APL Bioengineering

Colorectal cancer (CRC) is the second most common cause of cancer-related mortality. At the molecular level, CRC is associated with genetic mutations and epigenetic modifications that dysregulate various signaling networks. From the biophysical viewpoint, invasive and metastatic cell migration need to be empowered by mechanical forces. In this study, we analyze the dynamic deformation of patient-derived CRC organoids in Fourier space and demonstrate how organoids with protooncogene BRAF mutation exhibit deformation phenotypes at an early stage. The organoids with BRAFmut have significantly lower elasticity and higher viscosity than those with BRAFWT, which mathematically indicated as the weakening of cell-cell adhesion. Immunohistochemical images, qRT-PCR, and TCGA data analysis confirm the downregulation of E-cadherin (CDH1) in BRAFmut organoids as well as in BRAFmut CRC, suggesting that the decrease in cell-cell adhesion in BRAFmut CRC facilitates invasive and metastatic migration. Notably, the recovery of CDH1 expression by pharmacological inhibition of DNA methylation can quantitatively be detected as the change in mechanical properties, suggesting that the complementary combination of dynamic phenotyping, mathematical modelling, and molecular-level analyses has a potential to unravel the mechanistic causality of the critical gene mutation and CRCs prognosis and the response to therapeutic interventions.

Advanced in vitro systems such as organoids and microfluidic organ-on-a-chip platforms enable physiologically richer experimentation, but their complexity creates large parameter spaces and makes it difficult to disentangle the mechanistic roles of transport, mechanics, and extracellular microstructure. Agent-based modelling provides a natural computational counterpart to these systems by representing heterogeneous cells as discrete entities coupled through local rules and environmental fields. However, realistic microenvironment models often remain limited by scalability, simplified extracellular matrix representations, and the practical difficulty of calibrating large numbers of parameters. Here we present Cellfoundry, a computational framework built on a FLAMEGPU2-based modelling template for simulating complex cellular microenvironments. The framework integrates multiple interacting agent populations, including cells, fibrous networks, and focal adhesions mediating attachment dynamics and traction-force transmission. It combines mechanically resolved cell-cell and cell-matrix interactions with multi-species diffusion fields that propagate biochemical signals through the extracellular environment and regulate processes such as metabolism, migration, and cell-cycle progression. Cellfoundry also supports customizable behaviours across multiple cell types, enabling the study of heterogeneous multicellular systems within a unified computational setting. To support reproducible model development and calibration, the framework includes a fibre-network generation module, automated performance benchmarking workflows, post-processing and reporting utilities, and an Optuna-based Bayesian optimization pipeline with configurable single- and multi-objective targets. Two showcase examples illustrate these capabilities: a migration assay calibrated against fibroblast motility descriptors and a multi-objective organoid growth scenario reproducing target population composition and expansion dynamics and over time. Together, these examples demonstrate how Cellfoundry can be used to build, calibrate, and extend mechanistically interpretable models of coupled biochemical and mechanical dynamics in advanced in vitro systems. HighlightsO_LIHighly versatile, GPU-accelerated agent-based framework for cellular microenvironments C_LIO_LIExplicit fibrous ECM networks with dynamic remodelling and focal adhesion agents C_LIO_LICoupled mechanics and multi-species diffusion regulate cell behaviour in a highly customizable environment C_LIO_LIModular architecture with automated benchmarking and Bayesian parameter optimization C_LI

IntroductionCancer progression is driven not only by tumor cells but also by interactions between the extracellular matrix (ECM), stromal cells, and immune cells within the tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs) are major drivers of ECM remodeling, assembling ECM with aberrant organization. Extra domain A fibronectin (EDA-FN), a cellular FN containing an extra type III domain, is upregulated in the TME. EDA-FN regulates cellular behavior and has been associated with poor patient prognosis. Macrophages are among the most abundant immune cells within the TME, where they contribute to TME remodeling and inflammation to promote cancer cell invasion and metastasis. However, how tumor-associated matrix-specific cues regulate macrophage behavior remains largely understudied. PurposeHere, we developed a fibroblast-derived matrix platform that captures the structural imprint of tumor-associated EDA-enriched matrices and investigated how matrix-specific cues regulate macrophage behavior in the absence of ongoing soluble factor cues. MethodHuman mammary fibroblasts (HMFs) preconditioned in incubated low-serum media (lNC, or control) and MDA-MB231 metastatic breast cancer cell-conditioned media (mTCM) were cultured on polyacrylamide gels of 2 kPa and 20 kPa, respectively, followed by decellularization. Matrix organization, including fiber alignment, width, and intrafibrillar spacing, was quantified from confocal images. Decellularized EDA-FN-enriched matrices were subsequently reseeded with macrophages to assess macrophage morphology, phenotype, and matrix interactions. ResultsThe combined effects of tumor-derived soluble factors and pathological stiffness induced a CAF-like phenotype in HMFs, accompanied by cytoskeletal reorganization and microarchitectural alterations of EDA-FN-enriched matrices. Tumor-associated matrices exhibited increased alignment, narrower fiber width, and enlarged intrafibrillar spacing compared to control matrices. These aberrant, tumor-associated matrix-derived features were associated with altered macrophage behavior, including heterogeneous morphology, enhanced localized EDA-FN matrix loss beneath the cell body, and a hybrid phenotype with a shift toward a CD206-dominant profile. ConclusionsThese findings demonstrate the feasibility of obtaining EDA-FN-enriched matrices to isolate matrix-specific cues for investigating macrophage-ECM interactions. Furthermore, this platform can be leveraged to identify matrix-targeting therapeutic approaches for modulating macrophage function within the TME.

Scaling up microvessel culture systems is essential for producing vascularized clinically relevant tissues, yet current platforms offer little guidance on how to preserve flow conditions during scale-up. Here, we present a computational-experimental framework using computational fluid dynamics (CFD) to guide the design and scaling of microvessel bioreactors. Interstitial flow distributions were pre-dicted in two perfusion-based platforms-a permeable insert and a rhomboidal microfluidic chamber-across multiple scaling factors and hydrostatic pressures. CFD identified IF ranges conducive to vascu-logenesis and quantified how geometry and pressure modulate flow uniformity. Scaled-up bioreactors generated microvessel networks with consistent morphology and connectivity over a 30-fold increase in culture volume, confirming that maintaining equivalent IF ensures reproducible outcomes. The permeable insert platform maintained uniform IF across scales, while the rhomboidal chamber produced spatially varying IF resulting in heterogeneous but physiologically relevant networks. These findings establish CFD as a predictive tool for rationally scaling perfusion bioreactors, enabling microvessel production at clinically relevant scales with controllable morphology. Significance StatementScaling up microvessel bioreactors is critical for engineering large pre-vascularized tissues. However, larger scales may disrupt flow conditions that drive vessel formation. This study demonstrates that computational fluid dynamics (CFD) can predict interstitial flow and guide the rational scale-up while preserving the vasculogenic microenvironment. Experiments across 30+-fold size increase confirmed that matching inter-stitial flow results in morphologically identical microvessel networks. By linking simulation-based design with experimental validation, this work establishes CFD as design tool for scalable perfusion bioreactors for production of microvessel networks at clinically relevant scales.

Microvasculature networks mediate nutrient delivery, waste removal, and drug distribution, yet current microfluidic devices fail to capture biological complexity. Here, we introduce an integrative framework to automate generation of bio-informed microvasculature models unifying in silico and in vitro applications. Our approach leverages a new stochastic growth algorithm governed by fundamental angiogenic principles to generate closed-circuit, fabrication-ready architectures with physiological relevance. We introduce an inverse design strategy that provides a principled mechanism to assign vessel characteristics that satisfy physiological scaling laws. We then present electrical network dynamics, a new algorithm that characterizes network behaviors 100-10,000X faster than CFD, while preserving quantitative predictions. We demonstrate models are fully interchangeable between experimental domains through systematic investigation of vascular topology influence of flow, transport, and cellular behavior. Our platform closes a long-standing gap and provides a generalizable foundation for studying microvascular function in health and disease.

It has been broadly recognized that the crosstalk between cells and their extracellular matrix (ECM) is crucial for the proper function of biological tissues. Relatively recently the role of ECM came in focus in the context of neuronal development and regeneration, where the effects of the ECM mechanics on the migration of neurons and neurite growth are still incompletely understood. Here we present an in silico twin framework for neurite growth focusing on its biophysical interactions with the ECM. This coarsegrained model accounts for viscoelastic liquid- and solid-like ECMs and neurite growth by ECM-mediated traction forces. Resulting growth trajectories can be rationalized based on the theory of random walks and polymer physics. To critically assess models predictive power, we performed experiments on neurites of hippocampal rat neurons growing in 3D collagen gels and observed a more persistent axon outgrowth in denser matricies. The model fully recapitulated the effect, thereby underpinning the central role of mechanical interactions with ECM as guiding principle of axonal growth. We argue that a combination our model with optical microscopy may provide an is silico twin helping to disentangle the contributions of "passive" physics from more complex effects of chemical queues or an apparent mechanosensing.

Maintaining a confluent, antithrombotic endothelium on cardiovascular biomaterial surfaces remains a major barrier to long-term hemocompatibility, as endothelial cells (ECs) rapidly denude under supraphysiological shear in prosthetic devices. Here, we hypothesized that mesoscale surface geometry ([~]100-200 {micro}m) could reorganize near-wall hemodynamics, preserving endothelial coverage and function under extreme shear. Engineered microtrenches were introduced onto an implant biomaterial to generate spatially defined shear environments. Under supraphysiological near-wall shear ([~]250 dyn/cm{superscript 2}), microtrenched geometries created attenuated shear and vorticity gradients. Endothelial monolayers were sustained in these flow domains for 120 hours, whereas flat controls rapidly denuded. Endothelial retention in 22.5{degrees} angled trenches increased dramatically, from an EC of 33 to 101 dyn/cm{superscript 2}. 45{degrees} angled trenches further increased endothelial shear resistance to an EC of 207 dyn/cm{superscript 2}. Endothelial monolayers demonstrated collective mechano-adaptation to ultra-high shear through VE-cadherin junction thickening and coordinated cytoskeletal and nuclear alignment. Mechanoadapted monolayers exhibited increased eNOS expression correlated with local shear and elevated nitrite production (45{degrees}: 50.4 {+/-} 6.1 {micro}M; 22.5{degrees}: 35.7 {+/-} 3.3 {micro}M; 0{degrees}: 28.4 {+/-} 6.8 {micro}M). In contrast, interfaces with abrupt shear transitions or elevated rotational flow exhibited reduced coverage, junctional thinning, and re-emergence of VCAM-1 and PAI-1, indicating inflammatory and pro-thrombotic activation. Structural, functional, and inflammatory readouts exhibited peak responses within a shared shear-vorticity regime. Multivariate regression identified shear-vorticity coupling as the dominant predictor of endothelial persistence, with optima clustering within a mechanical range ({approx}0.8-2.9 x 10 dyn{middle dot}cm-{superscript 2}{middle dot}s-{superscript 1}). These findings establish geometry-driven modulation of near-wall flow as a predictive, material-agnostic strategy for endothelialization and vasoprotection of high-shear cardiovascular implants.

Caveolae, flask-shaped membrane invaginations highly enriched in endothelial cells, play a central role in buffering membrane tension, yet the principles governing their spatial organization remain elusive. This investigation sought to generate the most comprehensive and systematic analysis of blood vessel caveolar spatial organization. To do so, our group leveraged micropatterning technologies to impose precise biophysical constraints on endothelial cell geometry to probe how caveolae are organized under defined tensional and polarity environments. These experiments were integrated with a high-throughput spatial cell mapping computational pipeline for analyzing thousands of caveolae, providing an extremely high-fidelity analysis. Our results provide a governing framework of how total cellular caveolae are spatially organized during random and directional migration, non-motile polarized, nascent and stable monolayers with differing confinement levels as well as in angiogenic vasculature in vivo. Broadly, our results demonstrated caveolae preferentially organized in the rear of migrating and polarized endothelial cells. In differing monolayer configurations, caveolae default to a peri-junctional spatial organization. Lastly, in mouse retinal blood vessels caveolae are most prominent in the vascular front due to their responsiveness to vascular endothelial growth factor signaling. Overall, these results strongly suggest that caveolae cellular arrangement and number are highly predictive of vascular stability and remodeling states.

Precision-cut lung slices (PCLS) retain the native cells and extracellular matrix that contribute to the structural and functional integrity of lung tissue. This technique enables the study of cell-matrix interactions and is particularly useful for pre-clinical pharmacological studies. More specifically, PCLS are widely used to model the complex pathophysiology of pulmonary fibrosis, an uncurable and progressive interstitial lung disease. Current ex vivo pulmonary fibrosis models expose PCLS to pro-fibrotic biochemical cues over a short timeframe (hours to days) and quickly collect samples for analysis due to viability concerns. This condensed timeline is a limitation to understanding chronic disease mechanisms. To extend the utility of ex vivo pulmonary fibrosis models, PCLS were embedded in engineered hydrogels and exposed to pro-fibrotic biochemical and biophysical cues. Hydrogel-embedded PCLS maintained greater than 80% total cell viability over 3 weeks in culture. Gene expression patterns in samples exposed to pro-fibrotic cues matched trends measured in human fibrotic lung tissue. Finally, treatment with Nintedanib, a Food and Drug Administration approved pulmonary fibrosis drug, moderately reduced fibroblast activation and influenced epithelial cell differentiation. Collectively, these results show that hydrogel-embedded PCLS models of pulmonary fibrosis extend our ability to study fibrotic processes ex vivo and, when applied to human tissues, present a new approach methodology for studying lung disease and treatment.

Patient-derived tumour spheroids are increasingly used as engineered three-dimensional tissue models for studying tumour growth, nutrient limitation, and therapeutic response. However, extracting quantitative, mechanistically interpretable information from longitudinal imaging data remains challenging. Here, we present a three-dimensional phase-field framework for modelling patient-derived tumour spheroids as continuum, self-organising tissues. The model captures the coupled evolution of viable and necrotic cell fractions through nutrient-limited growth, death, and mechanically and thermodynamically mediated motion, using seven biologically interpretable effective parameters. Key experimental observables emerge naturally from nutrient-growth coupling, without imposing explicit species interfaces or quiescent layers. The framework was quantitatively calibrated against longitudinal imaging data from melanoma spheroids across two cell lines and three initial seeding densities. Across all conditions, simulations reproduced the temporal evolution of all measured observables with low relative error ({approx} 3{sigma} of experimental data), and direct comparison with an established Greenspan-type ODE model demonstrated comparable or improved predictive accuracy. Parameter identifiability analysis revealed weak individual parameter constraints, yet model predictions remained robust, a profile consistent with biological models. We demonstrate that a general PDE-based growth framework can match or outperform a dedicated spheroid model while remaining fully biologically interpretable. Beyond predictive accuracy, the phase-field formulation naturally resolves internal mechanical structure, providing access to quantities that are not directly experimentally observable. These results establish that mechanistically grounded continuum models can be quantitatively calibrated to routine spheroid imaging data, offering a foundation for integrating spatial and mechanical information into the interpretation of organoid-based assays. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=77 SRC="FIGDIR/small/717345v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@12eddb2org.highwire.dtl.DTLVardef@1dce430org.highwire.dtl.DTLVardef@1091fc2org.highwire.dtl.DTLVardef@4055e_HPS_FORMAT_FIGEXP M_FIG C_FIG

The tumor microenvironment (TME) is a complex ecosystem composed of tumor cells, cancer-associated fibroblasts (CAFs), immune suppressive cells, and the extracellular matrix (ECM), playing a crucial role in tumor development and CAR-T cell therapy efficacy. CAR-T therapy has shown promise in hematological malignancies but faces challenges in solid tumors due to the TMEs ability to suppress CAR-T cell infiltration, proliferation, and cytotoxicity. Traditional drug evaluation models, such as 2D cell cultures and animal models, have significant limitations due to oversimplification of the in vivo environment or physiological differences between species. Organoid models offer a more biomimetic approach but often fail to fully recapitulate the TMEs complexity and heterogeneity. Our research developed a tumor organoid and CAF co-culture model using the IBAC co-culture chip, demonstrating that CAFs significantly impact CAR-T cell therapy efficacy by forming physical (e.g., fibronectin) and chemical (e.g., IL-10) barriers that prevent CAR-T cell infiltration and cytotoxicity. This model provides a high-biomimetic platform for investigating the TMEs effects on CAR-T therapy and highlights the importance of incorporating a comprehensive stromal component into in vitro models to enhance their predictive power for cancer treatment.

The development of large-scale, three-dimensional human tissues is crucial for various applications in therapeutic tissue engineering, disease modeling, and drug testing. However, due to the diffusion limit of oxygen, the lack of functional vascular networks is a significant limitation in maintaining these engineered tissues in the laboratory. To address this challenge, we present a systematic, model-based design process for artificial supply networks that can ensure a sufficient supply of oxygen and nutrients to engineered human tissue. Our approach combines mathematical models of fluid dynamics, cell metabolism, and network properties to identify key parameters influencing the supply performance. We demonstrate the applicability and possibilities of this design process by simulating different network structures, including cuboid and rhombic do-decahedral honeycombs, under various conditions. Our results show that the structure of the artificial supply network, oxygen concentration, and solute flow within the network strongly influence cellular metabolic activity and viability. We also examine the effects of non-uniform cell density, channel blockage, and long channel length on the oxygen distribution inside the cell-containing tissue compartment. Our findings highlight the importance of considering these factors in the design of artificial supply networks for large-scale engineered human tissues. This study provides a promising approach for quickly exploring the vast design space of possible network structures under different conditions for desired cell and tissue states, ultimately contributing to the development of more efficient and effective tissue engineering strategies.

Glioblastoma (GBM) lethality arises from aggressive invasion and diffuse infiltration of brain tissue. Conventional GBM preclinical models often fail to predict clinical therapeutic efficacy because they do not recapitulate the pathological extracellular matrix (ECM) cues that drive tumor invasion. Here, we present an ECM mimetic 3D platform using a fibrin scaffold to recapitulate the hemorrhagic, pro-thrombotic tumor microenvironment characteristic of high-grade gliomas. This fibrin scaffold induces a pro-invasive phenotype in GBM spheroids by upregulating proliferation/cell cycle- (MYC, FOXOM1, CCND1) and invasion-associated-(CTSS, FOXM1, CCND1) genes. Traditional cell morphology quantification methods (e.g., circularity) distil complex shapes into singular metrics and cannot capture the nuances of invasion. To address this limitation, we have applied a deep-learning segmentation pipeline (MARS-Net) and high-content morphodynamic descriptors. By using the Preserving Heterogeneity (PHet) algorithm, the 3D platform accurately classifies invasiveness levels and captures the invasion-inhibitory effects of potential repurposable drug candidates. We demonstrate that our model can predict a spheroids long-term invasive fate with high accuracy using only partial image sets from early time-points, rather than the complete time-course images. Our work presents an in vivo-like, scalable 3D platform integrated with a quantitative high-throughput pipeline to elucidate GBM invasion mechanisms and to evaluate anti-invasive compounds.

It is increasingly necessary to both study biology in 3D and obtain quantitative measurements. Not all 3D-reconstructions are created equal, particularly when using the anatomical model as a basis for force calculations, i.e. computational modeling. Here, we compare 3D anatomical reconstructions from two emerging imaging modalities: 4D ultrasound (4DUS) and light sheet fluorescent microscopy (LSFM) against our previous nano-computed tomography (nanoCT) cohort data, using the tortuous highly intricate pharyngeal arch artery system of the chick embryo as a test bed. We highlight modality-specific morphological image acquisition discrepancies and their influence on subsequent computational fluid dynamics results. Overall, LSFM accurately captured quantitative volumetric measurements of small rapidly-changing vascular morphologies while 4DUS systematically inflated small tortuous vessels. Differences in image-based morphology changes led to significant changes in computationally-obtained force magnitudes and flow patterns linked to vessel angle and tortuosity. This validates LSFM as a comparative preclinical vascular quantitative imaging tool and suggests that 4DUS needs extensive 3D anatomical validation for non cardiac chamber vessels.

Co-culturing cells with mismatched densities, where one cell type adheres to surfaces while the other floats, represents a fundamental challenge in cell biology. This is particularly evident in studying macrophage-adipocyte interactions, where macrophages must engage and clear lipid-rich apoptotic adipocytes, a process critical to understanding chronic inflammation in obesity and metabolic disease. The density disparity between macrophages, which sink and adhere to culture surfaces, and adipocytes, which float due to their lipid content, has prevented conventional co-culture approaches from achieving sustained cell-cell contact. To address this challenge, we developed a microfluidic system that confines adipocytes and lipid droplets in close proximity to macrophages. This platform features recessed micro-traps within the upper surface of a microfluidic chamber that trap buoyant objects while allowing media exchange and delivery of reagents for live-cell and immunofluorescence imaging. Time lapse imaging revealed that the dynamic process of macrophages-dead corpse interactions, showing that individual macrophages cannot engulf entire corpses but instead mechanically deform them. Furthermore, the platform successfully recapitulates the formation of Crown-Like Structures (CLS), clusters of macrophages surrounding dead adipocytes that are hallmarks of adipose tissue inflammation. Long-term culture revealed that CLS effectively clear lipids compared to partial macrophage engagement, providing mechanistic insights that were previously unattainable with standard histological approaches. Beyond the macrophage-lipid interaction, this platform has potential for studying interactions between adherent cells and buoyant targets, such as microplastics, opening new avenues for research where density mismatch poses a major barrier.

The vascular system exhibits complex, non-planar geometries that become further distorted during pathological remodeling, including arterial tortuosity and aneurysms. Although hemodynamic shear stress is a well-established regulator of vascular function, the direct effects of curvature as an intrinsic geometric cue remain poorly defined. This is largely because existing in vitro models are static and fail to capture the dynamic changes that accompany disease progression. To address this gap, we used a magnetoactive hydrogel platform that enables real-time, on-demand curvature of endothelial monolayers to reproduce clinically established tortuosity metrics. Using this system, we found that elevated curvature increased nuclear localization of yes-associated protein (YAP), with the strongest response in convex relative to concave regions of highly tortuous endothelial monolayers. This mechanosensitive response was accompanied by reduced VE-Cadherin junctional thickness and increased membrane localization of endothelial nitric oxide synthase. Together, these findings identify local curvature, independent of shear stress, as a regulator of endothelial cell mechanosensing and function, and establish a dynamic hydrogel platform for isolating geometric regulation from shear stress inputs in vascular mechanobiology.

The spatial arrangement of cells is fundamental to the mechanical and functional integrity of three-dimensional (3D) tissues, yet engineering spatially well-controlled tissue architectures remains challenging. Here, we computationally investigated how layered tissue architectures can be designed by modulating cell-cell interfacial tension. We performed simulations using a 3D vertex model and systematically varied interfacial tension magnitudes. The simulations generated a range of layered tissue architectures, including planar monolayers, bilayers, and structurally stratified states. In homogeneous cell populations, increasing interfacial tension drove transitions from monolayer to structurally stratified configurations. In heterogeneous populations, differential interfacial tensions induced out-of-plane cell sorting and the formation of compositionally sorted multilayers. Moreover, a recursive tension design strategy enabled hierarchical organization of multiple cell types into separate layers. Notably, this recursive scheme uses only two tension levels (high vs. low) assigned across interfaces and can, in principle, be extended to specify layered architectures with an arbitrary number of layers. Together, these results identify cell-cell interfacial tension as a tunable mechanical parameter for regulating layered tissue architecture and provide design principles for layered tissue engineering and regenerative medicine.

The thymus plays a pivotal role in the generation of immunocompetent T cells. Although its function is dependent on its complex extracellular matrix, its 3D architecture and mechanical properties remain poorly characterised This knowledge gap limits efforts to model and engineer the organ, which is a critical step towards the development of strategies for the treatment of many haematological and autoimmune diseases. Here, we provide the first comprehensive multiscale dataset of bovine thymic extracellular matrix architecture and viscoelastic behaviour, including quantitiative descriptors such as relaxation times, instantaneous and equilibrium elastic moduli, storage and loss moduli, and spatial mechanical heterogeneity. Taken together, our data define the thymus as a compliant, highly dissipative viscoelastic organ with a fibrillar architecture. They also represent a unique database, which, for the first time, paves the way for quantitative thymus tissue engineering.

The growth rates of biological tissues are influenced by the existing substrate geometry, mechanobiological processes and the interplay between them. Disentangling the contributions of geometry and mechanobiology experimentally is challenging, as mechanical properties are difficult to measure and tissue samples provide only static snapshot in time. However, the composition of a tissue preserves cues of the dynamic processes that shaped its architecture. In this work, we present a computational model of tissue growth that captures aspects of the interplay between geometry, mechanics, and stochastic biological processes, which we use to generate synthetic tissue compositions directly comparable with experimental samples. This framework enables quantitative analysis of tissue morphology, inference of underlying growth mechanisms, estimation of dynamic rates from single-time-point data, and investigation of how stochasticity contributes to emergent growth patterns. We demonstrate the applicability of the model to simulate the growth of different tissue types by applying this framework to two distinct tissue growth scenarios: (i) tissue grown within 3D-printed porous scaffolds, and (ii) bone formation in cortical pores.

Nucleus shape is a sensitive indicator of cell state, influenced by numerous bio-chemical and physiological factors. While prior work has cataloged how perturbations alter nucleus morphology, we address the inverse: inferring underlying molecular changes from nucleus shape alone. We previously developed a mechanical model yielding two nondimensional parameters: flatness index and scale factor, which are surrogate measures for cortical actin tension and nuclear envelope compliance respectively. In this study, we apply these parameters to investigate the dynamics in cellular mechanics during confined migration. We fabricated polydimethylsiloxane (PDMS) microchannels with widths of 3 {micro}m (high confinement) and 10 {micro}m (low confinement) and tracked cells migrating through them. We captured high-frequency 3D nucleus shapes via double fluorescence exclusion microscopy and custom image analysis. Fitting the model and estimating flatness index and scale factor to time-resolved shapes revealed dynamic regulation in 3 {micro}m channels: actin tension decreased and nucleus compliance increased immediately before nucleus entry into the constriction, with rapid restoration to baseline upon exit. No such changes occurred in 10 {micro}m channels, indicating active, confinement-dependent cytoskeletal adaptation. Immunostaining for YAP and lamin-A,C confirmed these model inferences. Our results uncover mechanostasis, active mechanical homeostasis, during confined migration and establish the combination of double fluorescence exclusion microscopy and nondimensional nucleus shape parameters as a powerful, non-invasive tool for single-cell mechanobiology studies.