Biology subjects

Glazier, J. A.

Publications and source records attributed to Glazier, J. A..

18 recordsLinked to original sources

bioRxiv2026

A Simulation of Semi-Infectious Particles and Genome Complementation Reproduces Interferon Response by Respiratory Epithelial Cells in vitro during Influenza A Virus Infection

In the respiratory epithelium, interferon (IFN)-induced antiviral resistance acts as a defense against infection. Influenza A viruses (IAVs) have evolved multiple strategies to counteract these defenses, including expression of the viral protein NS1, which inhibits both IFN production and the IFN-mediated transcription of Interferon Stimulated Genes (ISG) in infected cells. However, experiments show that this inhibition is imperfect, especially at a low multiplicity of infection (MOI). One hypothesis to describe this phenomenon relies on the presence of Semi-infectious Particles (SIPs) that fail to express NS1. In this scenario, the IFN response is incompletely suppressed at low MOI, while it is successfully inhibited at high MOI because most cells are infected by multiple virions, allowing complementation to rescue NS1 expression. To test this hypothesis, we developed a computer simulation that models viral gene defects and complementation. We compared the model outputs with in vitro experiments at different MOIs. To assess inter-host reproducibility and calibrate the model parameters, we measured IFN levels and viral load over time in bronchial epithelial cell cultures from five human donors. We observed no statistically significant heterogeneity in IFN response or virus production between donors, and the calibrated simulation fits the experimental time series for IFN and viral load. Consistent with literature (1,2), the model predicted higher IFN levels at low MOI than at high MOI. Finally, simulations of IFN treatment applied before and during infection showed reduced viral load, in agreement with our experiments. Increasing the viral genome defect rate above the experimentally estimated rate increased IFN levels and reduced viral load. High MOI simulations showed lower cumulative IFN levels, while NS1 knockout recovered high IFN levels. These results demonstrate the ability of mechanistic models of viral dynamics to predict the innate immune response of epithelial cells during viral infection. Author SummaryRespiratory viruses such as influenza A are highly infectious and pose significant challenges for the human immune system. Through laboratory experiments and computer simulations, we investigated how cells in the respiratory epithelium defend themselves and their neighbors against infection. Using cells collected from different donors, we generated 3-dimensional cell cultures that mimic human airways and measured how they respond to IAV. When a tissue was initially exposed to a small amount of virus, cells could successfully slow or stop the spread of the infection. This phenomenon is hypothesized to be due in part to the high error rate in IAV replication, resulting in many viral particles that are not fully functional. We recapitulated this experimental result with our computational model, validating the model design and parameter estimates. We then simulated a scenario in which cells were pre-treated with interferon, a protective cytokine important to early immune response, and showed that this pre-treatment could successfully limit infection. Laboratory experiments subsequently confirmed this predicted behavior. The computational model reproduced key observations across infection conditions and identified nonfunctional viral particles as important drivers of the early immune response.

systems biology↗

bioRxiv2025

V-Cornea:A computational model of corneal epithelium homeostasis, injury, and recovery

PurposeTo develop a computational model that addresses limitations in current ocular irritation assessment methods, particularly regarding long-term effects, and recovery patterns following chemical exposure or trauma to the cornea. MethodsV-Cornea is an agent-based computer simulation implemented in CompuCell3D that models corneal epithelial homeostasis and injury response. The model incorporates biologically-inspired rules governing cell behaviors (proliferation, differentiation, death) and key signaling pathways including Epidermal Growth Factor (EGF), translating cell-level behaviors to tissue-level outcomes (in vitro to in vivo extrapolation, IVIVE). ResultsV-Cornea successfully reproduces corneal epithelial architecture and maintains tissue homeostasis over extended simulated periods. Following simulated trauma or toxicant exposure, the model accurately predicts healing timeframes of 3-5 days for slight and mild injuries. For moderate injuries with basement membrane disruption, the model demonstrates longer recovery times and emergent dynamic structural disorganization that mimics recurrent corneal erosions, providing mechanistic insights into these pathological conditions. ConclusionsV-Corneas modular CompuCell3D implementation is easily extensible to incorporate additional recovery and injury mechanisms. Future versions will include more realistic basement membrane dynamics and explicit representation of stromal tissue and immune response to improve predictivity for moderate injuries. This virtual-tissue approach shows potential not only for toxicological assessments but also for drug discovery and therapy optimization by providing a platform to test interventions and predict outcomes across various injury scenarios.

systems biology↗

bioRxiv2025

Modeling Epithelial Morphogenesis and Cell Rearrangement during Zebrafish Epiboly: Tissue Deformation, Cell-Cell Coupling, and the Mechanical Response to Stress

Morphogenesis in early development involves complex and extreme deformations in response to intra- and intercellular forces. Zebrafish epiboly, the spreading of the blastoderm to cover and engulf the large yolk cell, is a key early event that sets the stage for the establishment of the body plan, but the way the forces driving expansion are generated and mediated is poorly understood. The enveloping layer (EVL), the thin squamous outer epithelium of the blastoderm, plays a central role. Forces generated in the yolk cell are transmitted through tight junctions to the marginal EVL cells, and then propagate through the rest of the EVL. To understand mechanisms of force generation and transduction during epiboly, we first need a mechanical model of the EVL capable of responding to such forces and undergoing the drastic deformation of epiboly. The expanding EVL more than doubles its surface area and experiences significant shear as it deforms from a thin cap at one pole to become a complete sphere, necessarily requiring extensive internal rearrangement. We constructed an agent-based model of the EVL and its response to exogenous forces using the center-based simulation framework, Tissue Forge. Our model captures the large viscoelastic deformation of the EVL by cell rearrangement, and incorporates algorithmic strategies to accommodate these dynamic changes while maintaining tissue cohesion. Features observed in living embryos, such as the straightening of the initially ragged leading edge, also emerge in the model. We identified two key components required for realistic epiboly in the model: first, a mechanism to enable tissue remodeling by cell rearrangement without tearing the tissue, and second, a negative feedback on the forces driving EVL expansion, to regulate and synchronize the advancement of the EVL margin. We discuss the implications of these findings for the behavior of living EVL and the mechanisms that drive epiboly.

developmental biology↗

bioRxiv2023

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 StatementWe 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

developmental biology↗

bioRxiv2023

PhenoCellPy: A Python package for biological cell behavior modeling

PhysiCell, a mechanistic center-based agent-based modeling framework, describes sequences of cell behaviors in a very elegant way with its phenotype submodel. However, its phenotypes are not usable in other frameworks, and the creation of a new phenotype is not straight forward. PhenoCellPy is an open-source Python package that implements PhysiCells phenotypes and their methods in a general way, that is easily extended and embedded into other python-based models. PhenoCellPy defines methods for modeling sequences of cell behaviors and states (e.g., the cell cycle, or the Phases of cellular necrosis). PhenoCellPy defines Python classes for the Cell Volume (which it subdivides between the cytoplasm and nucleus) and its evolution, the state of the cell and the behaviors the cell displays in each state (called the Phase), and the sequence of behaviors (called the Phenotype). PhenoCellPys can extend existing modeling frameworks as an embedded model. It integrates with the frameworks by defining the cell states (phases), signaling when a state change occurs, if division occurs, and by receiving information from the framework (e.g., how much interferon the cell is exposed to). PhenoCellPy can function with any python-based modeling framework that supports Python 3, NumPy and SciPy.

bioinformatics↗

bioRxiv2022

A spatialised agent-based model of NOTCH signalling pathway in Endothelial Cells predicts emergent heterogeneity due to continual dynamic phenotypic adjustments.

Vascular Endothelial Cells (EC) plasticity is key to homeostasis and its disruption is a hallmark of diseases such as cancer, atherosclerosis, and diabetes. The EC lineage has evolved to address in parallel sensor and actuator functions. This ability is reflected in remarkable phenotypical heterogeneity of EC across different tissues, within the same tissue, and within the same vascular bed as demonstrated by single cell image analysis and transcriptomics studies. However, how the molecular signalling dynamics in EC could generate and maintain such heterogeneity in different contexts is still largely unexplored. Recently we reported that confluent EC have spatially heterogeneous NOTCH signalling pathway (NSP) levels in vitro as confirmed from analysis of available OMICS databases. Here, we show that spatial heterogeneity of NSP levels is a feature of aortic murine endothelia in vivo and recapitulated by human EC in culture despite absence of signalling from mural cells. We study lateral induction and inhibition, cis-interactions and signalling, and target genes autoregulation in NSP. Using mathematical models and experimental observations we report that NSP dynamics can generate stable, periodic, and asynchronous oscillations of the NSP target HES1. Importantly, we observe that cell contact dependent NSP signal oscillations is the most likely parsimonious mechanistic hypothesis justifying observed spatial heterogeneity in endothelia. We propose that NSP is sufficient to enable individual EC in monolayers to acquire different phenotypes dynamically explaining robustness of quiescent endothelia in performing parallel functions.

cell biology↗

bioRxiv2022

Reconstitution of renal cyst formation in 3D culture reveals a role for AMOT and Yap1 in determining cyst size.

Despite substantial progress in identifying and understanding causative mutations in autosomal dominant polycystic kidney disease (ADPKD), little is known about subsequent cellular events leading to cyst formation. In prior studies we reported that Cadherin 8, a type II Cadherin, expression is sufficient to induce cyst emergence from HK-2 cells grown as tubule arrays in collagen matrix (1). However, emergent cysts did not exhibit the luminal enlargement observed in ADPKD. In this communication, we reconstitute cyst emergence with consequent cyst lumen expansion in 3D culture by stable co-expression of Cadherin 8 in combination with a constitutively active mutant of YAP1, the key effector of the HIPPO pathway. Specifically, immortalized cells derived from ADPKD cyst epithelia formed cysts with substantially larger lumen sizes when transduced with YAP1-5SA. Conversely, expression of the YAP1 inhibitor, AMOTL1, in these cells resulted in their forming cysts with smaller lumens than control cells. Our data show that cyst formation results from a sequential two-step process consisting of cyst initiation and subsequent cyst expansion. Taken together, cyst initiation induced by Cadherin 8 expression is proposed to result from decreased cell-cell adhesion while cyst expansion is driven by increased Yap1 activity.

cell biology↗

bioRxiv2021

Mechanical influences on in silico tumor evolution

Experimental insight and conceptual understanding of tumor growth are steadily growing and leading to new therapeutic interventions. Experiments and clinical studies are able to link single-cell properties to macroscopic tumor attributes. The development of cellular subpopulations in heterogeneous tumors can be understood as an evolutionary system with different cell types competing over both space and nutrients. However, to predict the growth trajectory and development of a tumor, fitness and trade-offs of cell properties in the context of the surroundings are required and often inaccessible. The optimum of the evolutionary trajectory provides a target for intervention, but can mostly not be identified. We propose that the optimal value of cellular properties is influenced by the tumor surrounding. Computational multiscale-modeling of tissue enables the observation of the trajectory of each cell while modeling the tumor surrounding. We model a 3D spheroid tumor and the fitness of individual cells and the evolutionary behavior of the tumor are quantified and linked to global parameters. Cell-cell adhesion and cell motility are two important mechanical properties for cell development and used as free parameters. Mechanical properties alone are able to drive the tumor towards low adhesion.We implement a dynamically changing nutrient surrounding representing the fluctuating blood-supply through blood vessel collapse and angiogenesis. We find that the evolutionary speed depends on the frequency of the fluctuations. We identify a frequency domain in which the evolutionary speed is significantly increased over a tumor with constant nutrient supply. The findings suggest that mechanically-induced fluctuations can accelerate tumor evolution. Author summaryLimited space and nutrients together with competing cell types drive an evolutionary process inside tumors. This process selects for the fittest cell types and optimizes the growing behavior for its local surroundings. An expanding tumor exerts mechanical forces on its cells and its surroundings, leading to a fluctuating nutrient supply through collapsing blood vessels. Here, we observe the influence of a dynamically changing surrounding on the evolutionary behavior of heterogeneous tumors in a high-resolution computational model. We find that the evolutionary speed depends on the frequency of the fluctuations and a fitness advantage of low-adhesion cells.

cancer biology↗

bioRxiv2021

Multicellular Spatial Model of RNA Virus Replication and Interferon Responses Reveals Factors Controlling Plaque Growth Dynamics

Respiratory viruses present major health challenges, as evidenced by the 2009 influenza pandemic and the ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Severe RNA virus respiratory infections often correlate with high viral load and excessive inflammation. Understanding the dynamics of the innate immune response and its manifestation at the cell and tissue levels are vital to understanding the mechanisms of immunopathology and developing improved, strain independent treatments. Here, we present a novel spatialized multicellular spatial computational model of two principal components of tissue infection and response: RNA virus replication and type-I interferon mediated antiviral response to infection within lung epithelial cells. The model is parameterized using data from influenza virus infected cell cultures and, consistent with experimental observations, exhibits either linear radial growth of viral plaques or arrested plaque growth depending on the local concentration of type I interferons. Modulating the phosphorylation of STAT or altering the ratio of the diffusion constants of interferon and virus in the cell culture could lead to plaque growth arrest. The dependence of arrest on diffusion constants highlights the importance of developing validated spatial models of cytokine signaling and the need for in vitro experiments to measure these diffusion constants. Sensitivity analyses were performed under conditions creating both continuous plaque growth and arrested plaque growth. Findings suggest that plaque growth and cytokine assay measurements should be collected during arrested plaque growth, as the model parameters are significantly more sensitive and more likely to be identifiable. The models metrics replicate experimental immunostaining imaging and titer based sampling assays. The model is easy to extend to include SARS-CoV-2-specific mechanisms as they are discovered or to include as a component linking epithelial cell signaling to systemic immune models. Author SummaryCOVID-19 is possibly the defining healthcare crisis of the current generation, with tens of millions of global cases and more than a million reported deaths. Respiratory lung infections form lesions in the lungs, whose number and size correlate with severity of illness. In some severe cases, the disease triggers a severe inflammatory condition known as cytokine storm. Given the complexity of the immune system, computational modeling is needed to link molecular signaling at the site of inflection to the signaling impact on the overall immune system, ultimately revealing how severe inflammatory conditions may emerge. Here, we created a computational model of the early stages of infection that simulates lung cells infected with RNA viruses, such those responsible for COVID-19 and influenza, to help explore how the disease forms viral plaques, an in vitro analog to lesion growth in the lung. Our model recapitulates in vitro observations that pretreatment of biological signaling molecules called with type-I interferons, which are currently being evaluated for treatment of COVID-19. Analyzing the model, we, can stop viral plaque growth. We found that enhancing certain aspects of the innate immune system, such as the JAK/STAT pathway, may be able to stop viral plaque growth, suggesting molecules involved in this pathway as possible drug candidates. Quantifying the parameters needed to model interferon signaling and viral replication, experiments should be performed under conditions that inhibit viral growth, such as pretreating cells with interferon. We present a computational framework that is essential to constructing larger models of respiratory infection induced immune responses, can be used to evaluate drugs and other medical interventions quickly, cheaply, and without the need for animal testing during the initial phase, and that defines experiments needed to improve our fundamental understanding of the mechanisms regulating the immune response.

systems biology↗

bioRxiv2021

A Multiscale Multicellular Spatiotemporal Model of Local Influenza Infection and Immune Response

Respiratory viral infections pose a serious public health concern, from mild seasonal influenza to pandemics like those of SARS-CoV-2. Spatiotemporal dynamics of viral infection impact nearly all aspects of the progression of a viral infection, like the dependence of viral replication rates on the type of cell and pathogen, the strength of the immune response and localization of infection. Mathematical modeling is often used to describe respiratory viral infections and the immune response to them using ordinary differential equation (ODE) models. However, ODE models neglect spatially-resolved biophysical mechanisms like lesion shape and the details of viral transport, and so cannot model spatial effects of a viral infection and immune response. In this work, we develop a multiscale, multicellular spatiotemporal model of influenza infection and immune response by combining non-spatial ODE modeling and spatial, cell-based modeling. We employ cellularization, a recently developed method for generating spatial, cell-based, stochastic models from non-spatial ODE models, to generate much of our model from a calibrated ODE model that describes infection, death and recovery of susceptible cells and innate and adaptive responses during influenza infection, and develop models of cell migration and other mechanisms not explicitly described by the ODE model. We determine new model parameters to generate agreement between the spatial and original ODE models under certain conditions, where simulation replicas using our model serve as microconfigurations of the ODE model, and compare results between the models to investigate the nature of viral exposure and impact of heterogeneous infection on the time-evolution of the viral infection. We found that using spatially homogeneous initial exposure conditions consistently with those employed during calibration of the ODE model generates far less severe infection, and that local exposure to virus must be multiple orders of magnitude greater than a uniformly applied exposure to all available susceptible cells. This strongly suggests a prominent role of localization of exposure in influenza A infection. We propose that the particularities of the microenvironment to which a virus is introduced plays a dominant role in disease onset and progression, and that spatially resolved models like ours may be important to better understand and more reliably predict future health states based on susceptibility of potential lesion sites using spatially resolved patient data of the state of an infection. We can readily integrate the immune response components of our model into other modeling and simulation frameworks of viral infection dynamics that do detailed modeling of other mechanisms like viral internalization and intracellular viral replication dynamics, which are not explicitly represented in the ODE model. We can also combine our model with available experimental data and modeling of exposure scenarios and spatiotemporal aspects of mechanisms like mucociliary clearance that are only implicitly described by the ODE model, which would significantly improve the ability of our model to present spatially resolved predictions about the progression of influenza infection and immune response.

systems biology↗

bioRxiv2021

Generating Agent-Based Multiscale Multicellular Spatiotemporal Models from Ordinary Differential Equations of Biological Systems, with Applications in Viral Infection

The biophysics of an organism span scales from subcellular to organismal and include spatial processes like diffusion of molecules, cell migration, and flow of intravenous fluids. Mathematical biology seeks to explain biophysical processes in mathematical terms at, and across, all relevant spatial and temporal scales. While non-spatial, ordinary differential equation (ODE) models are often used and readily calibrated to experimental data, they do not explicitly represent spatial and stochastic features of a biological system, limiting their insights and applications. Spatial models describe biological systems with spatial information but are mathematically complex and computationally expensive, which limits the ability to calibrate and deploy them. In this work we develop a formal method for deriving cell-based, spatial, multicellular models from ODE models of population dynamics in biological systems, and vice-versa. We provide examples of generating spatiotemporal, multicellular models from ODE models of viral infection and immune response. In these models the determinants of agreement of spatial and non-spatial models are the degree of spatial heterogeneity in viral production and rates of extracellular viral diffusion and decay. We show how ODE model parameters can implicitly represent spatial parameters, and cell-based spatial models can generate uncertain predictions through sensitivity to stochastic cellular events, which is not a feature of ODE models. Using our method, we can test ODE models in a multicellular, spatial context and translate information to and from non-spatial and spatial models, which help to employ spatiotemporal multicellular models using calibrated ODE model parameters, investigate objects and processes implicitly represented by ODE model terms and parameters, and improve the reproducibility of spatial, stochastic models. We hope to employ our method to generate new ODE model terms from spatiotemporal, multicellular models, recast popular ODE models on a cellular basis, and generate better models for critical applications where spatial and stochastic features affect outcomes. Statement of SignificanceOrdinary differential equations (ODEs) are widely used to model and efficiently simulate multicellular systems without explicit spatial information, while spatial models permit explicit spatiotemporal modeling but are mathematically complicated and computationally expensive. In this work we develop a method to generate stochastic, agent-based, multiscale models of multicellular systems with spatial resolution at the cellular level according to non-spatial ODE models. We demonstrate how to directly translate model terms and parameters between ODE and spatial models and apply non-spatial model terms to boundary conditions using examples of viral infection modeling, and show how spatial models can interrogate implicitly represented biophysical mechanisms in non-spatial models. We discuss strategies for co-developing spatial and non-spatial models and reconciling disagreements between them.

biophysics↗

bioRxiv2020

Cellular mechanisms of chick limb bud morphogenesis

Although some of the molecular pathways involved in limb bud morphogenesis have been identified, the cellular basis of the process is not yet understood. Proposed cell behaviours include active cell migration and oriented cell division, but ultimately, these questions can only be resolved by watching individual mesenchymal cells within a completely normal developmental context. We developed a minimally-invasive in ovo two-photon technique, to capture high quality time-lapse sequences up to 100 microns deep in the unperturbed growing chick limb bud. Using this technique, we characterized cell shapes and other oriented behaviours throughout the limb bud, and found that cell intercalation drives tissue movements, rather than oriented cell divisions or migration. We then developed a 3D cell-based computer simulation of morphogenesis, in which cellular extensions physically pull cells towards each other, with directional bias controlled by molecular gradients from the ectoderm (Wnts) and the Apical Ectodermal Ridge (FGFs). We defined the initial and target shapes of the chick limb bud in 3D by OPT scanning, and explored which orientations of mesenchymal intercalation correctly explain limb morphogenesis. The model made a couple of predictions: Firstly, that elongation can only be explained when cells intercalate along the direction towards the nearest ectoderm. This produces a general convergence of tissue towards the central proximo-distal (PD) axis of the limb, and a resultant extension of the tissue along the PD axis. Secondly, the correct in silico morphology can only be achieved if the contractile forces of mesenchymal cells in the very distal region (under the Apical Ectodermal Ridge) have shorter life times than in the rest of the limb bud, effectively making the tissue more fluid by augmenting the rate of cell rearrangement. We argue that this less-organised region of mesenchyme is necessary to prevent PD-oriented intercalation events in the distal tip that would otherwise inhibit outgrowth.

developmental biology↗

bioRxiv2020

Transcriptogram analysis reveals relationship between viral titer and gene sets responses during Corona-virus infection.

To understand the difference between benign and severe outcomes after Coronavirus infection, we urgently need ways to clarify and quantify the time course of tissue and immune responses. Here we re-analyze 72-hour time-series microarrays generated in 2013 by Sims and collaborators for SARS-CoV-1 in vitro infection of a human lung epithelial cell line. Transcriptograms, a Bioinformatics tool to analyze genome-wide gene expression data, allow us to define an appropriate context-dependent threshold for mechanistic relevance of gene differential expression. Without knowing in advance which genes are relevant, classical analyses detect every gene with statistically-significant differential expression, leaving us with too many genes and hypotheses to be useful. Using a Transcriptogram-based top-down approach, we identified three major, differentially-expressed gene sets comprising 219 mainly immune-response-related genes. We identified timescales for alterations in mitochondrial activity, signaling and transcription regulation of the innate and adaptive immune systems and their relationship to viral titer. At the individual-gene level, EGR3 was significantly upregulated in infected cells. Similar activation in T-cells and fibroblasts in infected lung could explain the T-cell anergy and eventual fibrosis seen in SARS-CoV-1 infection. The methods can be applied to RNA data sets for SARS-CoV-2 to investigate the origin of differential responses in different tissue types, or due to immune or preexisting conditions or to compare cell culture, organoid culture, animal models, and human-derived samples.

bioinformatics↗

bioRxiv2020

A Modular Framework for Multiscale Spatial Modeling of Viral Infection and Immune Response in Epithelial Tissue

Simulations of tissue-specific effects of primary acute viral infections like COVID-19 are essential for understanding disease outcomes and optimizing therapies. Such simulations need to support continuous updating in response to rapid advances in understanding of infection mechanisms, and parallel development of components by multiple groups. We present an open-source platform for multiscale spatiotemporal simulation of an epithelial tissue, viral infection, cellular immune response and tissue damage, specifically designed to be modular and extensible to support continuous updating and parallel development. The base simulation of a simplified patch of epithelial tissue and immune response exhibits distinct patterns of infection dynamics from widespread infection, to recurrence, to clearance. Slower viral internalization and faster immune-cell recruitment slow infection and promote containment. Because antiviral drugs can have side effects and show reduced clinical effectiveness when given later during infection, we studied the effects on progression of treatment potency and time-of-first treatment after infection. In simulations, even a low potency therapy with a drug which reduces the replication rate of viral RNA greatly decreases the total tissue damage and virus burden when given near the beginning of infection. Many combinations of dosage and treatment time lead to stochastic outcomes, with some simulation replicas showing clearance or control (treatment success), while others show rapid infection of all epithelial cells (treatment failure). Thus, while a high potency therapy usually is less effective when given later, treatments at late times are occasionally effective. We illustrate how to extend the platform to model specific virus types (e.g., hepatitis C) and add additional cellular mechanisms (tissue recovery and variable cell susceptibility to infection), using our software modules and publicly-available software repository. Author summaryThis study presents an open-source, extensible, multiscale platform for simulating viral immune interactions in epithelial tissues, which enables the rapid development and deployment of sophisticated models of viruses, infection mechanisms and tissue types. The model is used to investigate how potential treatments influence disease progression. Simulation results suggest that drugs which interfere with virus replication (e.g., remdesivir) yield substantially better infection outcomes when administered prophylactically even at very low doses than when used at high doses as treatment for an infection that has already begun.

bioengineering↗

bioRxiv2020

Development of a Coupled Simulation Toolkit for Computational Radiation Biology Based on Geant4 and CompuCell3D

Understanding and designing clinical radiation therapy is one of the most important areas of state-of-the-art oncological treatment regimens. Decades of research have gone into developing sophisticated treatment devices and optimization protocols for schedules and dosages. In this paper, we presented a comprehensive computational platform that facilitates building of the sophisticated multi-cell-based model of how radiation affects the biology of living tissue. We designed and implemented a coupled simulation method, including a radiation transport model, and a cell biology model, to simulate the tumor response after irradiation. The radiation transport simulation was implemented through Geant4 which is an open-source Monte Carlo simulation platform that provides many flexibilities for users, as well as low energy DNA damage simulation physics, Geant4-DNA. The cell biology simulation was implemented using CompuCell3D (CC3D) which is a cell biology simulation platform. In order to couple Geant4 solver with CC3D, we developed a "bridging" module that extracts tumor cellular geometry of the CC3D simulation (including specification of the individual cells) and ported it to the Geant4 for radiation transport simulation. The cell dose and cell DNA damage distribution in multicellular system were obtained using Geant4. The tumor response was simulated using cell-based tissue models based on CC3D. By merging two powerful and widely used modeling platforms, CC3D and Geant4, we delivered a novel tool that can give us the ability to simulate the dynamics of biological tissue in the presence of ionizing radiation, which provides a powerful framework for quantifying the biological consequences of radiation therapy. The developed tool has an advantage on that it has strong extensibility due to the exploitability of two modeling platforms. In this introductory methods paper, we described our modeling platform in detail and showed how it can be applied to study the application of radiotherapy to a vascularized tumor.

biophysics↗

bioRxiv2020

Rapid community-driven development of a SARS-CoV-2 tissue simulator

The 2019 novel coronavirus, SARS-CoV-2, is a pathogen of critical significance to international public health. Knowledge of the interplay between molecular-scale virus-receptor interactions, single-cell viral replication, intracellular-scale viral transport, and emergent tissue-scale viral propagation is limited. Moreover, little is known about immune system-virus-tissue interactions and how these can result in low-level (asymptomatic) infections in some cases and acute respiratory distress syndrome (ARDS) in others, particularly with respect to presentation in different age groups or pre-existing inflammatory risk factors. Given the nonlinear interactions within and among each of these processes, multiscale simulation models can shed light on the emergent dynamics that lead to divergent outcomes, identify actionable "choke points" for pharmacologic interventions, screen potential therapies, and identify potential biomarkers that differentiate patient outcomes. Given the complexity of the problem and the acute need for an actionable model to guide therapy discovery and optimization, we introduce and iteratively refine a prototype of a multiscale model of SARS-CoV-2 dynamics in lung tissue. The first prototype model was built and shared internationally as open source code and an online interactive model in under 12 hours, and community domain expertise is driving regular refinements. In a sustained community effort, this consortium is integrating data and expertise across virology, immunology, mathematical biology, quantitative systems physiology, cloud and high performance computing, and other domains to accelerate our response to this critical threat to international health. More broadly, this effort is creating a reusable, modular framework for studying viral replication and immune response in tissues, which can also potentially be adapted to related problems in immunology and immunotherapy.

systems biology↗

bioRxiv2020

Computational Modelling of Cell Movements and Aggregations during Kidney Organogenesis

During early kidney organogenesis, nephron progenitor (NP) cells move from the tip to the corner region of the ureteric bud (UB) branches in order to form the pretubular aggregate, the early structure giving rise to nephron formation. Chemotaxis and cell-cell adhesion differences are believed to drive cell patterning during this critical period of organogenesis, but the spatiotemporal organization of this process is incompletely understood. We applied a Cellular Potts model to explore to how these processes contribute to directed cell movement and aggregation. Model parameters were estimated based on fitting to experimental data obtained in ex vivo kidney explant and dissociation-reaggregation organoid culture studies. Our simulations indicated that optimal enrichment and aggregation of NP cells in the UB corner niche requires chemoattractant secretion from both the UB epithelial cells and the NP cells themselves, as well as differences in cell-cell adhesion energies. Furthermore, NP cells were observed, both experimentally and by modelling, to move at higher speed in the UB corner as compared to the tip region where they originated. The existence of different cell speed domains along the UB was confirmed using self-organizing map analysis. In summary, we demonstrated the suitability of a Cellular Potts Model approach to simulate cell movement and patterning during early nephrogenesis. Further refinement of the model should allow us to recapitulate the effects of developmental changes of cell phenotypes and molecular crosstalk during organ development. Author SummaryThe emergence of tissue patterns during vertebrate development is a major interest of both experimental research and biocomputational modelling. In this study, we established a Cellular Potts Model to explore cellular processes during early kidney development. The goal was to elucidate movements and aggregations of nephron progenitor cells. These precursor cells derive from mesenchymal cells around the ureteric buds and eventually form the epithelial structure of the nephron. Moreover, we wanted to explore computationally the mechanisms how these cells segregate from metanephric mesenchyme and move towards the location where the nephron will be formed. Utilizing the Compucell3D simulation software, we developed a model which assumes that nephron progenitor movement and aggregation is governed by only two mechanisms, i.e. cell-cell adhesion differences between cell types and nephron progenitor cell chemotaxis in response to chemoattractant secretion from two sources. These sources were either the epithelial cells of a static ureteric bud and/or the nephron progenitor cells themselves. The simulations indicated faster average cell speeds near the ureteric bud corner, the target region of cell movement and aggregation, and slower speeds near the place of origin, the tip of ureteric bud. The results were validated by comparison of the model predictions with experimental data from two ex vivo embryonic kidney models and a computational optimization protocol.

systems biology↗

bioRxiv2019

A Mechanical Model of Early Somite Segmentation

The clock-and-wavefront model (CW) hypothesizes that the formation of somites in vertebrate embryos results from the interplay of molecular oscillations with a wave traveling along the body axis. This model however does not explain how molecular information is interpreted by cells to modulate their rearrangement into somites. Here we performed Scanning Electron Microscopy (SEM) on the pre-somitic mesoderm (PSM) of chicken embryos at stages 11-12 to describe in detail the cell shape changes occurring along the axis of the PSM. This reveals a wave of epithelialization of the dorsal PSM that precedes somite segmentation. Signs of spatially periodic apical constriction appear in this layer starting at least 3-4 somite lengths caudal to the most recently formed somite. The sizes of these clusters correspond to the typical diameter of chicken somites. We propose that a mechanical instability process leads to the separation of cells into these structures and positions the future inter-somite boundaries. We present a model in which a wave of apical constriction leads to increasing tension and periodic failure of adhesion junctions within the dorsal epithelial layer of the PSM, thus positioning somite boundaries. This model can produce spatially periodic segments whose size depends on the speed of the contraction wave (W) and the rate of increase of apical contractility ({Lambda}). The {Lambda}/W ratio determines whether this mechanism produces spatially and temporally regular or irregular segments, and whether segment sizes increase with the wave speed (scaling) as in the CW model. We discuss the limitations of a purely mechanical model of somite segmentation and the role of biomechanics along with CW during somitogenesis.

developmental biology↗