Multiscale Computational Model Research Articles

Multiscale computational model of aortic remodeling following postnatal disruption of TGFβ signaling

The healthy adult aorta is a remarkably resilient structure, able to resist relentless cardiac-induced and hemodynamic loads under normal conditions. Fundamental to such mechanical homeostasis is the mechano-sensitive cell signaling that controls gene products and thus the structural integrity of the wall. Mouse models have shown that smooth muscle cell-specific disruption of transforming growth factor-beta (TGFβ) signaling during postnatal development compromises this resiliency, rendering the aortic wall susceptible to aneurysm and dissection under normal mechanical loading. By contrast, disruption of such signaling in the adult aorta appears to introduce a vulnerability that remains hidden under normal loading, but manifests under increased loading as experienced during hypertension. We present a multiscale (transcript to tissue) computational model to examine possible reasons for compromised mechanical homeostasis in the adult aorta following reduced TGFβ signaling in smooth muscle cells.

Platelet Biorheology and Mechanobiology in Thrombosis and Hemostasis: Perspectives from Multiscale Computation.

Thrombosis is the pathological clot formation under abnormal hemodynamic conditions, which can result in vascular obstruction, causing ischemic strokes and myocardial infarction. Thrombus growth under moderate to low shear (<1000 s-1) relies on platelet activation and coagulation. Thrombosis at elevated high shear rates (>10,000 s-1) is predominantly driven by unactivated platelet binding and aggregating mediated by von Willebrand factor (VWF), while platelet activation and coagulation are secondary in supporting and reinforcing the thrombus. Given the molecular and cellular level information it can access, multiscale computational modeling informed by biology can provide new pathophysiological mechanisms that are otherwise not accessible experimentally, holding promise for novel first-principle-based therapeutics. In this review, we summarize the key aspects of platelet biorheology and mechanobiology, focusing on the molecular and cellular scale events and how they build up to thrombosis through platelet adhesion and aggregation in the presence or absence of platelet activation. In particular, we highlight recent advancements in multiscale modeling of platelet biorheology and mechanobiology and how they can lead to the better prediction and quantification of thrombus formation, exemplifying the exciting paradigm of digital medicine.

Multiscale Modeling of Plutonium Radiation Chemistry in Nitric Acid Solutions. 1. Cobalt-60 Gamma Irradiation of Pu(IV).

Careful manipulation of the plutonium oxidation states is essential in the study and utilization of its rich redox chemistry. To achieve this level of control, a comprehensive mechanistic understanding of radiation-induced plutonium redox chemistry is critical due to the unavoidable exposure of plutonium to ionizing radiation fields, both inherent and from in-process applications. To this end, we have developed an experimentally evaluated multiscale computer model for the prediction of gamma radiation-induced Pu(IV) redox chemistry in concentrated nitric acid solutions (1.0, 3.0, and 6.0 M). Under these acidic, aqueous solution conditions, cobalt-60 gamma irradiation afforded marginal net conversion of Pu(IV) to Pu(VI), the extent of which was dependent on the concentration of HNO3 and absorbed gamma dose. Multiscale calculations, which are in excellent agreement with experimental data, indicate that this observation is due to a combination of inherent plutonium disproportionation reactions and several radiation-induced processes, including redox cycling between Pu(IV) and Pu(III), as achieved by the reduction of Pu(IV) by nitrous acid and hydrogen peroxide, the oxidation of Pu(III) by nitrate and hydroxyl radicals, and the sequential oxidation of Pu(IV) to Pu(V) and Pu(VI) by the remaining available yield of nitrate radicals.

Comparing the bone regeneration potential between a trabecular bone and a porous scaffold through osteoblast migration and differentiation: A multiscale approach.

Both cell migration and osteogenic differentiation are critical for successful bone regeneration. Therefore, understanding the mechanobiological aspects that govern these two processes is essential in designing effective scaffolds that promote faster bone regeneration. Studying these two factors at different locations is necessary to manage bone regeneration in various sections of a scaffold. Hence, a multiscale computational model was used to observe the mechanical responses of osteoblasts placed in different positions of the trabecular bone and gyroid scaffold. Fluid shear stresses in scaffolds at cell seeded locations (representing osteogenic differentiation) and strain energy densities in cells at cell substrate interface (representing cell migration) were observed as mechanical response parameters in this study. Comparison of these responses, as two critical factors for bone regeneration, between the trabecular bone and gyroid scaffold at different locations, is the overall goal of the study. This study reveals that the gyroid scaffold exhibits higher osteogenic differentiation and cell migration potential compared to the trabecular bone. However, the responses in the gyroid only mimic the trabecular bone in two out of nine positions. These findings can guide us in predicting the ideal cell seeded sites within a scaffold for better bone regeneration and in replicating a replaced bone condition by altering the physical parameters of a scaffold.

Multiscale Computational Framework for the Liquid-Liquid Phase Separation of Intrinsically Disordered Proteins.

The reversible assembly of intrinsically disordered proteins (IDPs) to form membraneless organelles (MLOs) is a fundamental process involved in the spatiotemporal regulation in living cells. MLOs formed via liquid-liquid phase separation (LLPS) serve as molecule-enhancing hubs to regulate cell functions. Owing to the complexity and dynamic nature of the protein assembly via a network of weak inter- and intra-molecular interactions, it is challenging to describe and predict the LLPS behavior. We have developed a multiscale computational model for IDPs, using the fused in sarcoma (FUS) protein and its variants as illustrative examples. To simplify the description of protein, FUS is represented as a linear chain of stickers interspaced by spacers, as inspired by the associative polymer theory. Low-complexity aromatic-rich kinked segments (LARKS) available in FUS were identified using LARKSdb and represented as "stickers". The pairwise potential energies of each pair of stickers and their β-sheet-forming propensity were estimated via molecular docking and all atomistic molecular dynamics (AA-MD) simulations. Subsequently, FUS chains were randomly positioned in a cubic lattice as coarse-grained (CG) beads, with the bead assignment based on the Kuhn length estimation of stickers and spacers. Stochastic FUS movements were modeled by Monte Carlo (MC) simulations. In addition to the Metropolis algorithm, discretized pair potential distributions between stickers were considered in the move acceptance criteria. The chosen pair potential represents one of the possible binding energy states, with its probability determined by the frequency of the binding energy distribution histogram. The fluctuations of averaged radial distribution functions (RDFs) in successive MC trial move intervals of equilibrated lattice MC simulations were used to indicate the dynamic nature of assembly/disassembly of the protein chains. This multiscale computational framework provides an economical and efficient way of predicting and describing the LLPS behavior of IDPs.

Higher-order multi-scale computational approach and its convergence for nonlocal gradient elasticity problems of composite materials

A novel higher-order multi-scale computational approach for efficient nonlocal gradient elasticity simulation of composite materials is proposed in this paper. Based on the use of decoupling technique, raw fourth-order nonlocal gradient equations with periodically oscillatory coefficients are decoupled as the new second-order multi-scale equations for reducing the continuity requirements when implementing multi-scale simulation. Next, a novel macro-micro coupled multi-scale computational model with higher-order corrected terms is rigorously devised for high-resolution multi-scale and nonlocal analysis of composite materials via multi-scale asymptotic analysis. Then, the local error analysis of higher-order multi-scale solutions in the point-wise sense illustrates that establishing higher-order asymptotic corrected terms plays a vital role in investigating the micromechanical mechanisms. Moreover, a rigorous global error estimation with an explicit rate of higher-order multi-scale solutions is first derived in the energy norm sense. In addition, an efficient multi-scale numerical algorithm is presented to effectively simulate nonlocal gradient elasticity problems of composite materials based on finite element method (FEM). And we also derive the convergence estimation of the proposed numerical algorithm. Finally, numerical experiments are conducted to gauge the efficiency and accuracy of the proposed higher-order multi-scale method. This paper offers a unified higher-order two-scale computational framework for enabling nonlocal gradient elasticity behavior simulation of composite materials.

Making the Case for Scaling Up Microwave Sintering of Ceramics

The densification and sintering of ceramics using microwaves is first reported in the mid‐1960s. Today, the reduced carbon footprint of this process has renewed interest as it uses less energy overall compared to conventional process heating/furnaces. However, scaling up and commercializing the microwave sintering process of ceramics remains a formidable challenge. As a contactless method, microwave sintering offers geometric flexibility over other field‐assisted sintering processes. Yet, the inability to address multiscale, multiphysics‐driven heterogeneities arising during microwave coupling limits discussions about a future scale‐up process. Herein, the case is made that unlike 60 years ago, new advances in multiscale computational modeling, materials characterization, control systems, and software open up new avenues for addressing these challenges. More importantly, the rise of additive manufacturing techniques demands the innovation of sintering processes in the ceramics community for realizing near‐net‐shaped and complex parts for applications ranging from medical implants to automotive and aerospace parts.

Multiscale Computational and Artificial Intelligence Models of Linear and Nonlinear Composites: A Review

Herein, state‐of‐the‐art multiscale modeling methods have been described. This research includes notable molecular, micro‐, meso‐, and macroscale models for hard (polymer, metal, yarn, fiber, fiber‐reinforced polymer, and polymer matrix composites) and soft (biological tissues such as brain white matter [BWM]) composite materials. These numerical models vary from molecular dynamics simulations to finite‐element (FE) analyses and machine learning/deep learning surrogate models. Constitutive material models are summarized, such as viscoelastic hyperelastic, and emerging models like fractional viscoelastic. Key challenges such as meshing, data variability and material nonlinearity‐driven uncertainty, limitations in terms of computational resources availability, model fidelity, and repeatability are outlined with state‐of‐the‐art models. Latest advancements in FE modeling involving meshless methods, hybrid ML and FE models, and nonlinear constitutive material (linear and nonlinear) models aim to provide readers with a clear outlook on futuristic trends in composite multiscale modeling research and development. The data‐driven models presented here are of varying length and time scales, developed using advanced mathematical, numerical, and huge volumes of experimental results as data for digital models. An in‐depth discussion on data‐driven models would provide researchers with the necessary tools to build real‐time composite structure monitoring and lifecycle prediction models.

A Computational Multiscale Modeling Method for Nanosilver-Sintered Joints with Stochastically Distributed Voids

A high power density is required in wide band gap power semiconductor packaging, which has led to the popularity of sintered nanosilver as an interconnecting material. However, affected by stochastically distributed voids in its microstructure, this material in practice exhibits instability leading to reduced reliability. In this paper, a computational multiscale modeling method is proposed to simulate the influence of micro-voids on macro-properties, providing an efficient tool to analyze the aforementioned problem. At the micro-scale, the three-parameter Weibull distribution of the equivalent Young’s modulus and the normal distribution of the equivalent Poisson’s ratio are captured by Monte Carlo-based finite element simulation on the reconstructed stochastic representative elements, where the density and distribution morphology of micro-voids are taken into consideration. At the macro-scale, the effect of the microscopic voids is transferred through a random sampling process to construct the multiscale model. The effectiveness and validity of the proposed method are verified through experimental case studies involving the modeling of nanosilver-sintered joints sintered at temperatures of 275°C and 300°C. In addition, the effects of the sintering temperature on the dispersion of the micro-voids, the distribution fluctuation of the constitutive parameters, and the mechanical properties are also discussed based on numerical and experimental results.Graphical

Spatiotemporal modeling of nano-delivered chemotherapeutics for synergistic microwave ablation cancer therapy

Background and objectiveThe effectiveness of current microwave ablation (MWA) therapies is limited. Administration of thermosensitive liposomes (TSLs) which release drugs in response to heat has presented a significant potential for enhancing the efficacy of thermal ablation treatment, and the benefits of targeted drug delivery. However, a complete knowledge of the mechanobiological processes underlying the drug release process, especially the intravascular drug release mechanism and its distribution in response to MWA needs to be improved. Multiscale computational-based modeling frameworks, integrating different biophysical phenomena, have recently emerged as promising tools to decipher the mechanobiological events in combo therapies. The present study aims to develop a novel multiscale computational model of TSLs delivery following MWA implantation. MethodsDue to the complex interplay between the heating procedure and the drug concentration maps, a computational model is developed to determine the intravascular release of doxorubicin from TSL, its transvascular transport into the interstitium, transport in the interstitium, and cell uptake. Computational models can estimate the interplays among liposome and drug properties, tumor perfusion, and heating regimen to examine the impact of essential parameters and to optimize a targeted drug delivery platform. ResultsResults indicated that the synergy of TSLs with MWA allows more localized drug delivery with lower side effects. The drug release rate and tumor permeability play crucial roles in the efficacy of TSLs during MWA treatment. The computational model predicted an unencapsulated drug lime around the ablated zone, which can destroy more cancer cells compared to MWA alone by 40%. Administration of TSLs with a high release rate capacity can improve the percentage of killed cancer cells by 24%. Since the heating duration in MWA is less than 15 min, the presented combination therapy showed better performance for highly permeable tumors. ConclusionThis study highlights the potential of the proposed computational framework to address complex and realistic scenarios in cancer treatment, which can serve as the future research foundation, including advancements in nanomedicine and optimizing the pair of TSL and MWA for both preclinical and clinical studies. The present model could be as a valuable tool for patient-specific calibration of essential parameters.

Mechanistic-based probabilistic optimization of industrial wood drying considering energy consumption, process duration, quality and cost

Drying is the most energy consuming process in the industrial transformation of wood. The current energy and climate crisis makes it imperative to adapt the process to energy availability, in terms of quantity, cost and temperature level. Wood drying schedules are historically based on practice. In the present work, a mechanistic drying model is used as a predictive tool to adapt conditions to specific situations. A multiscale computational model, Multi_Wood_DryS, has been combined with a probabilistic optimization code to propose tailor-made drying schedules that meet operators’ expectations in terms of energy consumption, quality, drying time and cost. Optimizations of drying schedules of a stack of boards are proposed. The cost-optimized schedule is advantageous for all criteria with a 48% reduction in drying time. The resulting metamodel is a first step toward an intelligent controller for wood drying.

Interactions between calcium-induced arrhythmia triggers and the electrophysiological-anatomical substrate underlying the induction of atrial fibrillation.

Atrial fibrillation (AF) is the most common cardiac arrhythmia and is sustained by spontaneous focal excitations and re-entry. Spontaneous electrical firing in the pulmonary vein (PV) sleeves is implicated in AF generation. The aim of this simulation study was to identify the mechanisms determining the localisation of AF triggers in the PVs and their contribution to the genesis of AF. A novel biophysical model of the canine atria was used that integrates stochastic, spontaneous subcellular Ca2+ release events (SCRE) with regional electrophysiological heterogeneity in ionic properties and a detailed three-dimensional model of atrial anatomy, microarchitecture and patchy fibrosis. Simulations highlighted the importance of the smaller inward rectifier potassium current (IK1 ) in PV cells compared to the surrounding atria, which enabled SCRE more readily to result in delayed-afterdepolarisations that induced triggered activity. There was a leftward shift in the dependence of the probability of triggered activity on sarcoplasmic reticulum Ca2+ load. This feature was accentuated in 3D tissue compared to single cells (Δ half-maximal [Ca2+ ]SR =58μM vs. 22μM). In 3D atria incorporating electrical heterogeneity, excitations preferentially emerged from the PV region. These triggered focal excitations resulted in transient re-entry in the left atrium. Addition of fibrotic patches promoted localised emergence of focal excitations and wavebreaks that had a more substantial impact on generating AF-like patterns than the PVs. Thus, a reduced IK1 , less negative resting membrane potential, and fibrosis-induced changes of the electrotonic load all contribute to the emergence of complex excitation patterns from spontaneous focal triggers. KEY POINTS: Focal excitations in the atria are most commonly associated with the pulmonary veins, but the mechanisms for this localisation are yet to be elucidated. We applied a multi-scale computational modelling approach to elucidate the mechanisms underlying such localisations. Myocytes in the pulmonary vein region of the atria have a less negative resting membrane potential and reduced time-independent potassium current; we demonstrate that both of these factors promote triggered activity in single cells and tissues. The less negative resting membrane potential also contributes to heterogeneous inactivation of the fast sodium current, which can enable re-entrant-like excitation patterns to emerge without traditional conduction block.

Computational demystification of iron carbonyls formation under syngas environment

Iron pentacarbonyl (IPC) gas forms upon the reaction of carbon monoxide with Fe containing metallic surfaces under gas reforming conditions. IPC formation can sometimes reach alarming levels that cause metal loss, pipeline thinning corrosion, catalyst poisoning, and contamination of sensitive industrial equipment. In this work, we demystify using multiscale computational modeling the mechanism of Iron pentacarbonyl formation: Density functional theory (DFT) is used to explore various catalytic reactions that involve a Fe adatom reacting with adsorbed carbon monoxide. Our calculated carbonyls desorption barriers on a perfect and clean Fe surface are too high to allow the carbonyls to form then desorb at temperatures <500 K at the rates reported experimentally. Most importantly, our calculations indicate that a high CO surface coverage, in addition to the presence of Fe adatoms, favors carbonyl formation and its desorption towards the flowing gas medium. Using insights extracted from ab initio molecular dynamics simulations, we propose that the most plausible IPC formation mechanism consists of: (1) on surface reactions of adsorbed CO molecules with an Fe adatom to form iron tricarbonyl (Fe(CO)3*) molecules; (2) an adsorbate assisted movement of iron tricarbonyl on top of the CO adlayer; and (3) the interaction of iron tricarbonyl with CO molecules from the gaseous medium eventually leading to iron adatom removal as Fe(CO)5 gas.

Abstract IA019: Multiscale systems approach to target tumor ecosystem responses for therapeutic benefit

Abstract Breast tumors arise and progress via processes that involve intrinsic deregulation of epithelial cells and that also alter the composition and function of associated stromal and immune cells. Together, these tumor-intrinsic and microenvironmental changes enable malignant epithelial cells in the tumor to acquire key cancer hallmarks, including proliferation, migration, immune evasion and further evolution. The resulting collection of cancer and stromal cells comprise a complex, adaptive tumor ecosystem. Long-term therapeutic success will require that drugs attack malignant cells, favorably activate the immune system, and create a tumor microenvironment that does not support tumor (re)growth. Creating a process for treatment selection will require an integrated systems biology approach to develop, test and refine a robust computational framework that takes into account the states, signaling relationships, and spatial organization that determine the emergent properties inherent in complex interacting systems. Toward this goal, we have developed a coordinated experimental-computational approach designed to enable identification of effective treatment strategies. To examine therapeutic responses of diverse aspects of the tumor ecosystem, we have deployed a novel drug delivery microdevice that enables rapid, high-throughput assessment of the effects of multiple therapies on tumor cells and the surrounding microenvironment. When coupled with multiplex tissue imaging, this platform provides a comprehensive assessment of the state and spatial organization of the tumor ecosystem as it adapts to therapy. Initial studies demonstrated that many drugs designed to target malignant epithelial cells strongly impact stromal and immune cells, providing new insights into the importance of considering multiple aspects of the tumor ecosystem when designing effective therapeutic strategies. The resultant data are being used to guide development of a multiscale computational model that considers the functional relationships between different tumor components and can be used to predict the effects of therapies on diverse aspects of tumor ecosystems. In total, our approach has the potential to transform cancer treatments through rational identification of effective therapeutic strategies that lead to long-term durable control of tumors. Citation Format: Laura M. Heiser. Multiscale systems approach to target tumor ecosystem responses for therapeutic benefit [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr IA019.

Genetically engineered HEK cells as a valuable tool for studying electroporation in excitable cells

Electric pulses used in electroporation-based treatments have been shown to affect the excitability of muscle and neuronal cells. However, understanding the interplay between electroporation and electrophysiological response of excitable cells is complex, since both ion channel gating and electroporation depend on dynamic changes in the transmembrane voltage (TMV). In this study, a genetically engineered human embryonic kidney cells expressing NaV1.5 and Kir2.1, a minimal complementary channels required for excitability (named S-HEK), was characterized as a simple cell model used for studying the effects of electroporation in excitable cells. S-HEK cells and their non-excitable counterparts (NS-HEK) were exposed to 100 µs pulses of increasing electric field strength. Changes in TMV, plasma membrane permeability, and intracellular Ca2+ were monitored with fluorescence microscopy. We found that a very mild electroporation, undetectable with the classical propidium assay but associated with a transient increase in intracellular Ca2+, can already have a profound effect on excitability close to the electrostimulation threshold, as corroborated by multiscale computational modelling. These results are of great relevance for understanding the effects of pulse delivery on cell excitability observed in context of the rapidly developing cardiac pulsed field ablation as well as other electroporation-based treatments in excitable tissues.

PREFACE: MULTISCALE AND MULTIPHYSICS COMPUTATIONAL MODELING IN GEOMECHANICS

PREFACE: MULTISCALE AND MULTIPHYSICS COMPUTATIONAL MODELING IN GEOMECHANICS

Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation.

The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.

Multi-{Physics, Phase, Scale} Computational Modeling of Interface-Coupled Problems in Redox Flow Battery Design

Motivated by an urgent need to deploy large-scale energy storage technologies to integrate intermittent renewables, redox flow batteries (RFBs) are gaining prominent attention due to their interesting features including higher durability and power-energy decoupling. RFBs are rechargeable electrochemical systems in which an electrolyte solution containing dissolved or suspended active species is stored in external tanks and pumped through the electrochemical stack - consisting of flow fields, porous electrodes, and membranes - where the active species undergo electrochemical reactions to charge/discharge the battery. However, large-scale deployment of RFBs has feasibility issues regarding costs and efficiency, which can be tackled by tuning their multi-scale design parameters [1].Developing a coupled multi-scale computational model for RFBs can facilitate our understanding of the interplay between intricate phenomena occurring at several length and time scales (depicted schematically in the figure), ranging from transport phenomena, i.e., mass, charge, momentum, and heat, on the cell level (macro and meso scales) to microstructure effects and wettability behavior on the pore-scale (micro and nano scales). Furthermore, the modeling framework should capture the reaction kinetics and multi-phase chemistry of emerging flow battery concepts (e.g. metal-air) including plating and stripping reactions [2].In this research, we are developing a multi-{physics, phase, scale} model for the optimization of next-generation RFBs in the cell-, electrode-, and pore-scale. The model includes free fluid flow (representing flow field channels) over a porous medium (porous electrode) and solves the transport mechanisms over the entire computational domain with a focus on the electrolyte-electrode interface. The model is developed using finite element/volume methods combined with pore network modeling techniques, implemented using various open-source modeling frameworks including OpenFOAM [3], FEniCS [4], and OpenPNM [5].In this presentation, I will first discuss the structure of the modeling framework including the continuum transport models, the pore network model, and the employed numerical schemes. Then, I will present the coupling between continuum and porous medium models. Finally, I will highlight some initial results focused on the efficiency of the coupling of the electrolyte fluid transport with the porous medium and pore network representation of the electrode [6]. We hope that the developed model can be a functional tool to assess and optimize RFB materials and operating conditions and can be broadly adopted to model emerging flow battery chemistries. References K. Chakrabarti et al., Sustainable Energy & Fuels. 4, 5433–5468 (2020).Hein et al., ACS Applied Energy Materials. 3, 8519–8531 (2020).G. Weller et al., Computers in Physics. 12, 620 (1998).Alnæs et al., Archive of Numerical Software, in press, doi:10.11588/ANS.2015.100.20553.Gostick et al., Computing in Science & Engineering. 18, 60–74 (2016).van der Heijden et al., Journal of The Electrochemical Society. 169, 040505 (2022). Figure 1

Evaluating pro-arrhythmogenic effects of the T634S-hERG mutation: insights from a simulation study.

A mutation to serine of a conserved threonine (T634S) in the hERG K+ channel S6 pore region has been identified as a variant of uncertain significance, showing a loss-of-function effect. However, its potential consequences for ventricular excitation and arrhythmogenesis have not been reported. This study evaluated possible functional effects of the T634S-hERG mutation on ventricular excitation and arrhythmogenesis by using multi-scale computer models of the human ventricle. A Markov chain model of the rapid delayed rectifier potassium current (IKr) was reconstructed for wild-type and T634S-hERG mutant conditions and incorporated into the ten Tusscher et al. models of human ventricles at cell and tissue (1D, 2D and 3D) levels. Possible functional impacts of the T634S-hERG mutation were evaluated by its effects on action potential durations (APDs) and their rate-dependence (APDr) at the cell level; and on the QT interval of pseudo-ECGs, tissue vulnerability to unidirectional conduction block (VW), spiral wave dynamics and repolarization dispersion at the tissue level. It was found that the T634S-hERG mutation prolonged cellular APDs, steepened APDr, prolonged the QT interval, increased VW, destablized re-entry and augmented repolarization dispersion across the ventricle. Collectively, these results imply potential pro-arrhythmic effects of the T634S-hERG mutation, consistent with LQT2.

Multiscale computational modeling of aortic valve calcification.

Calcific aortic valve disease (CAVD) is a common cardiovascular disease that affects millions of people worldwide. The disease is characterized by the formation of calcium nodules on the aortic valve leaflets, which can lead to stenosis and heart failure if left untreated. The pathogenesis of CAVD is still not well understood, but involves several signaling pathways, including the transforming growth factor beta (TGF ) pathway. In this study, we developed a multiscale computational model for TGF -stimulated CAVD. The model framework comprises cellular behavior dynamics, subcellular signaling pathways, and tissue-level diffusion fields of pertinent chemical species, where information is shared among different scales. Processes such as endothelial to mesenchymal transition (EndMT), fibrosis, and calcification are incorporated. The results indicate that the majority of myofibroblasts and osteoblast-like cells ultimately die due to lack of nutrients as they become trapped in areas with higher levels of fibrosis or calcification, and they subsequently act as sources for calcium nodules, which contribute to a polydispersed nodule size distribution. Additionally, fibrosis and calcification processes occur more frequently in regions closer to the endothelial layer where the cell activity is higher. Our results provide insights into the mechanisms of CAVD and TGF signaling and could aid in the development of novel therapeutic approaches for CAVD and other related diseases such as cancer. More broadly, this type of modeling framework can pave the way for unraveling the complexity of biological systems by incorporating several signaling pathways in subcellular models to simulate tissue remodeling in diseases involving cellular mechanobiology.