Biology subjects

Aponte-Serrano, J. O.

Publications and source records attributed to Aponte-Serrano, J. O..

4 recordsLinked to original sources

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

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

Run for your life: an integrated virtual tissue platform for incorporating exercise oncology into immunotherapy

The purpose of this paper is to introduce a novel in silico platform for simulating early stage solid tumor growth and anti-tumor immune response. We present the model, test the sensitivity and robustness of its parameters, and calibrate it with clinical data from exercise oncology experiments which offer a natural biological backdrop for modulation of anti-tumor immune response. We then perform two virtual experiments with the model that demonstrate its usefulness in guiding pre-clinical and clinical studies of immunotherapy. The first virtual experiment describes the intricate dynamics in the tumor microenvironment between the tumor and the infiltrating immune cells. Such dynamics is difficult to probe during a pre-clinical study as it requires significant redundancy in lab animals and is prohibitively time-consuming and labor-intensive. The result is a series of spatiotemporal snapshots of the tumor and its microenvironment that can serve as a platform to test mechanistic hypotheses on the role and dynamics of different immune cells in anti-tumor immune response. The second virtual experiment shows how dosage and/or frequency of immunotherapy drugs can be optimized based on the aerobic fitness of the patient, so that possible adverse side effects of the treatment can be minimized.

cancer biology↗

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↗