Brain Microvascular Pericyte Pathology Linking Alzheimer's Disease to Diabetes.

The brain microvasculature, which delivers oxygen and nutrients and forms a critical barrier protecting the central nervous system via capillaries, is deleteriously affected by both Alzheimer's disease (AD) and type 2 diabetes (T2D). T2D patients have an increased risk of developing AD, suggesting potentially related microvascular pathological mechanisms. Pericytes are an ideal cell type to study for functional links between AD and T2D. These specialized capillary-enwrapping cells regulate capillary density, lumen diameter, and blood flow. Pericytes also maintain endothelial tight junctions to ensure blood-brain barrier integrity, modulation of immune cell extravasation, and clearance of toxins. Changes in these phenomena have been observed in both AD and T2D, implicating "pericyte pathology" as a common feature of AD and T2D. This review examines the mechanisms of AD and T2D from the perspective of the brain microvasculature, highlighting how pericyte pathology contributes to both diseases. Our review identifies voids in understanding how AD and T2D negatively impact the brain microvasculature and suggests future studies to examine the intersections of these diseases.

Modeling the roles of cohesotaxis, cell-intercalation, and tissue geometry in collective cell migration of Xenopus mesendoderm.

Collectively migrating Xenopus mesendoderm cells are arranged into leader and follower rows with distinct adhesive properties and protrusive behaviors. In vivo, leading row mesendoderm cells extend polarized protrusions and migrate along a fibronectin matrix assembled by blastocoel roof cells. Traction stresses generated at the leading row result in the pulling forward of attached follower row cells. Mesendoderm explants removed from embryos provide an experimentally tractable system for characterizing collective cell movements and behaviors, yet the cellular mechanisms responsible for this mode of migration remain elusive. We introduce a novel agent-based computational model of migrating mesendoderm in the Cellular-Potts computational framework to investigate the respective contributions of multiple parameters specific to the behaviors of leader and follower row cells. Sensitivity analyses identify cohesotaxis, tissue geometry, and cell intercalation as key parameters affecting the migration velocity of collectively migrating cells. The model predicts that cohesotaxis and tissue geometry in combination promote cooperative migration of leader cells resulting in increased migration velocity of the collective. Radial intercalation of cells towards the substrate is an additional mechanism contributing to an increase in migratory speed of the tissue. Model outcomes are validated experimentally using mesendoderm tissue explants.

Agent-based model demonstrates the impact of nonlinear, complex interactions between cytokinces on muscle regeneration.

Muscle regeneration is a complex process due to dynamic and multiscale biochemical and cellular interactions, making it difficult to identify microenvironmental conditions that are beneficial to muscle recovery from injury using experimental approaches alone. To understand the degree to which individual cellular behaviors impact endogenous mechanisms of muscle recovery, we developed an agent-based model (ABM) using the Cellular-Potts framework to simulate the dynamic microenvironment of a cross-section of murine skeletal muscle tissue. We referenced more than 100 published studies to define over 100 parameters and rules that dictate the behavior of muscle fibers, satellite stem cells (SSCs), fibroblasts, neutrophils, macrophages, microvessels, and lymphatic vessels, as well as their interactions with each other and the microenvironment. We utilized parameter density estimation to calibrate the model to temporal biological datasets describing cross-sectional area (CSA) recovery, SSC, and fibroblast cell counts at multiple timepoints following injury. The calibrated model was validated by comparison of other model outputs (macrophage, neutrophil, and capillaries counts) to experimental observations. Predictions for eight model perturbations that varied cell or cytokine input conditions were compared to published experimental studies to validate model predictive capabilities. We used Latin hypercube sampling and partial rank correlation coefficient to identify in silico perturbations of cytokine diffusion coefficients and decay rates to enhance CSA recovery. This analysis suggests that combined alterations of specific cytokine decay and diffusion parameters result in greater fibroblast and SSC proliferation compared to individual perturbations with a 13% increase in CSA recovery compared to unaltered regeneration at 28 days. These results enable guided development of therapeutic strategies that similarly alter muscle physiology (i.e. converting extracellular matrix [ECM]-bound cytokines into freely diffusible forms as studied in cancer therapeutics or delivery of exogenous cytokines) during regeneration to enhance muscle recovery after injury.

Optimized Biomanufacturing for Treatment of Volumetric Muscle Loss Enables Physiomimetic Recovery.

Volumetric muscle loss (VML) injuries are defined by loss of sufficient skeletal muscle to produce persistent deficits in muscle form and function, with devastating lifelong consequences to both soldiers and civilians. There are currently no satisfactory treatments for VML injuries. The work described herein details the implementation of a fully enclosed bioreactor environment (FEBE) system that efficiently interfaces with our existing automated bioprinting and advanced biomanufacturing methods for cell deposition on sheet-based scaffolds for our previously described tissue-engineered muscle repair (TEMR) technology platform. Briefly, the TEMR technology consists of a porcine bladder acellular matrix seeded with skeletal muscle progenitor cells and preconditioned via 10% uniaxial cyclic stretch in a bioreactor. Overall, TEMR implantation in an established rat tibialis anterior (TA) VML injury model can result in 60 to ∼90% functional recovery. However, our original study documented >50% failure rate. That is, more than half of the implanted TEMR constructs produced no functional improvement beyond no treatment/repair. The high failure rate was attributed to the untoward mechanical disruption of TEMR during surgical implantation. In a follow-up study, adjustments were made to the geometry of both the VML injury and the TEMR construct, and the "nonresponder" group was reduced from over half the TEMR-treated animals to just 33%. Nonetheless, additional improvement is needed for clinical applicability. The main objectives of the current study were twofold: (1) explore the use of advanced biomanufacturing methods (i.e., FEBE bioreactor) to further improve TEMR reliability (i.e., increase functional response rate), (2) determine if previously established bioprinting methods, when coupled to the customized FEBE system would further improve the rate, magnitude or amplitude of functional outcomes following TEMR implantation in the same rat TA VML injury model. The current study demonstrates the unequivocal benefits of a customized bioreactor system that reduces manipulation of TEMR during cell seeding and maturation via bioprinting while simultaneously maximizing TEMR stability throughout the biofabrication process. This new biomanufacturing strategy not only accelerated the rate of functional recovery, but also eliminated all TEMR failures. In addition, implementation of bioprinting resulted in more physiomimetic skeletal muscle characteristics of repaired muscle tissue.

Multiscale computational model predicts how environmental changes and drug treatments affect microvascular remodeling in fibrotic disease.

Investigating the molecular, cellular, and tissue-level changes caused by disease, and the effects of pharmacological treatments across these biological scales, necessitates the use of multiscale computational modeling in combination with experimentation. Many diseases dynamically alter the tissue microenvironment in ways that trigger microvascular network remodeling, which leads to the expansion or regression of microvessel networks. When microvessels undergo remodeling in idiopathic pulmonary fibrosis (IPF), functional gas exchange is impaired due to loss of alveolar structures and lung function declines. Here, we integrated a multiscale computational model with independent experiments to investigate how combinations of biomechanical and biochemical cues in IPF alter cell fate decisions leading to microvascular remodeling. Our computational model predicted that extracellular matrix (ECM) stiffening reduced microvessel area, which was accompanied by physical uncoupling of endothelial cell (ECs) and pericytes, the cells that comprise microvessels. Nintedanib, an FDA-approved drug for treating IPF, was predicted to further potentiate microvessel regression by decreasing the percentage of quiescent pericytes while increasing the percentage of pericytes undergoing pericyte-myofibroblast transition (PMT) in high ECM stiffnesses. Importantly, the model suggested that YAP/TAZ inhibition may overcome the deleterious effects of nintedanib by promoting EC-pericyte coupling and maintaining microvessel homeostasis. Overall, our combination of computational and experimental modeling can explain how cell decisions affect tissue changes during disease and in response to treatments.

Agent-based model demonstrates the impact of nonlinear, complex interactions between cytokines on muscle regeneration.

Muscle regeneration is a complex process due to dynamic and multiscale biochemical and cellular interactions, making it difficult to identify microenvironmental conditions that are beneficial to muscle recovery from injury using experimental approaches alone. To understand the degree to which individual cellular behaviors impact endogenous mechanisms of muscle recovery, we developed an agent-based model (ABM) using the Cellular Potts framework to simulate the dynamic microenvironment of a cross-section of murine skeletal muscle tissue. We referenced more than 100 published studies to define over 100 parameters and rules that dictate the behavior of muscle fibers, satellite stem cells (SSC), fibroblasts, neutrophils, macrophages, microvessels, and lymphatic vessels, as well as their interactions with each other and the microenvironment. We utilized parameter density estimation to calibrate the model to temporal biological datasets describing cross-sectional area (CSA) recovery, SSC, and fibroblast cell counts at multiple time points following injury. The calibrated model was validated by comparison of other model outputs (macrophage, neutrophil, and capillaries counts) to experimental observations. Predictions for eight model perturbations that varied cell or cytokine input conditions were compared to published experimental studies to validate model predictive capabilities. We used Latin hypercube sampling and partial rank correlation coefficient to identify in silico perturbations of cytokine diffusion coefficients and decay rates to enhance CSA recovery. This analysis suggests that combined alterations of specific cytokine decay and diffusion parameters result in greater fibroblast and SSC proliferation compared to individual perturbations with a 13% increase in CSA recovery compared to unaltered regeneration at 28 days. These results enable guided development of therapeutic strategies that similarly alter muscle physiology (i.e. converting ECM-bound cytokines into freely diffusible forms as studied in cancer therapeutics or delivery of exogenous cytokines) during regeneration to enhance muscle recovery after injury.

General, open-source vertex modeling in biological applications using Tissue Forge.

Vertex models are a widespread approach for describing the biophysics and behaviors of multicellular systems, especially of epithelial tissues. Vertex models describe a wide variety of developmental scenarios and behaviors like cell rearrangement and tissue folding. Often, these models are implemented as single-use or closed-source software, which inhibits reproducibility and decreases accessibility for researchers with limited proficiency in software development and numerical methods. We developed a physics-based vertex model methodology in Tissue Forge, an open-source, particle-based modeling and simulation environment. Our methodology describes the properties and processes of vertex model objects on the basis of vertices, which allows integration of vertex modeling with the particle-based formalism of Tissue Forge, enabling an environment for developing mixed-method models of multicellular systems. Our methodology in Tissue Forge inherits all features provided by Tissue Forge, delivering open-source, extensible vertex modeling with interactive simulation, real-time simulation visualization and model sharing in the C, C++ and Python programming languages and a Jupyter Notebook. Demonstrations show a vertex model of cell sorting and a mixed-method model of cell migration combining vertex- and particle-based models. Our methodology provides accessible vertex modeling for a broad range of scientific disciplines, and we welcome community-developed contributions to our open-source software implementation.

Modeling the roles of cohesotaxis, cell-intercalation, and tissue geometry in collective cell migration of Xenopus mesendoderm.

Collectively migrating Xenopus mesendoderm cells are arranged into leader and follower rows with distinct adhesive properties and protrusive behaviors. In vivo, leading row mesendoderm cells extend polarized protrusions and migrate along a fibronectin matrix assembled by blastocoel roof cells. Traction stresses generated at the leading row result in the pulling forward of attached follower row cells. Mesendoderm explants removed from embryos provide an experimentally tractable system for characterizing collective cell movements and behaviors, yet the cellular mechanisms responsible for this mode of migration remain elusive. We introduce an agent-based computational model of migrating mesendoderm in the Cellular-Potts computational framework to investigate the relative contributions of multiple parameters specific to the behaviors of leader and follower row cells. Sensitivity analyses identify cohesotaxis, tissue geometry, and cell intercalation as key parameters affecting the migration velocity of collectively migrating cells. The model predicts that cohesotaxis and tissue geometry in combination promote cooperative migration of leader cells resulting in increased migration velocity of the collective. Radial intercalation of cells towards the substrate is an additional mechanism to increase migratory speed of the tissue. Summary Statement: We present a novel Cellular-Potts model of collective cell migration to investigate the relative roles of cohesotaxis, tissue geometry, and cell intercalation on migration velocity of Xenopus mesendoderm.

Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in antibiotic responses.

Antibiotic resistance poses mounting risks to human health, as current antibiotics are losing efficacy against increasingly resistant pathogenic bacteria. Of particular concern is the emergence of multidrug-resistant strains, which has been rapid among Gram-negative bacteria such as Escherichia coli. A large body of work has established that antibiotic resistance mechanisms depend on phenotypic heterogeneity, which may be mediated by stochastic expression of antibiotic resistance genes. The link between such molecular-level expression and the population levels that result is complex and multi-scale. Therefore, to better understand antibiotic resistance, what is needed are new mechanistic models that reflect single-cell phenotypic dynamics together with population-level heterogeneity, as an integrated whole. In this work, we sought to bridge single-cell and population-scale modeling by building upon our previous experience in "whole-cell" modeling, an approach which integrates mathematical and mechanistic descriptions of biological processes to recapitulate the experimentally observed behaviors of entire cells. To extend whole-cell modeling to the "whole-colony" scale, we embedded multiple instances of a whole-cell E. coli model within a model of a dynamic spatial environment, allowing us to run large, parallelized simulations on the cloud that contained all the molecular detail of the previous whole-cell model and many interactive effects of a colony growing in a shared environment. The resulting simulations were used to explore the response of E. coli to two antibiotics with different mechanisms of action, tetracycline and ampicillin, enabling us to identify sub-generationally-expressed genes, such as the beta-lactamase ampC, which contributed greatly to dramatic cellular differences in steady-state periplasmic ampicillin and was a significant factor in determining cell survival.

General, Open-Source Vertex Modeling in Biological Applications Using Tissue Forge.

Vertex models are a widespread approach for describing the biophysics and behaviors of multicellular systems, especially of epithelial tissues. Vertex models describe a wide variety of developmental scenarios and behaviors like cell rearrangement and tissue folding. Often, these models are implemented as single-use or closed-source software, which inhibits reproducibility and decreases accessibility for researchers with limited proficiency in software development and numerical methods. We developed a physics-based vertex model methodology in Tissue Forge, an open-source, particle-based modeling and simulation environment. Our methodology describes the properties and processes of vertex model objects on the basis of vertices, which allows integration of vertex modeling with the particle-based formalism of Tissue Forge, enabling an environment for developing mixed-method models of multicellular systems. Our methodology in Tissue Forge inherits all features provided by Tissue Forge, delivering opensource, extensible vertex modeling with interactive simulation, real-time simulation visualization and model sharing in the C,C++ and Python programming languages and a Jupyter Notebook. Demonstrations show a vertex model of cell sorting and a mixed-method model of cell migration combining vertex- and particle-based models. Our methodology provides accessible vertex modeling for a broad range of scientific disciplines, and we welcome community-developed contributions to our open-source software implementation.

Correction: A computational modeling approach for predicting multicell spheroid patterns based on signaling-induced differential adhesion.

[This corrects the article DOI: 10.1371/journal.pcbi.1010701.].

Variations in mechanical stiffness alter microvascular sprouting and stability in a PEG hydrogel model of idiopathic pulmonary fibrosis.

OBJECTIVE: Microvascular remodeling is governed by biomechanical and biochemical cues which are dysregulated in idiopathic pulmonary fibrosis. Understanding how these cues impact endothelial cell-pericyte interactions necessitates a model system in which both variables can be independently and reproducibly modulated. In this study we develop a tunable hydrogel-based angiogenesis assay to study how varying angiogenic growth factors and environmental stiffness affect sprouting and vessel organization. METHODS: Lungs harvested from mice were cut into 1 mm long segments then cultured on hydrogels having one of seven possible stiffness and growth factor combinations. Time course, brightfield, and immunofluorescence imaging were used to observe and quantify sprout formation. RESULTS: Our assay was able to support angiogenesis in a comparable manner to Matrigel in soft 2 kPa gels while enabling tunability to study the effects of stiffness on sprout formation. Matrigel and 2 kPa groups contained significantly more samples with sprouts when compared to the stiffer 10 and 20 kPa gels. Growth factor treatment did not have as obvious an effect, although the 20 kPa PDGF + FGF-treated group had significantly longer vessels than the vascular endothelial growth factor-treated group. CONCLUSIONS: We have developed a novel, tunable hydrogel assay for the creation of lung explant vessel organoids which can be modulated to study the impact of specific environmental cues on vessel formation and maturation.

Polarized localization of phosphatidylserine in the endothelium regulates Kir2.1.

Lipid regulation of ion channels is largely explored using in silico modeling with minimal experimentation in intact tissue; thus, the functional consequences of these predicted lipid-channel interactions within native cellular environments remain elusive. The goal of this study is to investigate how lipid regulation of endothelial Kir2.1 - an inwardly rectifying potassium channel that regulates membrane hyperpolarization - contributes to vasodilation in resistance arteries. First, we show that phosphatidylserine (PS) localizes to a specific subpopulation of myoendothelial junctions (MEJs), crucial signaling microdomains that regulate vasodilation in resistance arteries, and in silico data have implied that PS may compete with phosphatidylinositol 4,5-bisphosphate (PIP2) binding on Kir2.1. We found that Kir2.1-MEJs also contained PS, possibly indicating an interaction where PS regulates Kir2.1. Electrophysiology experiments on HEK cells demonstrate that PS blocks PIP2 activation of Kir2.1 and that addition of exogenous PS blocks PIP2-mediated Kir2.1 vasodilation in resistance arteries. Using a mouse model lacking canonical MEJs in resistance arteries (Elnfl/fl/Cdh5-Cre), PS localization in endothelium was disrupted and PIP2 activation of Kir2.1 was significantly increased. Taken together, our data suggest that PS enrichment to MEJs inhibits PIP2-mediated activation of Kir2.1 to tightly regulate changes in arterial diameter, and they demonstrate that the intracellular lipid localization within the endothelium is an important determinant of vascular function.

A computational modeling approach for predicting multicell spheroid patterns based on signaling-induced differential adhesion.

Physiological and pathological processes including embryogenesis and tumorigenesis rely on the ability of individual cells to work collectively to form multicell patterns. In these heterogeneous multicell systems, cell-cell signaling induces differential adhesion between cells that leads to tissue-level patterning. However, the sensitivity of pattern formation to changes in the strengths of signaling or cell adhesion processes is not well understood. Prior work has explored these issues using synthetically engineered heterogeneous multicell spheroid systems, in which cell subpopulations engage in bidirectional intercellular signaling to regulate the expression of different cadherins. While engineered cell systems provide excellent experimental tools to observe pattern formation in cell populations, computational models of these systems may be leveraged to explore more systematically how specific combinations of signaling and adhesion parameters can drive the emergence of unique patterns. We developed and validated two- and three-dimensional agent-based models (ABMs) of spheroid patterning for previously described cells engineered with a bidirectional signaling circuit that regulates N- and P-cadherin expression. Systematic exploration of model predictions, some of which were experimentally validated, revealed how cell seeding parameters, the order of signaling events, probabilities of induced cadherin expression, and homotypic adhesion strengths affect pattern formation. Unsupervised clustering was also used to map combinations of signaling and adhesion parameters to these unique spheroid patterns predicted by the ABM. Finally, we demonstrated how the model may be deployed to design new synthetic cell signaling circuits based on a desired final multicell pattern.

Identifying individual social risk factors using unstructured data in electronic health records and their relationship with adverse clinical outcomes.

Objective: To determine the prevalence of individual-level social risk factors documented in unstructured data from electronic health records (EHRs) and the relationship between social risk factors and adverse clinical outcomes. Study setting: Inpatient encounters for adults (≥18 years) at the University of Virginia Medical Center during a 12-month study period between July 2018 and June 2019. Inpatient encounters for labor and delivery patients were excluded, as well as encounters where the patient was discharged to hospice, left against medical advice, or expired in the hospital. The study population included 21,402 inpatient admissions, representing 15,116 unique patients who had at least one inpatient admission during the study period. Study design: We identified measures related to individual social risk factors in EHRs through existing workflows, flowsheets, and clinical notes. Multivariate binomial logistic regression was performed to determine the association of individual social risk factors with unplanned inpatient readmissions, post-discharge emergency department (ED) visits, and extended length of stay (LOS). Other predictors included were age, sex, severity of illness, location of residence, and discharge destination. Results: Predictors of 30-day unplanned readmissions included severity of illness (OR = 3.96), location of residence (OR = 1.31), social and community context (OR = 1.26), and economic stability (OR = 1.37). For 30-day post-discharge ED visits, significant predictors included location of residence (OR = 2.56), age (OR = 0.60), economic stability (OR = 1.39), education (OR = 1.38), social and community context (OR = 1.39), and neighborhood and built environment (OR = 1.61). For extended LOS, significant predictors were age (OR = 0.51), sex (OR = 1.18), severity of illness (OR = 2.14), discharge destination (OR = 2.42), location of residence (OR = 0.82), economic stability (OR = 1.14), neighborhood and built environment (OR = 1.31), and education (OR = 0.79). Conclusions: Individual-level social risk factors are associated with increased risk for unplanned hospital readmissions, post-discharge ED visits, and extended LOS. While individual-level social risk factors are currently documented on an ad-hoc basis in EHRs, standardized SDoH screening tools using validated metrics could help eliminate bias in the collection of SDoH data and facilitate social risk screening.

Photoacoustic microscopy of vascular adaptation and tissue oxygen metabolism during cutaneous wound healing.

Cutaneous wounds affect millions of people every year. Vascularization and blood oxygen delivery are critical bottlenecks in wound healing, and understanding the spatiotemporal dynamics of these processes may lead to more effective therapeutic strategies to accelerate wound healing. In this work, we applied multi-parametric photoacoustic microscopy (PAM) to study vascular adaptation and the associated changes in blood oxygen delivery and tissue oxygen metabolism throughout the hemostasis, inflammatory, proliferation, and early remodeling phases of wound healing in mice with skin puncture wounds. Multifaceted changes in the vascular structure, function, and tissue oxygen metabolism were observed during the 14-day monitoring of wound healing. On the entire wound area, significant elevations of the arterial blood flow and tissue oxygen metabolism were observed right after wounding and remained well above the baseline over the 14-day period. On the healing front, biphasic changes in the vascular density and blood flow were observed, both of which peaked on day 1, remained elevated in the first week, and returned to the baselines by day 14. Along with the wound closure and thickening, tissue oxygen metabolism in the healing front remained elevated even after structural and functional changes in the vasculature were stabilized. On the newly formed tissue, significantly higher blood oxygenation, flow, and tissue metabolism were observed compared to those before wounding. Blood oxygenation and flow in the new tissue appeared to be independent of when it was formed, but instead showed noticeable dependence on the phase of wound healing. This PAM study provides new insights into the structural, functional, and metabolic changes associated with vascular adaptation during wound healing and suggests that the timing and target of vascular treatments for wound healing may affect the outcomes.

In silico optimization of heparin microislands in microporous annealed particle hydrogel for endothelial cell migration.

Biomaterials capable of generating growth factor gradients have shown success in guiding tissue regeneration, as growth factor gradients are a physiologic driver of cell migration. Of particular importance, a focus on promoting endothelial cell migration is vital to angiogenesis and new tissue formation. Microporous Annealed Particle (MAP) scaffolds represent a unique niche in the field of regenerative biomaterials research as an injectable biomaterial with an open porosity that allows cells to freely migrate independent of material degradation. Recently, we have used the MAP platform to heterogeneously include spatially isolated heparin-modified microgels (heparin microislands) which can sequester growth factors and guide cell migration. In in vitro sprouting angiogenesis assays, we observed a parabolic relationship between the percentage of heparin microislands and cell migration, where 10% heparin microislands had more endothelial cell migration compared to 1% and 100%. Due to the low number of heparin microisland ratios tested, we hypothesize the spacing between microgels can be further optimized. Rather than use purely empirical methods, which are both expensive and time intensive, we believe this challenge represents an opportunity to use computational modeling. Here we present the first agent-based model of a MAP scaffold to optimize the ratio of heparin microislands. Specifically, we develop a two-dimensional model in Hybrid Automata Library (HAL) of endothelial cell migration within the unique MAP scaffold geometry. Finally, we present how our model can accurately predict cell migration trends in vitro, and these studies provide insight on how computational modeling can be used to design particle-based biomaterials. STATEMENT OF SIGNIFICANCE: While the combination of experimental and computational approaches is increasingly being used to gain a better understanding of cellular processes, their combination in biomaterials development has been relatively limited. Heparin microislands are spatially isolated heparin microgels; when located within a microporous annealed particle (MAP) scaffold, they can sequester and release growth factors. Importantly, we present the first agent-based model of MAP scaffolds to optimize the ratio of heparin microislands within the scaffold to promote endothelial cell migration. We demonstrate this model can accurately predict trends in vitro, thus opening a new avenue of research to aid in the design of MAP scaffolds.

Biophysical quantification of reorganization dynamics of human pancreatic islets during co-culture with adipose-derived stem cells.

Islet transplantation is a potential therapy for type 1 diabetes, but it is expensive due to limited pancreas donor numbers and the variability in islet quality. The latter is often addressed by co-culture of harvested islets with stem cells to promote in vitro remodeling of their basement membrane and enable expression of angiogenic factors for enhancing vascularization. However, given the heterogeneity in islet size, shape and function, there is a need for metrics to assess the reorganization dynamics of single islets over the co-culture period. Based on shape-evolution of individual multi-cell aggregates formed during co-culture of human islets with adipose derived stem cells and the pressures required for their bypass through microfluidic constrictions, we present size-normalized biomechanical metrics for monitoring the reorganization. Aggregates below a threshold size exhibit faster reorganization, as evident from rise in their biomechanical opacity and tightening of their size distribution, but this size threshold increases over culture time to include a greater proportion of the aggregates. Such biomechanical metrics can quantify the subpopulation of reorganized aggregates by distinguishing them versus those with incomplete reorganization, over various timepoints during the co-culture.

Vivarium: an interface and engine for integrative multiscale modeling in computational biology.

MOTIVATION: This article introduces Vivarium-software born of the idea that it should be as easy as possible for computational biologists to define any imaginable mechanistic model, combine it with existing models and execute them together as an integrated multiscale model. Integrative multiscale modeling confronts the complexity of biology by combining heterogeneous datasets and diverse modeling strategies into unified representations. These integrated models are then run to simulate how the hypothesized mechanisms operate as a whole. But building such models has been a labor-intensive process that requires many contributors, and they are still primarily developed on a case-by-case basis with each project starting anew. New software tools that streamline the integrative modeling effort and facilitate collaboration are therefore essential for future computational biologists. RESULTS: Vivarium is a software tool for building integrative multiscale models. It provides an interface that makes individual models into modules that can be wired together in large composite models, parallelized across multiple CPUs and run with Vivarium's discrete-event simulation engine. Vivarium's utility is demonstrated by building composite models that combine several modeling frameworks: agent-based models, ordinary differential equations, stochastic reaction systems, constraint-based models, solid-body physics and spatial diffusion. This demonstrates just the beginning of what is possible-Vivarium will be able to support future efforts that integrate many more types of models and at many more biological scales. AVAILABILITY AND IMPLEMENTATION: The specific models, simulation pipelines and notebooks developed for this article are all available at the vivarium-notebooks repository: https://github.com/vivarium-collective/vivarium-notebooks. Vivarium-core is available at https://github.com/vivarium-collective/vivarium-core, and has been released on Python Package Index. The Vivarium Collective (https://vivarium-collective.github.io) is a repository of freely available Vivarium processes and composites, including the processes used in Section 3. Supplementary Materials provide with an extensive methodology section, with several code listings that demonstrate the basic interfaces. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Perfusable cell-laden micropatterned hydrogels for delivery of spatiotemporal vascular-like cues to tissues.

The integration of vasculature at physiological scales within 3D cultures of cell-laden hydrogels for the delivery of spatiotemporal mass transport, chemical and mechanical cues, is a stepping-stone towards building in vitro tissue models that recapitulate in vivo cues. To address this challenge, we present a versatile method to micropattern adjoining hydrogel shells with a perfusable channel or lumen core, for enabling facile integration with fluidic control systems, on one hand, and to cell-laden biomaterial interfaces, on the other hand. This microfluidic imprint lithography methodology utilizes the high tolerance and reversible nature of the bond alignment process to lithographically position multiple layers of imprints within a microfluidic device for sequential filling and patterning of hydrogel lumen structures with single or multiple shells. Through fluidic interfacing of the structures, the ability to deliver physiologically relevant mechanical cues for recapitulating cyclical stretch on the hydrogel shell and shear stress on endothelial cells in the lumen are validated. We envision application of this platform for recapitulation of the bio-functionality and topology of micro-vasculatures, alongside the ability to deliver transport and mechanical cues, as needed for 3D culture to construct in vitro tissue models.