Jay D. Humphrey

Physics-informed machine learning (PIML) is emerging as a potentially transformative paradigm for modeling complex biomedical systems by integrating parameterized physical laws with data-driven methods. Here, we review three main classes of PIML frameworks: physics-informed neural networks (PINNs), neural ordinary differential equations (NODEs), and neural operators (NOs), highlighting their growing role in biomedical science and engineering. We begin with PINNs, which embed governing equations into deep learning models and have been successfully applied to biosolid and biofluid mechanics, mechanobiology, and medical imaging, among other areas. We then review NODEs, which offer continuous-time modeling, especially suited to dynamic physiological systems, pharmacokinetics, and cell signaling. Finally, we discuss deep NOs as powerful tools for learning mappings between function spaces, enabling efficient simulations across multiscale and spatially heterogeneous biological domains. Throughout, we emphasize applications where physical interpretability, data scarcity, or system complexity make conventional black-box learning insufficient. We conclude by identifying open challenges and future directions for advancing PIML in biomedical science and engineering, including issues of uncertainty quantification, generalization, and integration of PIML and large language models.

Abstract Vascular smooth muscle cell (VSMC) plasticity is implicated in extracellular matrix (ECM) turnover and arterial failure. The osteochondrocytic phenotypes of synthetic VSMCs are thought to drive glycosaminoglycan (GAG) accumulation and swelling typically seen in connective tissue disease and hypertension. A central question is whether this phenotype switching under non-homeostatic conditions is a cause or effect of those conditions. We implement a cause–effect association between ECM damage, lost cell mechanosensitivity, and cell phenotype modulation using the Constrained Mixture Model, to simulate the evolution of VSMC population over time. We modelled a cylindrical bi-layer of media and adventitia of a mouse common carotid artery and simulated remodelling in response to initially compromised ECM, concurrent with varying degrees of hypertension. In normo- and moderately hypertensive ECM disruption, physiological remodelling restores mechanical homeostasis to cells with slightly altered mechanical properties. Alternatively, severe hypertension yields complete medial degeneration. Complete loss of stored elastic energy is observed, with stiffened arteries yielding characteristically high pulse wave velocities (PWVs). Early intervention recovering hypertensive to normotensive pressure, as well as enhanced adventitial collagen turnover, are shown to prevent medial degeneration. Our model thus offers a tool to better understand the relationship between ECM damage, arterial failure, and hypertension.

In situ vascular tissue engineering aims to create living blood vessel replacements from biodegradable scaffolds. The functionality of these tissue-engineered vascular grafts (TEVGs) has often been limited, with substantial failure rate and outcome variability. Current optimization strategies seem unable to satisfy all requirements for functional TEVGs and the key sources of outcome variability remain unclear. Here, we computationally explored potential sources of TEVG variability and effects of manipulating Notch, a key vascular signaling pathway. We simulated the evolution of a TEVG from a degradable scaffold under varying patient-specific conditions, driven by immuno-mechano-mediated growth and remodeling mechanisms including Notch. Our simulations suggest that differential inflammatory production, scaffold degradation, and scaffold axial pre-stretch are major sources of variability in TEVG outcome. Immobilizing Jagged ligands to the scaffold did not substantially reduce outcome variability in our simulations, but did improve some aspects of TEVG functionality. This intervention may therefore be beneficial in combination with other treatments that compensate for predicted negative effects. Overall, our model may advance future TEVG optimization by incorporating Notch manipulations under various patient-specific conditions.

Postnatal development requires precise coordination across molecular, cellular and tissue scales. However, existing methods often fail to resolve the causal hierarchies that underlie this coordination. Transcriptome-wide studies can identify trait-associated genes but cannot predict how gene expression changes propagate through gene–cell–tissue networks. Here, we present CausaLink, a cross-scale causal mapping framework that reconstructs directed networks by integrating transcriptomic and tissue biomechanical data. We applied it to the postnatal pulmonary arterial tissue remodeling, where a surge in arterial flow triggers conversion of low-flow conduits into high-flow conduits. Longitudinal single-cell and bulk transcriptomic profiling of proximal pulmonary artery (P2-P84) mapped dynamic vascular lineage transitions. Our analysis suggested a bifurcation of shared fibroblast–smooth muscle progenitor in the neonatal phase, initiating downstream shifts at both the cellular and tissue levels. In this context, CausaLink identified Mgl2 + macrophages as central regulators of balanced artery growth. Network-based, multi-trait simulation predicted Mgl2 + macrophages promote lumen expansion while preventing pathological wall thinning or thickening. We validated these predictions in a human induced pluripotent stem cell-derived arterial assembloid populated with MGL high macrophages. In this model, MGL high macrophages drove fibroblast expansion and coordinated tissue growth, as predicted. CausaLink enables systemic prediction across biological scales and offers a data-driven approach for therapeutic design in diseases involving disrupted tissue homeostasis.

Thoracic aortic aneurysms (TAAs) stem from diverse mechanical and mechanobiological disruptions to the aortic wall that can also increase the risk of dissection or rupture. There is increasing evidence that dysfunctions along the aortic mechanotransduction axis, including reduced integrity of elastic fibers and loss of cell-matrix connections, are particularly capable of causing thoracic aortopathy. Because different insults can produce distinct mechanical vulnerabilities, there is a pressing need to identify interacting factors that drive progression. In this work, we employ a finite element framework to generate synthetic TAAs arising from hundreds of heterogeneous insults that span a range of compromised elastic fiber integrity and cellular mechanosensing. From these simulations, we construct localized dilatation and distensibility maps throughout the aortic domain to serve as training data for neural network models to predict the initiating combined insult. Several candidate architectures (Deep Operator Networks, UNets, and Laplace Neural Operators) and input data formats are compared to establish a standard for handling future subject-specific information. We further quantify the predictive capability when networks are trained on geometric (dilatation) information alone, which mimics current clinical guidelines, versus training on both geometric and mechanical (distensibility) information. We show that prediction errors based on dilatation data are significantly higher than those based on dilatation and distensibility across all networks considered, highlighting the benefit of obtaining local distensibility measures in TAA assessment. Additionally, we identify UNet as the best-performing architecture across all training data formats. These findings demonstrate the importance of obtaining full-field measurements of both dilatation and distensibility in the aneurysmal aorta to identify the mechanobiological insults that drive disease progression, which will advance personalized treatment strategies that target the underlying pathologic mechanisms.

Abstract Postnatal pulmonary arteries experience an abrupt surge in flow that demands tightly coordinated remodeling of vascular structure and mechanical compliance. However, the cellular programs orchestrating these multiscale adaptation remain poorly defined, partly due to the absence of analytical tools integrating gene–cell–tissue scales. Here, we present CausaLink, a cross-scale causal mapping framework that predicts how altered gene expression propagates through gene–cell–tissue networks by integrating time-course transcriptomic and tissue morpho-mechanical data. We constructed a single-cell transcriptomic atlas of 11,143 proximal pulmonary artery-derived cells from C57BL/6J mice across five developmental stages (P2, P10, P21, P42, and P84), revealing dynamic lineage transitions and transient mesenchymal population specifying into fibroblasts and smooth muscle cells from P2 to P21. Using this framework, we identified Mgl2 ⁺ macrophages as central regulators promoting lumen expansion while preventing pathological wall thinning or thickening. To validate these predictions, we established a human induced pluripotent stem cell–derived arterial assembloid enriched in MGL high macrophages. In this model, MGL high macrophages supported lumen enlargement while maintaining overall wall thickness and induced formation of an adventitia-like fibroblast layer that recapitulated approximately 80% of the native adventitial fraction with balanced collagen-elastin remodeling. By linking predictive modeling to human organoid validation, this study establishes a cross-scale workflow for tracing how gene programs shape vascular architecture, offering mechanistic insights and a foundation for predictive regenerative medicine.

Thoracic aortic aneurysms (TAAs) arise from diverse mechanical and mechanobiological disruptions to the aortic wall that increase the risk of dissection or rupture. Evidence links TAA development to dysfunctions in the aortic mechanotransduction axis, including loss of elastic fiber integrity and cell-matrix connections. Because distinct insults create different mechanical vulnerabilities, there is a critical need to identify interacting factors that drive progression. Here, we use a finite element framework to generate synthetic TAAs from hundreds of heterogeneous insults spanning varying degrees of elastic fiber damage and impaired mechanosensing. From these simulations, we construct spatial maps of localized dilatation and distensibility to train neural networks that predict the initiating combined insult. We compare several architectures (Deep Operator Networks, UNets, and Laplace Neural Operators) and multiple input data formats to define a standard for future subject-specific modeling. We also quantify predictive performance when networks are trained using only geometric data (dilatation) versus both geometric and mechanical data (dilatation plus distensibility). Across all networks, prediction errors are significantly higher when trained on dilatation alone, underscoring the added value of distensibility information. Among the tested models, UNet consistently provides the highest accuracy across all data formats. These findings highlight the importance of acquiring full-field measurements of both dilatation and distensibility in TAA assessment to reveal the mechanobiological drivers of disease and support the development of personalized treatment strategies.

Dysfunctional mechanosensing via focal adhesions (FAs) significantly contributes to thoracic aortic dissection in the β-aminopropionitrile mouse model by triggering FA kinase activation and subsequent Rho/ROCK and mTOR signaling pathways. The present findings indicate that these signaling pathways play distinct roles in ascending vs descending dissections. Specifically, in the ascending aorta, smooth muscle cell FA kinase-Rho/ROCK signaling acts as a beneficial adaptive mechanism to prevent dissections, whereas mTOR signaling is pathogenic and contributes to dissections.

Thoracic aortic aneurysms predisposing to aortic dissections are rare but potentially deadly conditions that can be inherited in families. Understanding the natural history and genetic causes of thoracic aortic disease (TAD) requires international collaboration. The aim of this project is to provide core data elements that can be assessed directly and summarized efficiently in research records while developing standards to harmonize data across registries and clinical trials. Ten contemporary TAD registries were analyzed to identify significant gaps in the amount and types of data that are needed for accurate, verifiable, and reproducible studies. The set of data elements recommended by this expert panel is intended to serve as a roadmap for future research combining precision clinical data with genetic and/or computational data to uncover new TAD phenotypes and risk factors. Standardization of data-acquisition protocols and definition of minimum requirements for computational modeling will be essential for future collaborative studies.

ABSTRACT There is a pressing need to identify pathologic mechanisms that render a thoracic aortic aneurysm susceptible to continued enlargement, dissection, or rupture, but additional insight can be gleaned by understanding potential compensatory mechanisms that prevent disease progression and thereby stabilize a lesion. Our biomechanical data suggest that the ascending aorta within a common mouse model of Marfan syndrome, Fbn1 C1041G/+ , exhibits progressive disease from 12 weeks to 1 year of age, but near growth arrest from 1 to 2 years of age. Comparison of the biomechanical phenotype, histological characteristics, proteomic signature, and transcriptional profile from 12 weeks to 1 year to 2 years suggests that numerous differentially expressed genes (including downregulated Ilk , Ltbp3, and Rictor ) and associated proteins may contribute to late-term growth arrest. There is also a conspicuous absence of proteins associated with inflammation from 1 to 2 years of age. Although there is a need to understand better the interconnected roles of temporal changes in differential gene expression and protein abundance, reducing mTOR signaling and reducing excessive inflammation appears to merit increased attention in preventing continued aneurysmal expansion in Marfan syndrome.

The geroscience hypothesis suggests that understanding underlying ageing mechanisms will enable us to delay aging and lessen age-related disability and diseases. While hallmarks of ageing list multiple contributing factors, role of mechanics has only been recently recognized and increasingly appreciated. Here, we use mouse models of ageing to investigate changes in mechanics of the proximal pulmonary artery, lung and right ventricle function in ageing. We found an age-related decline in the capacity to store energy and increased circumferential stiffness of the proximal pulmonary artery with age that associated with a reorientation of collagen towards the circumferential direction, decreased exercise ability, and decreased function of the lung and right ventricle. The observed compromised mechanics in proximal pulmonary artery is consistent across multiple mouse models of accelerated ageing. Further, transcriptional changes in proximal pulmonary artery indicate that aging is associated with senescence of perivascular macrophages, adventitial fibroblasts, and medial smooth muscle cells. Older pulmonary arteries increase expression of genes associated with ECM turnover (including genes in the TGFβ pathway) and increased intercellular signaling amongst perivascular macrophages, fibroblasts and smooth muscle cells. Our results provide promising biomarkers of ageing for diagnosis and potential pathways and molecular targets for targeting anti-ageing therapies.

Many congenital conditions and surgical interventions perturb haemodynamics in the proximal pulmonary arteries during postnatal development, thereby altering gene expression and associated changes in vascular structure and function. Among these, pathological conditions include patent ductus arteriosus, pulmonary atresia and stenosis and hypoxemia-induced pulmonary hypertension while surgical interventions include the placement of a Blalock-Thomas-Taussig shunt and the Glenn and Fontan procedures. Despite the significant morbidity associated with these diverse conditions, little attention has been directed to understanding the natural postnatal development of pulmonary arteries from both biological and mechanical perspectives. Without such information, we cannot truly understand the phenotype of the affected pulmonary artery, which is fundamental to improving diagnosis, treatment and prognosis. In this paper, we present novel data from wild-type mice that document normal postnatal changes in select gene expression, wall composition and biomechanical properties of proximal pulmonary arteries. These findings enabled the establishment of a novel, data-informed computational model of pulmonary artery development capable of simulating outcomes in response to perturbations in pulmonary artery haemodynamics.

Abstract Introduction: We hypothesized that that progressive vascular maladaptations to reduced blood oxygenation and subsequent altered hemodynamics drive progressive deterioration of end-organ function via insidious positive feedback that compromises proximal pulmonary artery (PPA) storage of systolic strain energy for use during diastole to optimize blood flow to the lungs. Methods: We tested this hypothesis by biomechanically phenotyping the cardiopulmonary system (right heart, pulmonary artery, and lungs) as a function of duration of hypoxic exposure (FiO2 10%) in neonatal (0 to 3 weeks), juvenile (3 to 8 weeks) and adult (8 to 13 weeks) female mice. Functional and mechanical properties were quantified using exercise testing, echocardiography, pulmonary function testing, and a custom computer-controlled biaxial testing device. We used single cell RNA sequencing to explore cellular-molecular mechanisms and changes in intercellular communication associated with hypoxia. Results: We found that chronic hypoxia decreases daily running distance in adult mice >5,000 m/day (p < 0.05). Juvenile mice displayed increased resilience and daily running distance increased >2,000 m/day (p< 0.05). Regardless of age of onset of hypoxic exposure, right ventricular systolic function (s’ and PAT:PET) decreased > 25% (p < 0.05) and changes to lung parenchyma occurred (>20% increase in alveolar chord length, static compliance, and lung volumes, p< 0.05). These changes associated with and were preceded by a rapid two-fold increase in PPA stiffening (p < 0.05), suggesting that PPA stiffening plays a critical and potentially causative role in cardiopulmonary impairment. The orientation of collagen fibers surrounding the main PPA after hypoxia, regardless of age of onset, re-oriented ∼10 degrees toward the circumferential direction. The changes in gene expression and intercellular signaling between smooth muscle cells, fibroblasts, and macrophages within the arterial wall suggested several hypotheses for this phenomenon, including phenotypic shift of SMCs from contractile to secretory phenotype and altered matrix degradation and deposition by resident cells within the arterial wall. Chronic hypoxia results in persistent impairment of the ability of the pulmonary vasculature to store elastic energy (p< 0.05), thus, cardiopulmonary hemodynamics remain impaired after periods of prolonged hypoxia. We tested several interventions to prevent persistent maladaptations (normoxic recovery +/- adjuvant exercise, selective macrophage knockouts, and prophylactic interventions during hypoxic exposure (mTOR/AMPK inhibition and forced exercise)). Interventions help to prevent some but not all of the maladaptive changes due to chronic hypoxia. Conclusion: Refined earlier therapies are necessary to prevent positive feedback loops (cell-cell and organ-organ) that occur during the hypoxemic period.

This study identifies Tenascin-C (TNC) as a key regulator of arteriovenous fistula (AVF) patency. TNC is spatially and temporally regulated, driving neointimal hyperplasia and thrombosis by promoting a prothrombotic, inflammatory microenvironment. In Tnc −/− mice, reduced TNC expression increased thrombomodulin and anti-inflammatory macrophage polarization but impaired wall thickening and AVF patency. These findings link sustained TNC expression to AVF failure and suggest that targeting TNC pathways could enhance AVF outcomes in patients requiring hemodialysis.

Tissue engineered vascular grafts can offer long-term benefits in matching the geometry, properties, and function of native blood vessels. Yet, choosing appropriate design parameters for biodegradeable scaffolds such that they evolve into neovessels with favorable characteristics is challenging with iterative experimental testing alone. Herein, we present an in silico framework for constrained optimization of scaffold microstructure, mechanical behavior, and degradation kinetics. Our approach combines a biomechanical model of growth and remodeling informed by large animal experiments with numerical optimization to identify design parameters that limit clinically relevant failure modes, including stenosis and dilatation, and improve functional matching to native vessel compliance. Towards this end, constraints on geometry were introduced as a straightforward way to prevent adverse remodeling outcomes and shown to be useful in shaping desired outcomes in graft remodeling. Our simulations of long-term graft evolution suggest the need for a modest initial immune response to ensure graded load transfer from polymer to neotissue and to prevent extreme changes in diameter. Optimized designs showed less sensitivity to simulated variability in their design parameters, which could limit subject-to-subject variability. Together, these findings highlight the utility of computational modeling in identifying candidate designs for improved outcomes in tissue engineered vascular grafts - elimination of stenosis/aneurysm, better compliance matching, and consistent changes in behavior over time.

Tissue-engineered vascular grafts (TEVGs) offer promising advancements in treating congenital heart disease by enabling the creation of autologous tissue for complex cardiac repairs. Our approach involves implanting biodegradable scaffolds seeded with autologous cells that remodel into functional neovessels. To understand better the factors guiding neovessel formation, we evaluated 50 ovine thoracic TEVGs using angiography at 1 and 6 weeks postimplantation. Nondimensionalization accounted for anatomical differences between animals and identified hemodynamics and surgical sizing as potential driving factors. Regression analysis revealed that narrowing at the inflow anastomosis and graft oversizing correlated significantly with stenosis development. Computational fluid dynamics showed that these factors influenced wall shear stress and flow patterns, contributing to neovessel narrowing. Comparisons with clinical trial data from Fontan conduits supported these findings, emphasizing that matching graft size to the native inflow vessel can reduce stenosis and enhance TEVG performance.

For single ventricle congenital heart patients, Fontan surgery is the final stage in a series of palliative procedures, bypassing the heart to enable passive flow of de-oxygenated blood from the inferior vena cava (IVC) to the pulmonary arteries. This circulation leads to severely elevated central venous pressure, diminished cardiac output, and thus numerous sequelae and premature mortality. To address these issues, we propose a bioprinted pulsatile conduit to provide a secondary power source for the Fontan circulation. A multiphysics computational framework was developed to predict conduit performance and to guide design prior to printing. Physics components included electrophysiology, cardiomyocyte contractility, and fluid-structure interaction coupled to a closed-loop lumped parameter network representing Fontan physiology. A range of myocardial contractility was considered and simulated. The initial conduit design with adult ventricular cardiomyocyte contractility values coupled to a Purkinje network demonstrated potential to reduce liver (IVC) pressure from 16.4 to 9.3 mmHg and increase cardiac output by 29%. After systematically assessing the impacts of contraction duration, fiber direction, and valve placement on conduit performance, we identified a favorable design that successfully reduces liver pressure to 7.3 mmHg and increases cardiac output by 38%, almost normalizing adverse hemodynamics in the lower venous circulation. Valves at the input and output of the conduit are essential to achieve these satisfactory results; without valves, performance is compromised. However, a potential drawback of the design is the elevation of superior vena cava (SVC) pressure, which varies linearly with liver pressure reduction.

There is a recent dramatic increase in research on thoracic aortic diseases that includes aneurysms, dissections, and rupture. Experimental studies predominantly use mice in which aortopathy is induced by chemical interventions, genetic manipulations, or both. Many parameters should be deliberated in experimental design in concert with multiple considerations when providing dimensional data and characterization of aortic tissues. The purpose of this review is to provide recommendations on guidance in (1) the selection of a mouse model and experimental conditions for the study, (2) parameters for standardizing detection and measurements of aortic diseases, (3) meaningful interpretation of characteristics of diseased aortic tissue, and (4) reporting standards that include rigor and transparency.

Hypoxemia impairs cardiopulmonary function. We investigated pulmonary artery remodeling in mice exposed to chronic hypoxia for up to five weeks and quantified associated changes in cardiac and lung function, without or with subsequent normoxic recovery in the absence or presence of exercise or pharmacological intervention. Hypoxia-induced stiffening of the proximal pulmonary artery stemmed primarily from remodeling of the adventitial collagen, which resulted in part from altered inter-cellular signaling associated with phenotypic changes in the mural smooth muscle cells and macrophages. Such stiffening appeared to precede and associate with both right ventricular and lung dysfunction, with changes emerging to similar degrees regardless of the age of onset of hypoxia during postnatal development. Key homeostatic target values of the wall mechanics were recovered by the pulmonary arteries with normoxic recovery while other values recovered only partially. Overall cardiopulmonary dysfunction due to hypoxia was similarly only partially reversible. Remodeling of the cardiopulmonary system due to hypoxia is a complex, multi-scale process that involves maladaptations of the proximal pulmonary artery.

Hypertension and transient increases in blood pressure from extreme exertion are risk factors for aortic dissection in patients with age-related vascular degeneration or inherited connective tissue disorders. Yet, a common experimental model of angiotensin II-induced aortopathy in mice appears independent of high blood pressure, as lesions do not occur in response to an alternative vasoconstrictor, norepinephrine, and are not prevented by cotreatment with a vasodilator, hydralazine. We investigated vasoconstrictor administration to adult mice following 1 week of disrupted TGF-β signaling in smooth muscle cells (SMCs). Norepinephrine increased blood pressure and induced aortic dissection by 7 days and even within 30 minutes (as did angiotensin II) that was prevented by hydralazine. Initial medial injury manifested as blood extravasation among SMCs and fibrillar matrix, progressive delamination from accumulation of blood, and stretched or ruptured SMCs with persistent attachments to elastic fibers. Altered regulatory contractile molecule expression was not of pathological importance. Rather, reduced synthesis of extracellular matrix yielded a vulnerable aortic phenotype by decreasing medial collagen, most dynamically basement membrane-associated multiplexin collagen, and impairing cell-matrix adhesion. We conclude that transient and sustained increases in blood pressure can cause dissection in aortas rendered vulnerable by inhibition of TGF-β-driven extracellular matrix production by SMCs.

Multiple cell types interact within the aortic wall to control development, homeostasis, and adaptation as well as to drive disease progression. Given the complexity of these interactions and their manifestations at the tissue level, there is a pressing need for a new class of computational models that integrate data across scales. We meld logic-based cell signaling models of vascular smooth muscle cells, adventitial fibroblasts, and macrophages and couple this multi-cell model with a tissue level-constrained mixture model of aortic growth and remodeling. The coupled multi-scale model is parameterized using data from the literature and then specialized for the case of angiotensin II-induced hypertensive remodeling of the descending thoracic aorta in wild-type mice. We contrast important contributions of chemo- and mechano-stimulation of cell responses and identify critical roles of recruited macrophages in driving the non-homeostatic thickening of the adventitial layer that reduces biaxial wall stress below setpoint values. We show the utility of a multi-scale, multi-cell model in delineating effects of different chemo-mechanical stimuli in aortic remodeling in hypertension.

The human body consists of many different soft biological tissues that exhibit diverse microstructures and functions and experience diverse loading conditions. Yet, under many conditions, the mechanical behaviour of these tissues can be described well with similar nonlinearly elastic or inelastic constitutive relations, both in health and some diseases. Such constitutive relations are essential for performing nonlinear stress analyses, which in turn are critical for understanding physiology, pathophysiology and even clinical interventions, including surgery. Indeed, most cells within load-bearing soft tissues are highly sensitive to their local mechanical environment, which can typically be quantified using methods of continuum mechanics only after the constitutive relations are determined from appropriate data, often multi-axial. In this review, we discuss some of the many experimental findings of the structure and the mechanical response, as well as constitutive formulations for 10 representative soft tissues or organs, and present basic concepts of mechanobiology to support continuum biomechanical studies. We conclude by encouraging similar research along these lines, but also the need for models that can describe and predict evolving tissue properties under many conditions, ranging from normal development to disease progression and wound healing. An important foundation for biomechanics and mechanobiology now exists and methods for collecting detailed multi-scale data continue to progress. There is, thus, considerable opportunity for continued advancement of mechanobiology and biomechanics.

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Constrained mixture models have successfully simulated many cases of growth and remodeling in soft biological tissues. So far, extensions of these models have been proposed to include either intracellular signaling or chemo-mechanical coupling on the organ-scale. However, no version of constrained mixture models currently exists that includes both aspects. Here, we propose such a version that resolves cellular signal processing by a set of logic-gated ordinary differential equations and captures chemo-mechanical interactions between cells by coupling a reaction-diffusion equation with the equations of nonlinear continuum mechanics. To demonstrate the potential of the model, we present 2 case studies within vascular solid mechanics: (i) the influence of angiotensin II on aortic growth and remodeling and (ii) the effect of communication between endothelial and intramural arterial cells via nitric oxide and endothelin-1.