Biology subjects

Sego, T. J.

Publications and source records attributed to Sego, T. J..

10 recordsLinked to original sources

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↗

bioRxiv2024

Injury-Specific Muscle Regeneration: A Computational Blueprint for Cellular and Cytokine Drivers

Skeletal muscle regeneration is crucial for maintaining muscle health and mobility, making it a key research focus. Common experimental models of muscle injury used to study this process in vivo include cardiotoxin injury (CTX), freeze-induced (FI) injury, and eccentric contraction (EC) injury. While the response to injury varies across these models, these variations are often overlooked in experimental designs. The cellular dynamics throughout the time course of muscle regeneration differ significantly between these injury types, presenting challenges for both experimental investigation and literature analysis. To enhance understanding of how regeneration responses differ across injury types, we extend our previously validated computational model of skeletal muscle regeneration to simulate muscle fiber remodeling during regeneration following CTX, FI, and EC injuries. We further validate our model against multiple literature-derived metrics of regeneration for each injury type. Analysis of model outcomes reveals that recovery from each injury type is sensitive to unique combinations of cells and cytokines acting at different critical time points, exerting the greatest influence on regeneration at 28 days post-injury. Notably, the cytokines responsible for satellite cell (SSC) recruitment, proliferation, and differentiation required for regeneration vary across injury types, driven by biological redundancy and feedback mechanisms. Specifically, for EC injuries, cross-sectional area (CSA) recovery was predominantly associated with hepatocyte growth factor (HGF) and vascular endothelial growth factor A (VEGF-A) during the early stages of regeneration, with SSC dynamics playing a critical role throughout. FI injuries demonstrated a consistent reliance on HGF across all phases, with additional influences from transforming growth factor beta (TGF-{beta}), tumor necrosis factor-alpha (TNF-), monocyte chemoattractant protein-1 (MCP-1), and SSC dynamics in later stages. CTX injuries showed early dependence on TGF-{beta}, with significant contributions from SSC, TNF-, VEGF-A, and fibroblast dynamics over time. Our findings emphasize the importance of considering the molecular and cellular mechanisms relevant to each injury type used in pre-clinical in vivo studies, and motivates the development of therapeutic strategies that are designed for specific injury types to optimize recovery outcomes.

systems 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

Tissue Forge: Interactive Biological and Biophysics Simulation Environment

Tissue Forge is an open-source interactive environment for particle-based physics, chemistry and biology modeling and simulation. Tissue Forge allows users to create, simulate and explore models and virtual experiments based on soft condensed matter physics at multiple scales, from the molecular to the multicellular, using a simple, consistent interface. While Tissue Forge is designed to simplify solving problems in complex subcellular, cellular and tissue biophysics, it supports applications ranging from classic molecular dynamics to agent-based multicellular systems with dynamic populations. Tissue Forge users can build and interact with models and simulations in real-time and change simulation details during execution, or execute simulations off-screen and/or remotely in high-performance computing environments. Tissue Forge provides a growing library of built-in model components along with support for user-specified models during the development and application of custom, agent-based models. Tissue Forge includes an extensive Python API for model and simulation specification via Python scripts, an IPython console and a Jupyter Notebook, as well as C and C++ APIs for integrated applications with other software tools. Tissue Forge supports installations on 64-bit Windows, Linux and MacOS systems and is available for local installation via conda. 1 Author SummaryTissue Forge is a physics-based modeling and simulation software environment for research problems in physics, chemistry and biology. Tissue Forge supports modeling at a wide range of scales, from as small as the sub-nanometer, to as large as hundreds of micrometers, using particle-based models. It provides rich features for simulation development and application at all stages of model-based research, like real-time simulation visualization and interactivity, and off-screen batch execution, rendering, and GPU acceleration. Users can employ built-in models to represent a wide variety of physical processes, like chemical reactions, fluid convection and intercellular adhesion, or define their own models for agentand rule-based modeling. Tissue Forge is open-source, free and easy to install, supports simulation development in C, C++ and Python programming languages, and can be used as integrated software or in an interactive IPython console and Jupyter Notebook. Tissue Forge also provides a dedicated space for application-specific and user-contributed modeling and simulation features, and developers are welcome to contribute their custom features for distribution in future releases.

biophysics↗

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↗

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

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↗