Integrative Biology

Cancer cells in hypoxic environments often proliferate less but exhibit enhanced migration relative to their normoxic counterparts. Recent in vitro and in silico studies have characterized the role of hypoxic memory - the ability of cancer cells to retain their hypoxic phenotype even when reoxygenated - in tumor invasion. However, the observations have been limited either to exposing cancer cells to hypoxia for a fixed duration or by assuming a fixed-time persistence of the hypoxic state upon reoxygenation independent of the duration of hypoxia exposure. Thus, time-dependent cell-state changes during hypoxia and their impact on hypoxic memory remains unclear. Here, we first analyze transcriptomic data from breast cancer samples to show that the genes upregulated at transcriptional level and hypomethylated at epigenetic level are enriched in cell invasion, indicating hypoxic memory-driven process of tumor invasion. Next, we used a computational model to investigate how the spatial-temporal dynamics of oxygen levels in a tumor drive time-dependent changes in hypoxic memory and influence tumor invasion dynamics. Our simulation results show that such dynamic hypoxic memory can drive enhanced tumor invasion over a fixed hypoxic memory by a) enriching hypoxic cell density at the tumor front, b) reducing sensitivity of hypoxic cell state to fluctuations in oxygen supply, and c) enhancing effective diffusion of hypoxic cells. Our results highlight the crucial role of dynamic hypoxic memory in shaping tumor invasion dynamics, underscoring the need to elucidate its underlying mechanisms in future studies.

BackgroundDespite improved cancer outcomes with kinase inhibitors (KIs), their cardiotoxicity remains a significant clinical challenge. Current approaches to predict and prevent KI-induced cardiac adverse events (CAEs) are limited by an incomplete understanding of underlying mechanisms, including the contribution of off-target kinase engagement. ObjectivesTo establish links between kinase inhibition profiles and cardiotoxic phenotypes using empirical proteomic data, and to leverage these profiles in machine learning (ML) models capable of predicting KI cardiotoxicity. MethodsWe curated a database connecting kinome-wide target binding profiles of FDA-approved KIs (n=44) with documented incidence rates of six distinct CAEs. Binding profiles were derived from unbiased chemoproteomics and used to assess associations between KI selectivity, specific kinase targets, and CAEs. Profiles were further used to develop ML models to predict CAE risk, with SHAP-based model interpretation applied to identify cardiotoxicity-associated kinases. ResultsKI promiscuity was not a significant predictor of cardiotoxicity across all six CAEs. Frequency analysis revealed that kinases including RET, PDGFRB, and DDR1 are recurrently inhibited across CAE-linked compounds, with nearly all identified as off-targets not annotated by the FDA. Network and pathway enrichment analyses supported a systems-level model in which cardiotoxicity arises from coordinated disruption of cardiac-relevant signaling networks. ML models achieved 66-84% cross-validated accuracy (ROC-AUC 0.75-0.8) across CAE endpoints, with SHAP analysis identifying PDGFRB, EGFR, and MEK1/2 among the most predictive kinases. ConclusionsProteomic kinome profiling combined with machine learning provides a mechanistically grounded framework for predicting KI cardiotoxicity and supports off-target-aware drug design to minimize cardiovascular risk.

Experimental methods for characterizing single cells and cell populations have improved tremendously over the past decades. This progress has enabled the development of quantitative, mechanistic models for cellular processes based on either single cell or bulk data. However, coherent statistical frameworks for the model-based integration of different data types at the single-cell and population levels are still missing. In this work, we present a mathematical modeling approach for integrating single-cell time-lapse, single-cell snapshot, single-cell time-to-event and population-average data. Utilizing a formulation based on nonlinear mixed-effect modeling, we enable the description of multiple data types, with and without single-cell resolution, and we propose a tailored parameter estimation method. Furthermore, we propose a tailored parameter estimation scheme that facilitates the assessment of underlying process parameters. Our study demonstrates that the proposed approach can reliably integrate diverse data types, thereby improving parameter identifiability and prediction accuracy. Applying this framework of extrinsic apoptosis reveals that simultaneously considering multiple data types can be essential, particularly when experimental constraints limit data availability. The proposed approach is broadly applicable and may significantly advance our understanding of complex biological processes.

Cancer-associated fibroblasts (CAFs) are a central component of the tumor microenvironment that facilitate a supportive niche for cancer progression and metastasis. Experimental evidence suggests that CAFs can facilitate estrogen-independent tumor growth, thereby reducing the efficacy of anti-hormonal therapies. Understanding and quantifying the complex interactions between tumor cells, hormonal signalling, and the microenvironment are crucial for designing more effective and individualized treatment strategies. We propose a mathematical framework to explore the influence of CAFs on ER+ breast cancer progression and to evaluate strategies to mitigate their impact. We develop a system of nonlinear ordinary differential equations that substantiates the experimental observations by providing a mechanistic basis for the role of CAFs in regulating estrogen-independent growth dynamics. We then employ optimal control theory to evaluate distinct therapeutic approaches involving monotherapy or combinations of: (i) inhibition of tumor-to-CAF signaling, (ii) inhibition of CAF-to-tumor proliferative signaling, and (iii) endocrine therapy. Taken together, our results demonstrate that CAF-targeted strategies can enhance treatment efficacy across various estrogen dosing regimens. Our study provides new insights into the potential of CAF as a therapeutic target that could help to improve existing approaches for endocrine therapies.

BackgroundActivated platelets release polyphosphate (polyP), a linear polymer of inorganic phosphate residues, from dense granules. Experiments performed under no-flow conditions show that polyP alters the kinetics of tissue factor (TF) pathway reactions, accelerating FXI activation by thrombin and FV activation by FXa and thrombin, and may impact inhibition by tissue factor pathway inhibitor (TFPI). How polyP influences this pathway in conjunction with platelet deposition under flow remains understudied. ObjectivesTo investigate how polyP-mediated acceleration of FV and FXI activation modulates thrombin generation under flow in TF-initiated coagulation. MethodsWe extended a previously validated mathematical model of platelet deposition and coagulation under flow to examine polyP-mediated effects following a small vascular injury during intravascular clotting. Simulations varied the surface density of TF exposed, wall shear rate, and plasma TFPI concentration. ResultsPolyP shifts the threshold TF density for a thrombin burst to lower TF densities. For TF densities above this threshold, polyP shortens the lag time to thrombin generation in a TF- and shear-rate-dependent manner. Although no explicit effect of polyP on TFPI function was included in the model, thrombin generation was much less sensitive to TFPI concentration with polyP, in a TF-dependent manner. Relative contributions of accelerations of FV and FXI activations depend on incompletely known enhancements by polyP. ConclusionsThe experimentally observed influence of polyP on TFPI function depends on TF density and may arise indirectly from accelerated FV and FXI activation, with the dominant effect arising through accelerated thrombin-mediated conversion of FV to FVa.

BackgroundTherapeutic agents targeting the PD1-PDL1 interaction are of great clinical value, however accurately predicting which patients are most likely to benefit is challenging. Improved predictive biomarkers for anti-PD1 therapy are clearly needed. Quantifying PD1 saturation by PDL1 in tumor tissue has the potential to serve as such a biomarker. Here we report a novel bioassay called the PD1 Ligand Receptor Complex Aptamer (LIRECAP) assay and demonstrate it can be used to quantify the saturation of PD1 by PDL1 in formalin-fixed paraffin-embedded tumor biospecimens. ResultsThe PD1 LIRECAP assay was developed by identifying a pair of RNA aptamers. One aptamer preferentially binds to unoccupied PD1 (P aptamer) and the other to the PD1-PDL1 complex (C aptamer). P and C aptamers were added together to a formalin-fixed sample, and bound aptamer extracted. A 2-color qRT-PCR assay using a single set of primers was used to determine the ratio of the sample-bound C to P aptamers (C:P ratio) which reflected PD1 saturation by PDL1 in the sample. Quantification of PD1 saturation by PDL1 as determined by the PD1 LIRECAP assay correlated closely with PD1-mediated signaling and PD1-PDL1 proximity. Analysis of sarcoma FFPE biospecimens confirmed the assay is technically reproducible on clinical biospecimens. There were significant differences in PD1 saturation by PDL1 between patients as well as considerable intratumoral heterogeneity. ConclusionsThe PD1 LIRECAP assay is novel assay that can be used to quantify PD1 saturation by PDL1 in clinical biospecimens. The assay is technically feasible, reproducible, and has the potential to serve as a superior predictive biomarker for PD1/PDL1-based therapy. Similar assays based on this platform could be used in other systems and settings to quantify interaction between two molecules.

AbstractsO_ST_ABSBackgroundC_ST_ABSQuantitative systems pharmacology (QSP) models provide mechanistic insight into drug response but are limited by labor-intensive, expert-driven workflows. We developed an AI-assisted QSP (AI-QSP) framework that integrates large language models (LLMs) with SBML-based modeling to enable automated reconstruction, extension, and calibration of mechanistic models. MethodsThe framework was applied to a published CAR-T QSP model. The model was reconstructed in SBML and extended via LLM-guided prompts to incorporate key resistance mechanisms: T-cell exhaustion, PD-1/PD-L1 checkpoint regulation, and tumor antigen escape. Model development followed an iterative expert-in-the-loop workflow. The resulting model (21 reactions, 9 species) was calibrated to synthetic benchmark data using 19-parameter optimization. Model credibility was assessed using ASME V&V 40 and ICH M15 principles, including global sensitivity and profile-likelihood analyses. ResultsThe calibrated model reproduced benchmark dynamics with high accuracy (mean log-RMSE = 0.132). Sensitivity analysis identified CAR-T killing and bystander cytotoxicity as dominant drivers of tumor response. Profile-likelihood analysis showed 71% of parameters were practically identifiable, with remaining parameters prioritised for future data-driven refinement. ConclusionsAI-assisted QSP modeling enables reproducible, scalable model reconstruction and evolution while maintaining mechanistic transparency and regulatory alignment. This framework provides a foundation for accelerating model-informed drug development in cell and gene therapies.

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.

Current clinical management of multiple myeloma (MM) relies on bone marrow (BM) biopsies for minimal residual disease (MRD) assessment. While BM biopsies are the gold standard, their invasive nature and potential to miss extramedullary or patchy disease necessitate sensitive, non-invasive liquid biopsy platforms. In this study, we evaluated the analytical performance of the CellSearch CMMC assay to determine its utility for deep-MRD monitoring. Using a standard 4 mL whole blood input, the assay achieves a WBC-normalized sensitivity of 2.45 x 10-7, supported by a limit of quantitation of 5 cells per run. Given this high analytical sensitivity, the assay provides a robust negative predictive value, rendering false-negative findings highly unlikely in populations with detectable peripheral disease. These findings characterize the CellSearch CMMC assay as a highly sensitive, analytically validated platform for non-invasive deep-MRD level longitudinal surveillance monitoring. When integrated into a clinical workflow that accounts for its specificity profile, the platform offers a patient-friendly complement to serial BM biopsies, with the potential to reduce their frequency in appropriate clinical contexts.

Tumor-induced bone disease (TIBD) arises from a complex interplay between metastatic cancer cells and the bone microenvironment, creating a self-reinforcing "vicious cycle" of bone destruction and tumor growth. Experimental evidence from our group (Buenrostro et al., Bone 113:77-88, 2018) suggests that tumor cells in the bone microenvironment early in disease rely more heavily on bone-derived growth factors, such as transforming growth factor-{beta} (TGF-{beta}), to sustain proliferation than tumor cells late in disease, which may grow independently of these factors. Here, we integrate a mechanistic, population-dynamics model of tumor-bone interactions with in vivo data to test the hypothesis that inhibiting bone resorption suppresses growth of non-adapted but not bone-adapted tumors. The model includes key regulators of TIBD, including TGF-{beta}-driven tumor proliferation, parathyroid hormone-related protein (PTHrP) secretion, and osteoblast (OB)-osteoclast (OC) coupling. Parameter calibration using data from mice injected intratibially with parental (non-adapted) and bone-adapted breast cancer cells reveals distinct parameter values for each tumor type. Bone-adapted cells exhibit a higher basal division rate and reduced sensitivity to TGF-{beta}-mediated stimulation, whereas parental-derived tumor cells depend more strongly on TGF-{beta} and secrete PTHrP at higher rates to compensate for their slower growth. Model simulations reproduce the greater bone loss observed experimentally for bone-adapted tumors and predict that, for non-adapted tumors, bone destruction results from a slower but meaningful rise in OC activity and a possible moderate decline in OBs. Simulated treatment of bone-adapted tumors with the bisphosphonate zoledronic acid stabilizes bone density but has limited or highly variable effects on tumor growth. These results suggest that OC inhibition alone may be insufficient to restrain tumor expansion once tumors have adapted to the bone microenvironment. Together, these findings support the hypothesis that tumor adaptation to the bone microenvironment governs dependence on bone-derived growth factors and response to OC-targeted therapy, underscoring the value of mechanistic modeling for elucidating tumor-bone interactions and guiding tumor-type-specific treatment strategies for TIBD.

The blood vasculature has a high capacity for structural regeneration, driven by the blood endothelial cells (BECs) that comprise it. This regenerative process, which involves BEC migration and proliferation to form these complex tissues, is linked to low frequency (< 0.1 Hz) calcium spiking that precedes these activities. However, we need new approaches to stimulating angiogenic responses in tissue engineering applications. By conducting experiments that manipulate local ionic concentrations and developing a simple, yet powerful, computational analysis, we demonstrate that sodium-calcium cross-talk is a crucial component that regulates the calcium signaling and downstream angiogenic responses. Activation and deactivation of the inositol triphosphate 3 receptors (IP3Rs) on the endoplasmic reticulum (ER) and the switch between forward and reverse modes of the sodium-calcium exchanger (NCX) are proposed to be the key mechanisms underlying calcium oscillations when cells are exposed to temporary cationic depletion. The spiking is suggested to be a release of intracellular calcium mediated by IP3R activity, and transport in or out of the cell is driven by NCX for the calcium oscillatory signaling pattern. The NCX and IP3R both contribute to manage intracellular calcium and ionic concentration as initially there is a long ER deactivation period while intracellular sodium slowly increases until a sudden onset of calcium is released by the ER. Other calcium and sodium ion channels can change this resonant coupling of ER and NCX to alter the inter-spike duration. Synchronization of the spiking intervals between cells is triggered by stimulating with vascular endothelial growth factor (VEGF), which induces a propagating wave of intracellular calcium across the 2D tissue culture, prior to coordinated cell migration and proliferation towards the VEGF source. This wave, which can be artificially induced and studied using electrical stimulation, suggests that the underlying sodium-calcium crosstalk mechanism introduces intracellular calcium polarization, whose orientation is transferred across cells through spike synchronization. Thus, control of calcium signaling dynamics through regulation of ionic depletion can serve as useful method for generating angiogenic responses in engineered tissue constructs.

Extracellular matrix (ECM) proteins assemble to form a heterogeneous connective scaffold that supports cells. Physical interactions between cells and the matrix regulate cellular behaviors and influence subsequent tissue construction. However, there is a lack of fundamental understanding regarding the contributions of individual native ECM proteins to the matrix. This gap arises from the need for nanoscopic characterization, which operates on a much smaller length scale than typical assessments in cell and tissue cultures, as well as in tissue reconstruction and clinical implantation. This study aims to systematically investigate how individual ECM proteins affect lipid membranes structurally and mechanically, and how these influences regulate cell migration. Results from Langmuir isotherm analysis, X-ray reflectivity measurements, and cell scratch assays demonstrate that strong collagen adsorption on the membrane surface disrupts lipid packing. However, its rigid network provides a sturdy scaffold for cell adhesion, thereby enhancing cell attachment and promoting cell migration. In contrast, elastin has a minimal structural or mechanical impact on the membrane during both adsorption and compression, but it benefits cells by facilitating migration and reducing the risk of infection. Fibronectin, on the other hand, exhibits complex mechanical responses to compression, characterized by significant structural rearrangements that occur during adsorption. This strong interaction with the membrane can result in excessively high adhesion forces, ultimately limiting cell motility. These findings lay the foundation for the design of artificial scaffolds that can manipulate cellular responses, a critical step toward advancing regenerative medicine and tissue engineering. SignificanceFabricating extracellular matrix (ECM) scaffolds from cells offers advantages over traditional approaches, such as decellularized tissues, which face donor limitations, and artificial scaffolds, which may hinder cellular communication. However, the slow harvesting process of cell-derived ECM has limited its clinical applications. This research is part of a larger mission to engineer ECM prescaffolds on lipid carriers tailored to cell requirements, enhancing ECM production and regulating cell behavior. The first step involves systematically analyzing the structural and mechanical effects of ECM on lipid membranes and how these effects regulate cellular behavior. This work confirms distinct characteristics of ECM proteins, advancing fundamental understanding of cell-matrix interactions and paving the way for scaffold engineering.

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.

Metastasis is the leading cause of death in breast cancer patients, yet there are no drugs specifically designed to block cancer cell intravasation, an early step of the metastatic cascade that originates circulating tumour cells (CTCs). A major challenge in developing anti-intravasation drugs is the scarcity of relevant in vitro platforms suitable for predictable drug discovery. Intravasation is a fundamental step of metastasis and involves the crossing of cancer cells through an endothelial barrier to enter the blood circulation. Here we developed an intravasation-on-a-chip model with controlled extracellular matrix composition, fluid flow and shear stress, which mimics the dynamic tumour-endothelium interface. The systems allows real-time imaging of intravasation and the isolation and quantification of intravasated cancer cells. As a proof-of-concept for drug testing, we show that perfusion with the PI3K/mTOR inhibitor Dactolisib, significantly reduced intravasation without compromising endothelial cell viability. The system also provides the capability to evaluate inhibitor on-target activity via imaging analysis. This intravasation-on-a-chip model offers a powerful, scalable, and imaging-compatible platform for discovering and evaluating anti-intravasation compounds.

Cells dynamically integrate biochemical and mechanical signals arising from their surrounding microenvironment to regulate morphology and behavior. Mechanical cues like matrix stiffness, surface topography, and other physical perturbations modify biophysical signals. Surface topography, particularly curvature regime acts as any important mediator of mechanotransduction by coordinating cytoskeletal organization, focal adhesion dynamics, and nuclear architecture. Curvature response has been demonstrated at broader length scales and influences nucleus shape change, chromatin organization, and gene regulation, positioning the nucleus as an active mechanosensitive hub. Bone tissue consists of a curvature-rich microenvironment defined by a trabecular architecture at tissue scale and by resorption cavities such as Howships lacunae at cellular scale. While these geometries are essential for homeostasis, their role in pathological context remains poorly understood. Osteosarcoma develops within this mechanically complex multiscale architecture, but how bone-inspired curvature regulates nuclear behavior and signaling in osteosarcoma cells remains unclear. Here, we engineered three-dimensional (3D) concave hemispherical substrates that recapitulate nucleus-scale bone micro-curvature and assessed their effects on human SaOS-2 osteosarcoma cells. In comparison with flat surfaces, concave confinement resulted in pronounced nuclear rounding and softening, accompanied by Lamin A/C reorganization and increased heterochromatin compaction marked by H3K9me3. Curvature-driven nuclear remodeling selectively modulated Hippo pathway main effectors YAP/TAZ without activating NF-{kappa}B mediated canonical inflammatory responses. Furthermore, cells maintained overall viability without elevated pathological DNA damage or apoptotic signaling, suggesting an adaptive, damage-tolerant nuclear response. Overall, these findings indicate nucleus-scale curvature as a critical regulator within the bone microenvironment that governs nuclear modelling and mechanosensitive signaling in osteosarcoma cells. Incorporating physiologically relevant geometry into in vitro models establishes new insight into cancer microenvironment crosstalk and highlights nuclear interior and outer architecture as a key regulator of tumor cell behavior.

BackgroundTo control leishmaniasis, chemotherapy drugs are currently under development. However, these drugs often exhibit poor efficacy and are associated with toxicity, adverse effects, and drug resistance. At present, no specific drug is available for the treatment of leishmaniasis. Meanwhile, vaccine research is ongoing. Recent studies have analysed some experimental vaccines using mathematical models. AimIn previous work, drug targeting was focused on the entire human body rather than specifically addressing infected macrophages and parasites. In our current approach, we aim to eliminate infected macrophages and parasites through nano-drug design. Specifically, we utilise two types of nanoparticles: iron oxide and citric acid-coated iron oxide. Moving forward, we plan to advance this strategy using mathematical modelling of macrophage-parasite interactions. MethodsWe design PDE-based models of macrophages and parasites, incorporating cytokine dynamics, to support nano-drug development. Drug efficacy is estimated using posterior distributions to analyse phenotypic fluctuations of macrophages and parasites during the design phase. We investigate implicit and semi-implicit treatment schemes, focusing on energy decay properties. To model drug flow during treatment, we introduce a three-phase moving boundary problem. Comparative analyses are conducted to evaluate macrophage and parasite behaviour with and without treatment. Finally, the entire framework is implemented within a virtual lab environment. ResultsThe results show that the nano-drug exhibits better efficacy compared to combined drug doses. We analysed and compared two types of nano-drug particles: iron oxide and citric acid-coated iron oxide. We discuss how the drug effectively targets and eliminates infected macrophages and parasites. ConclusionOur models results and simulations will support researchers conducting further studies in nano-drug design for leishmaniasis. These simulations are performed within a virtual lab environment.

Agent-based modeling (ABM) is a computational method for predicting the emergent outcomes of interacting, autonomous individuals in a complex system. Here, ABM is used to simulate interactions between fibroblast and myofibroblast cells during idiopathic pulmonary fibrosis (IPF) in alveolar tissue microenvironments. These microenvironments are derived from histology of a healthy human lung sample and moderate- and severe-IPF lung samples. Fibroblast differentiation, cell migration, and collagen secretion in response to the spatial distribution of the cytokine transforming growth factor-beta are captured in the ABM using NetLogo software. Results are presented from one simulated year without treatment and with mechanisms representing treatment by pirfenidone and pentoxifylline, alone and in combination. A total of 180 in silico experiments are run, analyzed, and compared in a high-throughput workflow. The effects of the initial number of fibroblasts and treatment scenarios on various metrics related to collagen accumulation and collagen invasion into alveolar regions are determined. The ABM and the analysis files are shared to facilitate model reuse. By integrating computational modeling of IPF and therapeutics, this research aims to improve understanding of fibrosis progression and assess the efficacy of novel and existing treatments targeting different mechanisms to inform decision-making for IPF treatment.

Detection of micro- and nanoplastics (MNPs) in human tissues has raised growing concern about their biological effects on tissue and cell function. While previous studies have examined MNP-cell interaction, most focused on limited cell and plastic types. Here, we present a comprehensive, quantitative investigation into how different types of nanoplastics (NPs) associate with and affect diverse cell types under physiologically relevant conditions. Using microfluidic-calibrated fluorescence microscopy, we quantify NP accumulation in cells in vitro and match cellular NP concentrations to levels reported in human tissues. While cell-associated NPs could be gradually released in vitro, they persist in vivo for over one month without detectable reduction in a mouse model. We discover that NP exposure at these levels broadly impairs cell proliferation across epithelial, endothelial, fibroblast, and immune cells, with cell type-dependent sensitivity. NP exposure also reduces motility in T cells and fibroblasts, with more complex effects observed in macrophages. Mechanistically, NP-cell association and trans-epithelial transport involved not only classical endocytic regulators but also pathways related to ion and water transport. Notably, NP association and release were highly sensitive to the extracellular fluid environment within the physiological range. By testing inhibitors of these pathways, we identified molecules that reduce NP-cell association and promote release. We further compared common NPs found in human samples and widely used in research: polystyrene (PS), polyethylene (PE), and polypropylene (PP). Although these NPs similarly impaired proliferation and motility, they showed markedly different cellular association and release dynamics. These findings reveal the impact of NPs on tissue cell functions and uncover novel regulatory pathways, establishing a quantitative framework for studying NP-cell interactions in biologically relevant conditions.

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.

Agent-based models (ABMs) are widely used to study complex multiscale biological systems, particularly in cancer research. However, their high-dimensional parameter spaces, stochasticity, and computational costs pose significant challenges for uncertainty quantification, calibration, and systematic comparison of competing mechanistic hypotheses. PhysiCell has evolved into a growing ecosystem of open-source tools supporting physics-based multicellular modeling, including model construction, visualization, and data integration. However, despite these advances, systematic support for uncertainty-aware model analysis, scalable parameter exploration, and formal calibration workflows remains limited. Here, we introduce UQ-PhysiCell, an open-source Python package that enables uncertainty quantification, calibration, and model selection for PhysiCell models using a modular and scalable workflow. UQ-PhysiCell acts as a manager of PhysiCell simulation inputs and outputs, including parameters, initial conditions, rules, and MultiCellDS-compliant objects, and provides automated orchestration of large ensembles of simulations. The framework supports multiple levels of parallelism to accelerate the analysis, including the parallel execution of independent simulations, stochastic replicates, and downstream analysis tasks. UQ-PhysiCell integrates seamlessly with established Python libraries for sensitivity analysis, optimization, Bayesian inference, and surrogate modeling, allowing users to construct customized pipelines that match their modeling goals and computational resource requirements. By decoupling model execution from statistical analysis and emphasizing extensibility and reproducibility, UQ-PhysiCell lowers the barrier to applying rigorous uncertainty-aware methodologies to agent-based modeling and supports the systematic evaluation of PhysiCell models in biological and biomedical research. Author summaryWe developed UQ-PhysiCell to address a key challenge in agent-based modeling: the systematic quantification of uncertainty in complex stochastic simulations. PhysiCell is widely used to model multicellular biological systems, particularly in cancer research; however, practical tools for uncertainty analysis, calibration, and model comparison are often developed in an ad hoc manner. This makes the results difficult to reproduce and limits the ability to rigorously evaluate competing biological hypotheses. UQ-PhysiCell provides a flexible Python framework that manages the inputs and outputs of PhysiCell simulations and enables large-scale computational analysis. We designed the software to be modular, allowing users to build their own analysis pipelines and combine different methodologies for sensitivity analysis, calibration, and model selection. Rather than enforcing a single workflow, UQ-PhysiCell supports customization to match specific scientific questions and computational requirements. To make uncertainty-aware analyses feasible for computationally intensive agent-based models, UQ-PhysiCell implements multiple parallelism strategies, enabling the concurrent execution of simulations, stochastic replicates, and downstream analyses. By promoting reproducibility, scalability, and methodological flexibility, UQ-PhysiCell helps researchers move beyond single best-fit simulations toward more reliable and interpretable computational modeling.