acute response to interventions which alter LV afterload conditions. However, such models are limited in their ability to analyze any impacts upon LV load or key biomarkers known to be implicated in driving remodeling processes as LV function is not accounted for in a mechanistic sense. This study addresses these limitations by reporting on progress made towards a novel electro-mechano-fluidic (EMF) model that represents the entire physics of LV electromechanics (EM) based on first principles. A biophysically detailed finite element (FE) model of LV EM was coupled with a FE-based CFD solver for moving domains using an arbitrary Eulerian-Lagrangian (ALE) formulation. Two clinical cases of patients suffering from aortic coarctations (CoA) were built and parameterized based on clinical data under pre-treatment conditions. For one patient case simulations under post-treatment conditions after geometric repair of CoA by a virtual stenting procedure were compared against pre-treatment results. Numerical stability of the approach was demonstrated by analyzing mesh quality and solver performance under the significantly large deformations of the LV blood pool. Further, computational tractability and compatibility with clinical time scales were investigated by performing strong scaling benchmarks up to 1536 compute cores. The overall cost of the entire workflow for building, fitting and executing EMF simulations was comparable to those reported for image-based kinematic models, suggesting that EMF models show potential of evolving into a viable clinical research tool., Comment: This research was supported by the grants F3210-N18 and I2760-B30 from the Austrian Science Fund (FWF), the EU grant CardioProof agreement 611232 and a BioTechMed award to GP. Additionally, this project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Action H2020-MSCA-IF-2016 InsiliCardio, GA No. 750835

Introduction: Stenotic aortic valve disease (AS) causes pressure overload of the left ventricle (LV) that may trigger adverse remodeling and precipitate progression towards heart failure (HF). As myocardial energetics can be impaired during AS, LV wall stresses and biomechanical power provide a complementary view of LV performance that may aide in better assessing the state of disease. Objectives: Using a high-resolution electro-mechanical (EM) in silico model of the LV as a reference, we evaluated clinically feasible Laplace-based methods for assessing global LV wall stresses and biomechanical power. Methods: We used N = 4 in silico finite element (FE) EM models of LV and aorta of patients suffering from AS. All models were personalized with clinical data under pre-treatment conditions. LV wall stresses and biomechanical power were computed accurately from FE kinematic data and compared to Laplace-based estimation methods which were applied to the same FE model data. Results and Conclusion: Laplace estimates of LV wall stress are able to provide a rough approximation of global mean stress in the circumferential-longitudinal plane of the LV. However, according to FE results spatial heterogeneity of stresses in the LV wall is significant, leading to major discrepancies between local stresses and global mean stress. Assessment of mechanical power with Laplace methods is feasible, but these are inferior in accuracy compared to FE models. The accurate assessment of stress and power density distribution in the LV wall is only feasible based on patient-specific FE modeling., Comment: This research was supported by the grants F3210-N18 and I2760-B30 from the Austrian Science Fund (FWF), the EU grant CardioProof agreement 611232 and a BioTechMed award to GP. Additionally, this project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk{\l}odowska-Curie Action H2020-MSCA-IF-2016 InsiliCardio, GA No. 750835

High-resolution and anatomically realistic computer models of biological soft tissues play a significant role in the understanding of the function of cardiovascular components in health and disease. However, the computational effort to handle fine grids to resolve the geometries as well as sophisticated tissue models is very challenging. One possibility to derive a strongly scalable parallel solution algorithm is to consider finite element tearing and interconnecting (FETI) methods. In this study we propose and investigate the application of FETI methods to simulate the elastic behavior of biological soft tissues. As one particular example we choose the artery which is - as most other biological tissues - characterized by anisotropic and nonlinear material properties. We compare two specific approaches of FETI methods, classical and all-floating, and investigate the numerical behavior of different preconditioning techniques. In comparison to classical FETI, the all-floating approach has not only advantages concerning the implementation but in many cases also concerning the convergence of the global iterative solution method. This behavior is illustrated with numerical examples. We present results of linear elastic simulations to show convergence rates, as expected from the theory, and results from the more sophisticated nonlinear case where we apply a well-known anisotropic model to the realistic geometry of an artery. Although the FETI methods have a great applicability on artery simulations we will also discuss some limitations concerning the dependence on material parameters., Comment: 29 pages