Biology subjects

Ford Versypt, A. N.

Publications and source records attributed to Ford Versypt, A. N..

15 recordsLinked to original sources

bioRxiv2026

Agent-Based Modeling of Idiopathic Lung Fibrosis and Mechanistic Treatments

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

systems biology↗

bioRxiv2025

Modeling and Design of Multi-layered Cylindrical Microcapsules for Intravitreal Controlled Release

Chronic diseases often require repeated oral or local administration, which can compromise patient compliance. In wet age-related macular degeneration (AMD), current therapies rely on intravitreal injections of anti-vascular endothelial growth factor agents every four to six weeks to maintain therapeutic drug levels. Controlled-release drug delivery systems offer a promising alternative by reducing injection frequency and extending drug release. In this study, we developed a continuum diffusion model to describe drug transport through porous polymeric microcapsules, implemented using the finite element method in COMSOL Multiphysics. The case study focused on cylindrical microcapsules fabricated with either a single polycaprolactone (PCL) layer or a bi-layered chitosan-PCL structure, tested at two capsule sizes and three salt leaching concentrations. Bovine serum albumin and bevacizumab were used as model drugs. Parameter estimation was performed using published release data, with a progressive fitting strategy that carried forward parameters from simpler systems into more complex designs. The model reproduced experimental release profiles across formulations and identified key transport parameters governing release dynamics, including porosity, tortuosity, and mass transfer rates. Design exploration revealed that polymer thickness was the dominant factor controlling release, while addition of the chitosan layer moderated the initial burst and extended therapeutic delivery. This framework demonstrates how computational modeling can reduce experimental burden, guide design optimization, and support the development of long-acting intravitreal drug delivery systems to treat wet AMD by linking drug release kinetics to design variables.

pharmacology and toxicology↗

bioRxiv2025

Mathematical Modeling of Bone Remodeling after Surgical Menopause

Osteoporosis is a skeletal pathology characterized by decreased bone mass and structural deterioration resulting from an imbalance in bone metabolic processes. Estrogen deficiency in postmenopausal women leads to an increased risk of osteoporosis, while women who have undergone complete oophorectomies display an even higher risk due to the sudden decrease in estrogen. Some evidence indicates that bone loss slows in the period beyond 15 years after surgery; however, there is substantial uncertainty in clinical data. To explore the effects of surgically induced menopausal transition, here we propose a mathematical model for the bone cell dynamical responses to sudden estrogen deficiency, which extends an existing model for osteoporosis due to aging and natural menopause. Using data on the key impacts observed in female mice and humans after bilateral oophorectomy, this new model considers the role of osteocytes embedded within the bone mineralized matrix in regulating osteoclastogenesis, which results in increased bone resorption after surgical menopause. The values of model parameters in natural and surgical menopause were estimated from aggregated human clinical data from existing longitudinal studies. The new model effectively captures the previously unmodeled increase in bone loss during the first 15 years post-surgical menopause and the rebound in bone mineral density in the long-term. With this model, effects of treatments on targeting osteocyte dynamics could be explored in the future.

systems biology↗

bioRxiv2024

Multi-Cellular Network Model Predicts Alterations in Glomerular Endothelial Structure in Diabetic Kidney Disease

Diabetic kidney disease (DKD) progression is often marked by early glomerular endothelial cell (GEC) dysfunction, including alterations in the fenestration size and number linked to impaired glomerular filtration. However, the cellular mechanisms regulating GEC fenestrations remain poorly understood due to limitations in existing in vitro models, challenges in imaging small fenestrations in vivo, and inconsistencies between in vitro and in vivo findings. This study used a logic-based protein-protein interaction network model with normalized Hill functions for dynamics to explore how glucose-mediated signaling dysregulation impacts fenestration dynamics in GECs. We identified key drivers of fenestration loss and size changes by incorporating signaling pathways related to actin remodeling, myosin light chain kinase, Rho-associated kinase, calcium, and VEGF and its receptor. The model predicted how hyperglycemia in diabetic mice leads to significant fenestration loss and increased size of fenestrations. We found that glycemic control in the pre-DKD stage mitigated signaling dysregulation but was less effective as DKD developed and progressed. The model suggested alternative disease intervention strategies to maintain fenestration structure integrity, such as targeting Rho-associated kinase, VEGF-A, NF{kappa}B, and actin stress fibers.

systems biology↗

bioRxiv2024

Agent-based modeling reveals impacts of cell adhesion and matrix remodeling on cancer collective cell migration phenotypes

Understanding the phenotypic transitions of cancer cells is crucial for elucidating tumor progression mechanisms, particularly the transition from a non-invasive spheroid phenotype to an invasive network phenotype. We developed an agent-based model (ABM) using Compucell3D, an open-source biological simulation software, to investigate how varying biophysical and biochemical parameters influence emerging properties of cellular communities, including cell growth, division, and migration. Our focus was on cell-cell contact adhesion and matrix remodeling effects on cancer cell migration. We simplified enzymatic remodeling of the extracellular matrix and the subsequent enhancements to cellular chemotaxis or durotaxis as a combined effect of localized cellular secretion of a chemoattractant. By varying the chemoattractant secretion rate and contact adhesion energy, we simulated their effects on cellular behavior and driving the transition from a spheroid phenotype to a network phenotype. The model serves as a digital twin for 3D cancer cell culture, simulating cancer cell growth, division, and invasion over 1 week, validated against published data. The simulations track the emergent morphological and collective phenotype changes using key metrics such as cell circularity and invasion. Our findings indicate that increased chemoattractant secretion enhances the invasiveness of the collective cells, promoting the transition to a network phenotype. Additionally, changing cell-cell contact energy from a strong cell-cell adhesion to a weak cell-cell adhesion affects the compactness of the spheroids, resulting in lower circularity and increased collective cell invasion. Our work advances the understanding of tumor progression by providing insights into the biophysical mechanisms behind invasive cancer cell phenotypic transitions.

biophysics↗

bioRxiv2024

Mathematical Modeling of Estrogen-Mediated Inflammation in the Gut and Immune System

Postmenopausal osteoporosis is a chronic inflammatory disease characterized by decreased bone mass and increased bone fracture risk. Estrogen deficiency during menopause plays a major role in post-menopausal osteoporosis by influencing bone, immune, and gut cell activity. In the gut, estrogen loss decreases tight junction proteins that bind epithelial cells of the intestinal barrier together. This increases gut permeability and alters the balance of T cells, increasing inflammatory T helper (Th17) cells and decreasing anti-inflammatory regulatory T cells (Tregs). Prebiotic dietary fibers are a promising treatment modality for gut-mediated inflammation and bone loss associated with estrogen deficiency. In a previous study, we investigated the effect of butyrate (a prebiotic by-product) on Treg levels in the gut, blood, and bone starting with healthy conditions. However, a gap remains in our mechanistic understanding of how estrogen affects systemic inflammation separately and in combination with prebiotic fibers. We developed a two-compartment dynamic model of estrogen in the gut and immune system to investigate the mechanisms of gut-mediated inflammation induced by estrogen-deficient conditions. Our initial model contains ODEs for estrogen, naive T cells, Tregs, and Th17 cells in gut and blood compartments. Estrogen is modeled using physiological mass balance equations considering its production, migration, and elimination. We integrate our estrogen model with an adapted version of our previous butyrate model, adding a population balance for Th17 cells and terms for bidirectional migration of Tregs and Th17 cells. Estrogen interactions with cells are modeled using Hill-type activation and inhibition functions. Our mathematical model predicts changes in T cell dynamics associated with changes in endogenous estrogen levels. We compare our predictions to in vivo results of estrogen, Tregs, and Th17 levels in the gut and blood after surgical menopause in mice. In the future, we will leverage this model to investigate whether the effect of prebiotic fiber treatment on estrogen-deficient bone loss is significantly affected by gut-mediated inflammation. The emphasis in this preprint is on the methods, particularly those for literature-based model parameterization.

systems biology↗

bioRxiv2024

Mathematical Modeling of Drug Delivery from Bi-Layered Core-Shell Polymeric Microspheres

Chronic retinal diseases usually require repetitive local dosing. Depending on factors such as dosing frequency, mode of administration, and associated costs, this can result in poor patient compliance. A better alternative involves using controlled release drug delivery systems to reduce the frequency of intravitreal dosing and extend drug release. However, reaching the market stage is a time-consuming process. In this study, we employed two computational approaches to model and estimate the parameters governing the diffusion-controlled drug release of bovine serum albumin and bevacizumab (an agent that slows neovascularization due to retinal disorders) from bi-layered core-shell microspheres composed of chitosan and polycaprolactone (PCL). We used the estimated parameters to simulate the cumulative release under various conditions, optimize device design to guide future experimental efforts and improve the duration of release above a target daily therapeutic release rate from the microspheres. We investigated the effects of polymeric layer sizes on drug release. We provided straightforward computational tools for others to reuse in designing bi-layered microspheres suitable for addressing intravitreal drug delivery needs in the treatment of ocular neovascularization in chronic retinal diseases.

pharmacology and toxicology↗

bioRxiv2024

Cross-Domain Text Mining of Pathophysiological Processes Associated with Diabetic Kidney Disease

Diabetic kidney disease (DKD) remains a significant burden on the healthcare system and is the leading cause of end-stage renal disease worldwide. The pathophysiology of DKD is multifactorial and characterized by various early signs of metabolic impairment, inflammatory biomarkers, and complex pathways that lead to progressive kidney damage. New treatment prospects rely on a comprehensive understanding of disease pathology. The study aimed to identify signaling drivers and pathways that modulate glomerular endothelial dysfunction in DKD via cross-domain text mining with SemNet 2.0. The open-source literature-based discovery approach, SemNet 2.0, leverages the power of text mining 33+ million PubMed articles to provide integrative insight into multiscalar and multifactorial pathophysiology. A set of identified relevant genes and proteins that regulate different pathological events associated with DKD were analyzed and ranked using normalized mean HeteSim scores. High-ranking genes and proteins intersecting three domains--DKD, immune response, and glomerular endothelial cells--were analyzed. The top 10% of ranked concepts mapped to the following biological functions: angiotensin, apoptosis, cell-cell function, cell adhesion, chemotaxis, growth factor signaling, vascular permeability, nitric oxide response, oxidative stress, cytokine response, macrophage signaling, NF{kappa}B factor activity, TLR signaling, glucose metabolism, inflammatory response, ERK/MAPK signaling, JAK/STAT signaling, T-cell mediated response, WNT signaling, renin angiotensin system, and NADPH response. High-ranking genes and proteins were used to generate a protein-protein interaction network. This comprehensive analysis identified testable hypotheses for interactions or molecules involved with dysregulated signaling in DKD, which can be further studied through biochemical network models.

bioinformatics↗

bioRxiv2023

Computer Modeling of Bevacizumab Drug Distribution after Intravitreal Injection in Rabbit and Human Eyes

Age-related macular degeneration (AMD) is a progressive eye disease that causes loss of central vision and has no cure. Wet AMD is the late neovascular form treated with vascular endothelial growth factor (VEGF) inhibitors. VEGF is the critical driver of wet AMD. One common off-label anti-VEGF drug used in AMD treatment is bevacizumab. Experimental efforts have been made to investigate the pharmacokinetic (PK) behavior of bevacizumab in vitreous and aqueous humor. Still, the quantitative effect of elimination routes and drug concentration in the macula are not well understood. In this work, we developed two spatial models representing rabbit and human vitreous to better understand the PK behavior of bevacizumab. This study explores different cases of drug elimination and the effects of injection location on drug concentration profiles. The models are validated by comparing them with experimental data. Our results suggest that anterior elimination is dominant for bevacizumab clearance from rabbit vitreous, whereas both anterior and posterior elimination have similar importance in drug clearance from the human vitreous. Furthermore, results indicate that drug injections closer to the posterior segment of the vitreous help maintain relevant drug concentrations for longer, improving bevacizumab duration of action in the vitreous. The rabbit and human models predict bevacizumab concentration in the vitreous and fovea, enhancing knowledge and understanding of wet AMD treatment.

systems biology↗

bioRxiv2023

A Mathematical Model of Glomerular Fibrosis in Diabetic Kidney Disease to Predict Therapeutic Efficacy

1BackgroundGlomerular fibrosis is a tissue damage that occurs within the kidneys of chronic and diabetic kidney disease patients. Effective treatments are lacking, and the mechanism of glomerular damage reversal is poorly understood. MethodA mathematical model suitable for hypothesis-driven systems pharmacology of glomerular fibrosis in diabetes was developed from a previous model of interstitial fibrosis. The adapted model consists of a system of ordinary differential equations that models the complex disease etiology and progression of glomerular fibrosis in diabetes. ResultsWithin the scope of the mechanism incorporated, advanced glycation end products (AGE)--matrix proteins that are modified due to high blood glucose--were identified as major contributors to the delay in the recovery from glomerular fibrosis after glucose control. The model predicted that inhibition of AGE production is not an effective approach for accelerating the recovery from glomerular fibrosis. Further, the model predicted that treatments breaking down accumulated AGE is the most productive at reversing glomerular fibrosis. The use of the model led to the identification that glucose control and aminoguanidine are ineffective treatments for reversing advanced glomerular fibrosis because they do not remove accumulated AGE. Additionally, using the model, a potential explanation was generated for the lack of efficacy of alagebrium in treating advanced glomerular fibrosis, which is due to the inability of alagebrium to reduce TGF-{beta}. ImpactUsing the mathematical model, a mechanistic understanding of disease etiology and complexity of glomerular fibrosis in diabetes was illuminated, and then hypothesis-driven explanations for the lack of efficacy of different pharmacological agents for treating glomerular fibrosis were provided. This understanding can enable the development of more efficacious therapeutics for treating kidney damage in diabetes.

systems biology↗

bioRxiv2022

Mathematical Modeling of Impacts of Patient Differences on COVID-19 Lung Fibrosis Outcomes

Patient-specific premorbidity, age, and sex are significant heterogeneous factors that influence the severe manifestation of lung diseases, including COVID-19 fibrosis. The renin-angiotensin system (RAS) plays a prominent role in regulating the effects of these factors. Recent evidence shows patient-specific alterations of RAS homeostasis concentrations with premorbidity and the expression level of angiotensin-converting enzyme 2 (ACE2) during COVID-19. However, conflicting evidence suggests decreases, increases, or no changes in RAS peptides after SARS-CoV-2 infection. In addition, detailed mechanisms connecting the patient-specific conditions before infection to infection-induced RAS alterations are still unknown. Here, a multiscale computational model was developed to quantify the systemic contribution of heterogeneous factors of RAS during COVID-19. Three submodels were connected--an agent-based model for in-host COVID-19 response in the lung tissue, a RAS dynamics model, and a fibrosis dynamics model to investigate the effects of patient-group-specific factors in the systemic alteration of RAS and collagen deposition in the lung. The model results indicated cell death due to inflammatory response as a major contributor to the reduction of ACE and ACE2. In contrast, there were no significant changes in ACE2 dynamics due to viral-bound internalization of ACE2. The model explained possible mechanisms for conflicting evidence of patient-group-specific changes in RAS peptides in previously published studies. Simulated results were consistent with reported RAS peptide values for SARS-CoV-2-negative and SARS-CoV-2-positive patients. RAS peptides decreased for all virtual patient groups with aging in both sexes. In contrast, large variations in the magnitude of reduction were observed between male and female virtual patients in the older and middle-aged groups. The patient-specific variations in homeostasis RAS peptide concentrations of SARS-CoV-2-negative patients also affected the dynamics of RAS during infection. The model results also showed that feedback between RAS signaling and renin dynamics could restore ANGI homeostasis concentration but failed to restore homeostasis values of RAS peptides downstream of ANGI. In addition, the results showed that ACE2 variations with age and sex significantly altered the concentrations of RAS peptides and led to collagen deposition with slight variations depending on age and sex. This model may find further applications in patient-specific calibrations of tissue models for acute and chronic lung diseases to develop personalized treatments.

systems biology↗

bioRxiv2021

Computational Modeling of Aerosol Particle Transport through Lung Mucosa

Delivery of aerosols to the lung has great potential for the treatment of various lung diseases. However, the lungs are coated by a protective mucus layer whose complex properties make this form of delivery difficult. Mucus is a non-Newtonian fluid and is cleared from the lungs over time by ciliated cells. Further, its gel-like structure hinders the diffusion of particles through it. Any aerosolized treatment of lung diseases must have certain properties to circumvent the mucosal barrier, and these properties may vary between diseases, drugs, and patients. Using computational fluid dynamics, a model of this mucus layer was constructed to simulate the convective and diffusive transport of impacted aerosol particles. The model predicts the dosage fraction of particles of a certain size that penetrate the mucus and reach the underlying tissue, as well as the distance downstream of the dosage site where epithelial concentration is maximized. Reactions that may occur in solution are also considered, with simulated data for the interaction of a model virus and antibody. The model is modular so that various lung regions and patient health states may be simulated.

bioengineering↗

bioRxiv2021

Mathematical modeling of the effects of Wnt-10b on bone metabolism

Bone health is determined by factors including bone metabolism or remodeling. Wnt-10b alters osteoblastogenesis through pre-osteoblast proliferation and differentiation and osteoblast apoptosis rate, which collectively lead to the increase of bone density. To model this, we adapted a previously published model of bone remodeling. The resulting model for the bone compartment includes differential equations for active osteoclasts, pre-osteoblasts, osteoblasts, osteocytes, and the amount of bone present at the remodeling site. Our alterations to the original model consist of extending it past a single remodeling cycle and implementing a direct relationship to Wnt-10b. Four new parameters were estimated and validated using normalized data from mice. The model connects Wnt-10b to bone metabolism and predicts the change in trabecular bone volume caused by a change in Wnt-10b input. We find that this model predicts the expected increase in pre-osteoblasts and osteoblasts while also pointing to a decrease in osteoclasts when Wnt-10b is increased.

systems biology↗

bioRxiv2021

Collagen Deposition in Diabetic Kidney Disease Boosts Intercellular Signaling: A Mathematical Model

Diabetic kidney disease is a health burden that is becoming more prevalent in the US and worldwide. The limited options for treating and preventing diabetic kidney disease are in part due to gaps in our understanding of the progression of diabetic kidney damage and its impacts on cellular function. An important cellular function in the kidney glomerulus is intercellular communication via the release and uptake of soluble cytokines and growth factors. In diabetic kidney disease, excess collagen deposition alters the mesangial matrix properties, which, we hypothesize, diminishes the intercellular signaling between key glomerular cells. To test our hypothesis, we utilized established mathematical models of transport to study the impact of pathological deposition on the ability of cells to communicate via intercellular signaling. Our analysis reveals that pathological collagen deposition can enhance the signaling range of the glomerular cells rather than diminishing it. SIGNIFICANCEThe incidence of diabetes is expected to rise to over 600 million by the year 2040, one third of whom are expected to develop diabetic kidney disease. Our lack of understanding of how diabetic kidney disease progresses and affects cellular and tissue function has led to our inability to mitigate the rapidly rising burden of diabetic kidney disease. A hallmark of diabetic kidney damage is collagen deposition, yet its impact on cellular and tissue function is still not well understood. The elucidation of the impact of collagen deposition on cellular and tissue function will enable the identification of mechanisms that exacerbate the progression of diabetic kidney damage and thus provide novel avenues for preventing or slowing the progression of diabetic kidney damage.

systems biology↗

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↗