Multiscale Computational Modeling of Vascular Adaptation: A Systems Biology Approach Using Agent-Based Models

Bridging hemodynamics, tissue mechanics, and pathophysiology in coronary artery disease: A new agent-based model with tetrahedral mesh integration

We introduce a new multi-physics, multi-scale modeling approach to understand plaque progression during coronary artery disease. Prior works have coupled agent-based models (ABMs) with finite element analysis (FEA) or computational fluid dynamics (CFD) to study the individual contributions of tissue mechanics or hemodynamics to plaque growth. However, these approaches could not simultaneously capture the dynamic interplay between all three domains that drive plaque development. This study aims to present a novel method that merges hemodynamics via CFD, biological processes via ABM, and biomechanics via FEA into a single multi-scale, multi-physics simulation (CAFe). A description of the mechanisms and modeling approaches utilized in the CAFe model is provided, as well as preliminary exploration of the model's capabilities in idealized healthy and stenosed coronary artery models. A volumetric 3D tetrahedral mesh of the artery is shared between CFD, ABM, and FEA to simulate geometrical and biological changes with continuity and consistency. The CFD and FEA modules, implemented with FEBio, calculate the wall shear stress and structural stress and strain, respectively. These biomechanical values are passed to the ABM, implemented in MATLAB, which simulates vascular remodeling using molecular diffusion, cell migration, equations for cellular processes, and volumetric growth to update the geometry. Initial results using CAFe suggest atherosclerotic arteries seek to maintain a hemodynamic threshold through preferential growth and remodeling downstream of a stenosis. The innovative approach described herein marks a significant step forward in predictive modeling of CAD progression and paves the way for powerful coupling of the spatiotemporal-dependent remodeling paradigms exhibited by the disease.

Clinical Validation of the PSCOPE Hybrid Model Prediction of Left Ventricular Assist Device Implantation Hemodynamics: Three Patient-Specific Cases

Objective

The Physiology Simulation Coupled Experiment (PSCOPE) is a hybrid modeling framework designed for mechanistic cardiovascular predictive modeling. It couples a physical fluid experiment with a lumped parameter network simulation to replicate the closed-loop feedback between simulated cardiovascular physiology and fluid dynamics in the physical experiment. This study validates PSCOPE's predictions of post-surgical physiology against clinical data in the context of HeartMate 3 left ventricular assist device implantation.

Methods

We designed a protocol to characterize the pre- and post-surgical hemodynamics of three adult HeartMate 3 patients using perioperative clinical measurements acquired from routine intensive care unit monitoring. For each patient, we tuned a lumped parameter network model to match their pre-surgical hemodynamic values, creating a patient-specific simulation of the pre-surgical physiology. The PSCOPE framework then modeled LVAD implantation by coupling these simulations to a physical HeartMate 3 device flow experiment. This hybrid model estimates physiological flow rate and pressure parameters to predict the patients' post-surgical hemodynamics.

Results

The percentage difference between PSCOPE predictions and clinical post-surgical hemodynamics ranged from 0.0% to 44.7% across different hemodynamic parameters in different patients. The predicted cardiac index, mean pulmonary arterial pressure, central venous pressure, and pulmonary arterial wedge pressure together accurately indicated the absence of post-implant right ventricular failure in all patients.

Conclusion

This validation study demonstrates the potential of PSCOPE in assisting LVAD patient management. PSCOPE hemodynamic predictions could help clinicians anticipate and manage post-implant outcomes, such as right ventricular failure, thereby improving the efficacy of surgical planning.

Investigating the relationship between geometry and hemodynamics in an experimentally derived murine coronary computational model

Despite the critical role of coronary morphology and hemodynamics in the development of coronary artery disease (CAD), comprehensive analyses of these factors in murine models are limited. Our study integrates in vivo approaches with computational methods to yield a complete set of precise and reliable morphologic and hemodynamic measurements and to investigate their interrelationship in the left coronary artery of healthy C57BL/6 mice. The work utilizes advanced micro-computed tomography imaging, enhanced with Microfil® coronary perfusion, complemented by morphometric analysis and computational fluid dynamic simulation. Our results in murine coronary arteries show: i) bifurcations are the most geometrically complex regions, susceptible to disturbed hemodynamics and, consequently, endothelial dysfunction; ii) vascular endothelial cells experience wall shear stress (WSS) an order of magnitude greater than in humans, primarily due to their smaller size, although minimal WSS multi-directionality is noted in both species; iii) intravascular flow exhibits reduced helical patterns compared to human coronaries, indicating a need for further investigation into their potential protective role against disease onset; and iv) strong correlations between geometric and hemodynamic indices highlight the need to integrate these factors for a comprehensive understanding of CAD initiation and progression in preclinical models. Thus, to optimize research based on murine models, it is essential not only to move beyond idealized geometries, but also to avoid uncritically relying on hemodynamic measurements from different species. This study grounds future development of mouse-specific predictive models of CAD, a critical step toward advancing translational research to understand and prevent CAD in humans.

Growth in a two-dimensional model of coarctation of the aorta: A CFD-informed agent based model

In the individualized treatment of a patient with Coarctation of the Aorta (CoA), a non-severe case which initially exhibits no symptoms, and thus requires no treatment, could potentially become severe over time. This progression can be attributed to insufficient growth at the coarctation site relative to the overall growth of the child. Therefore, an agent-based model (ABM) to predict the aortic growth of a CoA patient is introduced. The multi-scale approach combines Computational Fluid Dynamics (CFD) and ABM to study systems that are influenced by both mechanical stimuli and biochemical responses characteristic of growth. Our focus is on ABM development; thus, CFD insights were applied solely to enhance the ABM framework. Comparative medicine was leveraged to develop a species-specific ABM by considering the rat and porcine species commonly used in cardiovascular research together with data from healthy human toddlers. The ABM luminal radius prediction accuracy was observed to be 79% for rat, above 95% for porcine and 91. 6% for the healthy toddler; while that observed for the growth rate was 38.7%, 90% and 64.3% respectively. Given its performance, the ABM was adapted to a 2.5-year-old patient-specific CoA. Subsequently, the model predicted that by age 3, the condition would worsen, marked by persistent CoA enhanced by the predicted least growth compared to growth predicted in the rest of the aorta, hypertension, and increased turbulent flow; thus, increased vessel injury risk. The findings advise for incorporating vascular remodelling into the ABM to enhance its predictive capability for intervention planning.

Plaque heterogeneity influences in-stent restenosis following drug-eluting stent implantation: Insights from patient-specific multiscale modelling

In-stent restenosis represents a major cause of failure of percutaneous coronary intervention with drug-eluting stent implantation. Computational multiscale models have recently emerged as powerful tools for investigating the mechanobiological mechanisms underlying vascular adaptation processes during in-stent restenosis. However, to date, the interplay between intervention-induced inflammation, drug delivery and drug retention has been under-investigated. Here, an original patient-specific multiscale agent-based modelling framework was developed to investigate the interplay between drug release, plaque composition and intervention-induced inflammation on in-stent restenosis following drug-eluting stent implantation. The framework integrated a finite element simulation of stent expansion, with a drug transport simulation and an agent-based model of cellular dynamics. A patient-specific coronary cross-section with heterogeneous diseased tissue was considered and rigorously analyzed through a variety of scenarios, including different plaque compositions and different inflammatory responses. The analysis revealed three significant findings: (i) calcifications substantially impeded drug transport, resulting in drug-depleted regions and reduced stent efficacy; (ii) by impacting drug transport, variations in plaque composition influenced arterial wall response, with the fully-calcific scenario showing the greatest lumen area reduction; (iii) the impact of different drug receptor saturation conditions (obtained with different plaque compositions) was particularly evident under conditions of persistent inflammatory state. This study represents a significant advancement in multiscale modelling of in-stent restenosis following drug-eluting stent implantation. The results obtained provided deeper insights into the complex interactions among patient-specific plaque composition, inflammation and drug retention, suggesting a patient-specific management of the intervention, particularly in cases of complex disease.

Patient-specific, multiscale modelling of neointimal hyperplasia in lower-limb vein grafts using readily available clinical data

The prediction of neointimal hyperplasia (NIH) growth, leading to vein graft failure in lower-limb peripheral arterial disease (PAD), is hindered by the multifactorial and multiscale mechanobiological mechanisms underlying the vascular remodelling process. Multiscale in silico models, linking patients' hemodynamics to NIH pathobiological mechanisms, can serve as a clinical support tool to monitor disease progression. Here, we propose a new computational pipeline for simulating NIH growth, carefully balancing model complexity/inclusion of mechanisms and readily available clinical data, and we use it to predict NIH growth for an entire vein graft. To this end, three different fittings to published in vitro data of time-averaged wall shear stress (TAWSS) vs nitric oxide (NO) production were tested for predicting long-term graft response (10-month follow-up) on a single patient. Additionally, the sensitivity of the model's predictions to different inflow boundary conditions (BCs) was assessed. The main findings indicate that: (i) a TAWSS-NO hyperbolic relationship best predicts long-term graft response; (ii) the model is insensitive to the inflow BCs if the waveform shape and the systolic acceleration time are comparable with the one acquired at the same time as the computed-tomography scan. This proof-of-concept study demonstrates the potential of using multiscale, computational techniques to predict NIH growth in lower-limb vein grafts, considering the routine clinical scenario of non-standardised data collection and sparse, incomplete datasets.

A quantitative study of the effects of a dual layer coating drug-eluting stent on safety and efficacy

A key strategy for increasing drug mass (DM) while maintaining good safety is to improve the drug release profile (RP). We designed a dual layer coating drug-eluting stent (DES) that exhibited smaller concentration gradients between the coating and the artery wall and significantly impacted the drug RP. However, a detailed understanding of the effects of the DES designed by our team on safety and efficacy is still lacking. The objective of this study was to provide a comprehensive multiscale computational framework that would allow us to probe the safety and efficacy of the DES we designed. This framework consisted of four coupled modules, namely (1) a mechanical stimuli module, simulating mechanical stimuli caused by percutaneous coronary intervention through a finite element analysis, (2) an inflammation module, simulating inflammation of vascular smooth muscle cells (VSMCs) induced by mechanical stimuli through an agent-based model (ABM), (3) a drug transport module, simulating drug transport through a continuum-based approach, and (4) a mitosis module, simulating VSMC mitosis through an ABM. Our results indicated that when the DM increased to two times the initial DM value, the DES we designed had higher safety and lower efficacy values than a conventional DES. When the DM increased to five times the initial DM value, the DES we designed had higher safety than a conventional DES, and negligible differences in efficacy compared with a conventional DES. In summary, the DES we designed exhibited a significant advantage in safety, but a slightly reduced efficacy compared with that of a conventional DES.

Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine

Cancer exhibits substantial heterogeneity, manifesting as distinct morphological and molecular variations across tumors, which frequently undermines the efficacy of conventional oncological treatments. Developments in multiomics and sequencing technologies have paved the way for unraveling this heterogeneity. Nevertheless, the complexity of the data gathered from these methods cannot be fully interpreted through multimodal data analysis alone. Mathematical modeling plays a crucial role in delineating the underlying mechanisms to explain sources of heterogeneity using patient-specific data. Intra-tumoral diversity necessitates the development of precision oncology therapies utilizing multiphysics, multiscale mathematical models for cancer. This review discusses recent advancements in computational methodologies for precision oncology, highlighting the potential of cancer digital twins to enhance patient-specific decision-making in clinical settings. We review computational efforts in building patient-informed cellular and tissue-level models for cancer and propose a computational framework that utilizes agent-based modeling as an effective conduit to integrate cancer systems models that encode signaling at the cellular scale with digital twin models that predict tissue-level response in a tumor microenvironment customized to patient information. Furthermore, we discuss machine learning approaches to building surrogates for these complex mathematical models. These surrogates can potentially be used to conduct sensitivity analysis, verification, validation, and uncertainty quantification, which is especially important for tumor studies due to their dynamic nature.

Impact of geometric and hemodynamic changes on a mechanobiological model of atherosclerosis

BACKGROUND AND OBJECTIVE

In this work, the analysis of the importance of hemodynamic updates on a mechanobiological model of atheroma plaque formation is proposed.

METHODS

For that, we use an idealized and axisymmetric model of carotid artery. In addition, the behavior of endothelial cells depending on hemodynamical changes is analyzed too. A total of three computational simulations are carried out and their results are compared: an uncoupled model and two models that consider the opposite behavior of endothelial cells caused by hemodynamic changes. The model considers transient blood flow using the Navier-Stokes equation. Plasma flow across the endothelium is determined with Darcy's law and the Kedem-Katchalsky equations, considering the three-pore model, which is also employed for the flow of substances across the endothelium. The behavior of the considered substances in the arterial wall is modeled with convection-diffusion-reaction equations, and the arterial wall is modeled as a hyperelastic Yeoh's material.

RESULTS

Significant variations are noted in both the morphology and stenosis ratio of the plaques when comparing the uncoupled model to the two models incorporating updates for geometry and hemodynamic stimuli. Besides, the phenomenon of double-stenosis is naturally reproduced in the models that consider both geometric and hemodynamical changes due to plaque growth, whereas it cannot be predicted in the uncoupled model.

CONCLUSIONS

The findings indicate that integrating the plaque growth model with geometric and hemodynamic settings is essential in determining the ultimate shape and dimensions of the carotid plaque.

Impact of Tissue Damage and Hemodynamics on Restenosis Following Percutaneous Transluminal Angioplasty: A Patient-Specific Multiscale Model

Multiscale agent-based modeling frameworks have recently emerged as promising mechanobiological models to capture the interplay between biomechanical forces, cellular behavior, and molecular pathways underlying restenosis following percutaneous transluminal angioplasty (PTA). However, their applications are mainly limited to idealized scenarios. Herein, a multiscale agent-based modeling framework for investigating restenosis following PTA in a patient-specific superficial femoral artery (SFA) is proposed. The framework replicates the 2-month arterial wall remodeling in response to the PTA-induced injury and altered hemodynamics, by combining three modules: (i) the PTA module, consisting in a finite element structural mechanics simulation of PTA, featuring anisotropic hyperelastic material models coupled with a damage formulation for fibrous soft tissue and the element deletion strategy, providing the arterial wall damage and post-intervention configuration, (ii) the hemodynamics module, quantifying the post-intervention hemodynamics through computational fluid dynamics simulations, and (iii) the tissue remodeling module, based on an agent-based model of cellular dynamics. Two scenarios were explored, considering balloon expansion diameters of 5.2 and 6.2 mm. The framework captured PTA-induced arterial tissue lacerations and the post-PTA arterial wall remodeling. This remodeling process involved rapid cellular migration to the PTA-damaged regions, exacerbated cell proliferation and extracellular matrix production, resulting in lumen area reduction up to 1-month follow-up. After this initial reduction, the growth stabilized, due to the resolution of the inflammatory state and changes in hemodynamics. The similarity of the obtained results to clinical observations in treated SFAs suggests the potential of the framework for capturing patient-specific mechanobiological events occurring after PTA intervention.

Guidelines for mechanistic modeling and analysis in cardiovascular research

Computational, or in silico, models are an effective, noninvasive tool for investigating cardiovascular function. These models can be used in the analysis of experimental and clinical data to identify possible mechanisms of (ab)normal cardiovascular physiology. Recent advances in computing power and data management have led to innovative and complex modeling frameworks that simulate cardiovascular function across multiple scales. While commonly used in multiple disciplines, there is a lack of concise guidelines for the implementation of computer models in cardiovascular research. In line with recent calls for more reproducible research, it is imperative that scientists adhere to credible practices when developing and applying computational models to their research. The goal of this manuscript is to provide a consensus document that identifies best practices for in silico computational modeling in cardiovascular research. These guidelines provide the necessary methods for mechanistic model development, model analysis, and formal model calibration using fundamentals from statistics. We outline rigorous practices for computational, mechanistic modeling in cardiovascular research and discuss its synergistic value to experimental and clinical data.

A perfectly imperfect engine: Utilizing the digital twin paradigm in pulmonary hypertension

Pulmonary hypertension (PH) is a severe medical condition with a number of treatment options, the majority of which are introduced without consideration of the underlying mechanisms driving it within an individual and thus a lack of tailored approach to treatment. The one exception is a patient presenting with apparent pulmonary arterial hypertension and shown to have vaso-responsive disease, whose clinical course and prognosis is significantly improved by high dose calcium channel blockers. PH is however characterized by a relative abundance of available data from patient cohorts, ranging from molecular data characterizing gene and protein expression in different tissues to physiological data at the organ level and clinical information. Integrating available data with mechanistic information at the different scales into computational models suggests an approach to a more personalized treatment of the disease using model-based optimization of interventions for individual patients. That is, constructing digital twins of the disease, customized to a patient, promises to be a key technology for personalized medicine, with the aim of optimizing use of existing treatments and developing novel interventions, such as new drugs. This article presents a perspective on this approach in the context of a review of existing computational models for different aspects of the disease, and it lays out a roadmap for a path to realizing it.

Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence

MOTIVATION

Emergent biological dynamics derive from the evolution of lower-level spatial and temporal processes. A long-standing challenge for scientists and engineers is identifying simple low-level rules that give rise to complex higher-level dynamics. High-resolution biological data acquisition enables this identification and has evolved at a rapid pace for both experimental and computational approaches. Simultaneously harnessing the resolution and managing the expense of emerging technologies-e.g. live cell imaging, scRNAseq, agent-based models-requires a deeper understanding of how spatial and temporal axes impact biological systems. Effective emulation is a promising solution to manage the expense of increasingly complex high-resolution computational models. In this research, we focus on the emulation of a tumor microenvironment agent-based model to examine the relationship between spatial and temporal environment features, and emergent tumor properties.

RESULTS

Despite significant feature engineering, we find limited predictive capacity of tumor properties from initial system representations. However, incorporating temporal information derived from intermediate simulation states dramatically improves the predictive performance of machine learning models. We train a deep-learning emulator on intermediate simulation states and observe promising enhancements over emulators trained solely on initial conditions. Our results underscore the importance of incorporating temporal information in the evaluation of spatio-temporal emergent behavior. Nevertheless, the emulators exhibit inconsistent performance, suggesting that the underlying model characterizes unique cell populations dynamics that are not easily replaced.

AVAILABILITY AND IMPLEMENTATION

All source codes for the agent-based model, emulation, and analyses are publicly available at the corresponding DOIs: 10.5281/zenodo.10622155, 10.5281/zenodo.10611675, 10.5281/zenodo.10621244, respectively.

Post-angioplasty remodeling of coronary arteries investigated via a chemo-mechano-biological in silico model

This work presents the application of a chemo-mechano-biological constitutive model of soft tissues for describing tissue inflammatory response to damage in collagen constituents. The material model is implemented into a nonlinear finite element formulation to follow up a coronary standard balloon angioplasty for one year. Numerical results, compared with available in vivo clinical data, show that the model reproduces the temporal dynamics of vessel remodeling associated with subintimal damage. Such dynamics are bimodular, being characterized by an early tissue resorption and lumen enlargement, followed by late tissue growth and vessel constriction. Applicability of the modeling framework in retrospective studies is demonstrated, and future extension towards prospective applications is discussed.

Advances in exercise-induced vascular adaptation: mechanisms, models, and methods

Insufficient physical activity poses a significant risk factor for cardiovascular diseases. Exercise plays a crucial role in influencing the vascular system and is essential for maintaining vascular health. Hemodynamic stimuli generated by exercise, such as shear stress and circumferential stress, directly impact vascular structure and function, resulting in adaptive changes. In clinical settings, incorporating appropriate exercise interventions has become a powerful supplementary approach for treating and rehabilitating various cardiovascular conditions. However, existing models for studying exercise-induced vascular adaptation primarily rely on in vivo animal and in vitro cellular models, each with its inherent limitations. In contrast, human research faces challenges in conducting mechanistic analyses due to ethics issues. Therefore, it is imperative to develop highly biomimetic in vitro/ex vivo vascular models that can replicate exercise stimuli in human systems. Utilizing various vascular assessment techniques is also crucial to comprehensively evaluate the effects of exercise on the vasculature and uncover the molecular mechanisms that promote vascular health. This article reviews the hemodynamic mechanisms that underlie exercise-induced vascular adaptation. It explores the advancements in current vascular models and measurement techniques, while addressing their future development and challenges. The overarching goal is to unravel the molecular mechanisms that drive the positive effects of exercise on the cardiovascular system. By providing a scientific rationale and offering novel perspectives, the aim is to contribute to the formulation of precise cardiovascular rehabilitation exercise prescriptions.

Computational modeling of in-stent restenosis: Pharmacokinetic and pharmacodynamic evaluation

Persistence of the pathology of in-stent restenosis even with the advent of drug-eluting stents warrants the development of highly resolved in silico models. These computational models assist in gaining insights into the transient biochemical and cellular mechanisms involved and thereby optimize the stent implantation parameters. Within this work, an already established fully-coupled Lagrangian finite element framework for modeling the restenotic growth is enhanced with the incorporation of endothelium-mediated effects and pharmacological influences of rapamycin-based drugs embedded in the polymeric layers of the current generation drug-eluting stents. The continuum mechanical description of growth is further justified in the context of thermodynamic consistency. Qualitative inferences are drawn from the model developed herein regarding the efficacy of the level of drug embedment within the struts as well as the release profiles adopted. The framework is then intended to serve as a tool for clinicians to tune the interventional procedures patient-specifically.

Investigating the effect of drug release on in-stent restenosis: A hybrid continuum - agent-based modelling approach

BACKGROUND AND OBJECTIVE

In-stent restenosis (ISR) following percutaneous coronary intervention with drug-eluting stent (DES) implantation remains an unresolved issue, with ISR rates up to 10%. The use of antiproliferative drugs on DESs has significantly reduced ISR. However, a complete knowledge of the mechanobiological processes underlying ISR is still lacking. Multiscale agent-based modelling frameworks, integrating continuum- and agent-based approaches, have recently emerged as promising tools to decipher the mechanobiological events driving ISR at different spatiotemporal scales. However, the integration of sophisticated drug models with an agent-based model (ABM) of ISR has been under-investigated. The aim of the present study was to develop a novel multiscale agent-based modelling framework of ISR following DES implantation.

METHODS

The framework consisted of two bi-directionally coupled modules, namely (i) a drug transport module, simulating drug transport through a continuum-based approach, and (ii) a tissue remodelling module, simulating cellular dynamics through an ABM. Receptor saturation (RS), defined as the fraction of target receptors saturated with drug, is used to mediate cellular activities in the ABM, since RS is widely regarded as a measure of drug efficacy. Three studies were performed to investigate different scenarios in terms of drug mass (DM), drug release profiles (RP), coupling schemes and idealized vs. patient-specific artery geometries.

RESULTS

The studies demonstrated the versatility of the framework and enabled exploration of the sensitivity to different settings, coupling modalities and geometries. As expected, changes in the DM, RP and coupling schemes illustrated a variation in RS over time, in turn affecting the ABM response. For example, combined small DM - fast RP led to similar ISR degrees as high DM - moderate RP (lumen area reduction of ∼13/17% vs. ∼30% without drug). The use of a patient-specific geometry with non-equally distributed struts resulted in a heterogeneous RS map, but did not remarkably impact the ABM response.

CONCLUSION

The application to a patient-specific geometry highlights the potential of the framework to address complex realistic scenarios and lays the foundations for future research, including calibration and validation on patient datasets and the investigation of the effects of different plaque composition on the arterial response to DES.

An agent-based model of cardiac allograft vasculopathy: toward a better understanding of chronic rejection dynamics

Cardiac allograft vasculopathy (CAV) is a coronary artery disease affecting 50% of heart transplant (HTx) recipients, and it is the major cause of graft loss. CAV is driven by the interplay of immunological and non-immunological factors, setting off a cascade of events promoting endothelial damage and vascular dysfunction. The etiology and evolution of tissue pathology are largely unknown, making disease management challenging. So far, in vivo models, mostly mouse-based, have been widely used to study CAV, but they are resource-consuming, pose many ethical issues, and allow limited investigation of time points and important biomechanical measurements. Recently, agent-based models (ABMs) proved to be valid computational tools for deciphering mechanobiological mechanisms driving vascular adaptation processes at the cell/tissue level, augmenting cost-effective in vivo lab-based experiments, at the same time guaranteeing richness in observation time points and low consumption of resources. We hypothesize that integrating ABMs with lab-based experiments can aid in vivo research by overcoming those limitations. Accordingly, this work proposes a bidimensional ABM of CAV in a mouse coronary artery cross-section, simulating the arterial wall response to two distinct stimuli: inflammation and hemodynamic disturbances, the latter considered in terms of low wall shear stress (WSS). These stimuli trigger i) inflammatory cell activation and ii) exacerbated vascular cell activities. Moreover, an extensive analysis was performed to investigate the ABM sensitivity to the driving parameters and inputs and gain insights into the ABM working mechanisms. The ABM was able to effectively replicate a 4-week CAV initiation and progression, characterized by lumen area decrease due to progressive intimal thickening in regions exposed to high inflammation and low WSS. Moreover, the parameter and input sensitivity analysis highlighted that the inflammatory-related events rather than the WSS predominantly drive CAV, corroborating the inflammatory nature of the vasculopathy. The proof-of-concept model proposed herein demonstrated its potential in deepening the pathology knowledge and supporting the in vivo analysis of CAV.

Diversity of cells and signals in the cardiovascular system

This white paper is the outcome of the seventh UC Davis Cardiovascular Research Symposium on Systems Approach to Understanding Cardiovascular Disease and Arrhythmia. This biannual meeting aims to bring together leading experts in subfields of cardiovascular biomedicine to focus on topics of importance to the field. The theme of the 2022 Symposium was 'Cell Diversity in the Cardiovascular System, cell-autonomous and cell-cell signalling'. Experts in the field contributed their experimental and mathematical modelling perspectives and discussed emerging questions, controversies, and challenges in examining cell and signal diversity, co-ordination and interrelationships involved in cardiovascular function. This paper originates from the topics of formal presentations and informal discussions from the Symposium, which aimed to develop a holistic view of how the multiple cell types in the cardiovascular system integrate to influence cardiovascular function, disease progression and therapeutic strategies. The first section describes the major cell types (e.g. cardiomyocytes, vascular smooth muscle and endothelial cells, fibroblasts, neurons, immune cells, etc.) and the signals involved in cardiovascular function. The second section emphasizes the complexity at the subcellular, cellular and system levels in the context of cardiovascular development, ageing and disease. Finally, the third section surveys the technological innovations that allow the interrogation of this diversity and advancing our understanding of the integrated cardiovascular function and dysfunction.

Predicting 1-year in-stent restenosis in superficial femoral arteries through multiscale computational modelling

In-stent restenosis in superficial femoral arteries (SFAs) is a complex, multi-factorial and multiscale vascular adaptation process whose thorough understanding is still lacking. Multiscale computational agent-based modelling has recently emerged as a promising approach to decipher mechanobiological mechanisms driving the arterial response to the endovascular intervention. However, the long-term arterial response has never been investigated with this approach, although being of fundamental relevance. In this context, this study investigates the 1-year post-operative arterial wall remodelling in three patient-specific stented SFA lesions through a fully coupled multiscale agent-based modelling framework. The framework integrates the effects of local haemodynamics and monocyte gene expression data on cellular dynamics through a bi-directional coupling of computational fluid dynamics simulations with an agent-based model of cellular activities. The framework was calibrated on the follow-up data at 1 month and 6 months of one stented SFA lesion and then applied to the other two lesions. The calibrated framework successfully captured (i) the high lumen area reduction occurring within the first post-operative month and (ii) the stabilization of the median lumen area from 1-month to 1-year follow-ups in all the stented lesions, demonstrating the potentialities of the proposed approach for investigating patient-specific short- and long-term responses to endovascular interventions.

In silico model of atherosclerosis with individual patient calibration to enable precision medicine for cardiovascular disease

OBJECTIVE

Guidance for preventing myocardial infarction and ischemic stroke by tailoring treatment for individual patients with atherosclerosis is an unmet need. Such development may be possible with computational modeling. Given the multifactorial biology of atherosclerosis, modeling must be based on complete biological networks that capture protein-protein interactions estimated to drive disease progression. Here, we aimed to develop a clinically relevant scale model of atherosclerosis, calibrate it with individual patient data, and use it to simulate optimized pharmacotherapy for individual patients.

APPROACH AND RESULTS

The study used a uniquely constituted plaque proteomic dataset to create a comprehensive systems biology disease model for simulating individualized responses to pharmacotherapy. Plaque tissue was collected from 18 patients with 6735 proteins at two locations per patient. 113 pathways were identified and included in the systems biology model of endothelial cells, vascular smooth muscle cells, macrophages, lymphocytes, and the integrated intima, altogether spanning 4411 proteins, demonstrating a range of 39-96% plaque instability. After calibrating the systems biology models for individual patients, we simulated intensive lipid-lowering, anti-inflammatory, and anti-diabetic drugs. We also simulated a combination therapy. Drug response was evaluated as the degree of change in plaque stability, where an improvement was defined as a reduction of plaque instability. In patients with initially unstable lesions, simulated responses varied from high (20%, on combination therapy) to marginal improvement, whereas patients with initially stable plaques showed generally less improvement.

CONCLUSION

In this pilot study, proteomics-based system biology modeling was shown to simulate drug response based on atherosclerotic plaque instability with a power of 90%, providing a potential strategy for improved personalized management of patients with cardiovascular disease.

Bioengineering Human Tissues and the Future of Vascular Replacement

Cardiovascular defects, injuries, and degenerative diseases often require surgical intervention and the use of implantable replacement material and conduits. Traditional vascular grafts made of synthetic polymers, animal and cadaveric tissues, or autologous vasculature have been utilized for almost a century with well-characterized outcomes, leaving areas of unmet need for the patients in terms of durability and long-term patency, susceptibility to infection, immunogenicity associated with the risk of rejection, and inflammation and mechanical failure. Research to address these limitations is exploring avenues as diverse as gene therapy, cell therapy, cell reprogramming, and bioengineering of human tissue and replacement organs. Tissue-engineered vascular conduits, either with viable autologous cells or decellularized, are the forefront of technology in cardiovascular reconstruction and offer many benefits over traditional graft materials, particularly in the potential for the implanted material to be adopted and remodeled into host tissue and thus offer safer, more durable performance. This review discusses the key advances and future directions in the field of surgical vascular repair, replacement, and reconstruction, with a focus on the challenges and expected benefits of bioengineering human tissues and blood vessels.

A predictive multiscale model of in-stent restenosis in femoral arteries: linking haemodynamics and gene expression with an agent-based model of cellular dynamics

In-stent restenosis (ISR) is a maladaptive inflammatory-driven response of femoral arteries to percutaneous transluminal angioplasty and stent deployment, leading to lumen re-narrowing as consequence of excessive cellular proliferative and synthetic activities. A thorough understanding of the underlying mechanobiological factors contributing to ISR is still lacking. Computational multiscale models integrating both continuous- and agent-based approaches have been identified as promising tools to capture key aspects of the complex network of events encompassing molecular, cellular and tissue response to the intervention. In this regard, this work presents a multiscale framework integrating the effects of local haemodynamics and monocyte gene expression data on cellular dynamics to simulate ISR mechanobiological processes in a patient-specific model of stented superficial femoral artery. The framework is based on the coupling of computational fluid dynamics simulations (haemodynamics module) with an agent-based model (ABM) of cellular activities (tissue remodelling module). Sensitivity analysis and surrogate modelling combined with genetic algorithm optimization were adopted to explore the model behaviour and calibrate the ABM parameters. The proposed framework successfully described the patient lumen area reduction from baseline to one-month follow-up, demonstrating the potential capabilities of this approach in predicting the short-term arterial response to the endovascular procedure.