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Viruses
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Computational Biology of Viruses: From Molecules to Epidemics, Volume 2
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Computational Biology of Viruses: From Molecules to Epidemics, Volume 2

A special issue of Viruses (ISSN 1999-4915). This special issue belongs to the section "General Virology".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 3192

Special Issue Editor

Dr. Viktor Müller

E-Mail Website
Guest Editor
Department of Plant Systematics, Ecology and Theoretical Biology, Institute of Biology, Eötvös Loránd University, Budapest, Hungary
Interests: computational modelling; data analysis and evolutionary theory of viral infections
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational approaches have been used to study viruses at all levels of organization: from the molecular processes that occur within infected cells, through the dynamics of populations of virions and cells inside infected hosts, up to the level of epidemics and transmission between hosts. Following up on the success of its first edition, this Special Issue invites submissions that involve computational methods (mathematical or simulation modeling, or data analysis) at any (or, for multiscale models, several) of these levels to gain new insights into the fundamental processes, etiology, spread, and evolution of viral infections.

Dr. Viktor Müller
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Viruses is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mathematical modeling
  • simulation modeling
  • molecular processes
  • within-host dynamics
  • epidemiological models
  • multiscale models

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Related Special Issues

Published Papers (4 papers)

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Research

36 pages, 11357 KiB  
Article
A Modular Mathematical Model of the Immune Response for Investigating the Pathogenesis of Infectious Diseases
by Maxim I. Miroshnichenko, Fedor A. Kolpakov and Ilya R. Akberdin
Viruses 2025, 17(5), 589; https://doi.org/10.3390/v17050589 - 22 Apr 2025
Viewed by 316
Abstract
The COVID-19 pandemic highlighted the importance of mathematical modeling for understanding viral infection dynamics and accelerated its application into immunological research. Collaborative efforts among international research groups yielded a wealth of experimental data, which facilitated model development and validation. This study focuses on [...] Read more.
The COVID-19 pandemic highlighted the importance of mathematical modeling for understanding viral infection dynamics and accelerated its application into immunological research. Collaborative efforts among international research groups yielded a wealth of experimental data, which facilitated model development and validation. This study focuses on developing a modular mathematical model of the immune response, capturing the interactions between innate and adaptive immunity, with an application to SARS-CoV-2 infection. The model was validated using experimental data from middle-aged individuals with moderate COVID-19 progression, including measurements of viral load in the upper and lower airways, serum antibodies, CD4+ and CD8+ T cells, and interleukin-6 levels. Parameter optimization and sensitivity analysis were performed to improve the model accuracy. Additionally, identifiability analysis was conducted to assess whether the data were sufficient for reliable parameter estimation. The verified model simulates the dynamics of moderate, severe, and critical COVID-19 progressions using measured data on lung epithelium damage, viral load, and IL-6 levels as key indicators of disease severity. We also performed a series of validation scenarios to assess whether the model correctly reproduces biologically relevant behaviors under various conditions, such as immunity hyperactivation, co-infection with HIV, and interferon administration as a therapeutic strategy. The model was developed as a component of the Digital Twin project and represents a general immune module that integrates both innate and adaptive immunity. It can be utilized for further COVID-19 research or serve as a foundation for studying other infectious diseases, provided sufficient data are available. Full article
(This article belongs to the Special Issue Computational Biology of Viruses: From Molecules to Epidemics, Volume 2)
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15 pages, 1603 KiB  
Article
Quantifying Impact of HIV Receptor Surface Density Reveals Differences in Fusion Dynamics of HIV Strains
by Anthony Gerg and Hana M. Dobrovolny
Viruses 2025, 17(4), 583; https://doi.org/10.3390/v17040583 - 18 Apr 2025
Viewed by 213
Abstract
Human Immunodeficiency Virus (HIV) Type-1 has been studied heavily for decades, yet one area that is still poorly understood is the virus’ ability to cause cell–cell fusion. In HIV, the fusion process is mediated by viral surface glycoproteins that bind to CD4 cell [...] Read more.
Human Immunodeficiency Virus (HIV) Type-1 has been studied heavily for decades, yet one area that is still poorly understood is the virus’ ability to cause cell–cell fusion. In HIV, the fusion process is mediated by viral surface glycoproteins that bind to CD4 cell receptors. This virus-mediated cell fusion creates multi-nucleated cells called syncytia that can affect infection dynamics. Syncytia formation is often studied using a cell–cell fusion assay, in which donor cells expressing the viral surface protein fuse with acceptor cells expressing the cell receptor. A mathematical model capable of reproducing the dynamics of the cell–cell fusion assay was recently developed and can be used to quantify changes in syncytia formation. In this study, we use this mathematical model to quantify the changes in syncytia formation in HIV as the surface density of the glycoproteins is varied. We find that we need to modify the model to explicitly include a density-dependent syncytia formation rate that allows us to capture the dynamics of the cell–cell fusion assay as the density of the glycoproteins changes. With this modification, we find that cell–cell fusion of the HXB2 strain, which uses the CXCR4 coreceptor, shows a threshold-like behavior, while cell–cell fusion of the Sf162 strain, which uses the CCR5 co-receptor, shows a more gradual change as surface density decreases. Full article
(This article belongs to the Special Issue Computational Biology of Viruses: From Molecules to Epidemics, Volume 2)
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21 pages, 1637 KiB  
Article
Structural and Practical Identifiability of Phenomenological Growth Models for Epidemic Forecasting
by Yuganthi R. Liyanage, Gerardo Chowell, Gleb Pogudin and Necibe Tuncer
Viruses 2025, 17(4), 496; https://doi.org/10.3390/v17040496 - 29 Mar 2025
Viewed by 230
Abstract
Phenomenological models are highly effective tools for forecasting disease dynamics using real-world data, particularly in scenarios where detailed knowledge of disease mechanisms is limited. However, their reliability depends on the model parameters’ structural and practical identifiability. In this study, we systematically analyze the [...] Read more.
Phenomenological models are highly effective tools for forecasting disease dynamics using real-world data, particularly in scenarios where detailed knowledge of disease mechanisms is limited. However, their reliability depends on the model parameters’ structural and practical identifiability. In this study, we systematically analyze the identifiability of six commonly used growth models in epidemiology: the generalized growth model (GGM), the generalized logistic model (GLM), the Richards model, the generalized Richards model (GRM), the Gompertz model, and a modified SEIR model with inhomogeneous mixing. To address challenges posed by non-integer power exponents in these models, we reformulate them by introducing additional state variables. This enables rigorous structural identifiability analysis using the StructuralIdentifiability.jl package in JULIA. We validated the structural identifiability results by performing parameter estimation and forecasting using the GrowthPredict MATLAB Toolbox. This toolbox is designed to fit and forecast time series trajectories based on phenomenological growth models. We applied it to three epidemiological datasets: weekly incidence data for monkeypox, COVID-19, and Ebola. Additionally, we assessed practical identifiability through Monte Carlo simulations to evaluate parameter estimation robustness under varying levels of observational noise. Our results confirm that all six models are structurally identifiable under the proposed reformulation. Furthermore, practical identifiability analyses demonstrate that parameter estimates remain robust across different noise levels, though sensitivity varies by model and dataset. These findings provide critical insights into the strengths and limitations of phenomenological models to characterize epidemic trajectories, emphasizing their adaptability to real-world challenges and their role in informing public health interventions. Full article
(This article belongs to the Special Issue Computational Biology of Viruses: From Molecules to Epidemics, Volume 2)
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23 pages, 4950 KiB  
Article
Comparative Evaluation of Open-Source Bioinformatics Pipelines for Full-Length Viral Genome Assembly
by Levente Zsichla, Marius Zeeb, Dávid Fazekas, Éva Áy, Dalma Müller, Karin J. Metzner, Roger D. Kouyos and Viktor Müller
Viruses 2024, 16(12), 1824; https://doi.org/10.3390/v16121824 - 24 Nov 2024
Cited by 1 | Viewed by 1436
Abstract
The increasingly widespread application of next-generation sequencing (NGS) in clinical diagnostics and epidemiological research has generated a demand for robust, fast, automated, and user-friendly bioinformatics workflows. To guide the choice of tools for the assembly of full-length viral genomes from NGS datasets, we [...] Read more.
The increasingly widespread application of next-generation sequencing (NGS) in clinical diagnostics and epidemiological research has generated a demand for robust, fast, automated, and user-friendly bioinformatics workflows. To guide the choice of tools for the assembly of full-length viral genomes from NGS datasets, we assessed the performance and applicability of four open-source bioinformatics pipelines (shiver—for which we created a user-friendly Dockerized version, referred to as dshiver; SmaltAlign; viral-ngs; and V-pipe) using both simulated and real-world HIV-1 paired-end short-read datasets and default settings. All four pipelines produced consensus genome assemblies with high quality metrics (genome fraction recovery, mismatch and indel rates, variant calling F1 scores) when the reference sequence used for assembly had high similarity to the analyzed sample. The shiver and SmaltAlign pipelines (but not viral-ngs and V-Pipe) also showed robust performance with more divergent samples (non-matching subtypes). With empirical datasets, SmaltAlign and viral-ngs exhibited an order of magnitude shorter runtime compared to V-Pipe and shiver. In terms of applicability, V-Pipe provides the broadest functionalities, SmaltAlign and dshiver combine user-friendliness with robustness, while the use of viral-ngs requires less computational resources compared to other pipelines. In conclusion, if a closely matched reference sequence is available, all pipelines can reliably reconstruct viral consensus genomes; therefore, differences in user-friendliness and runtime may guide the choice of the pipeline in a particular setting. If a matched reference sequence cannot be selected, we recommend shiver or SmaltAlign for robust performance. The new Dockerized version of shiver offers ease of use in addition to the accuracy and robustness of the original pipeline. Full article
(This article belongs to the Special Issue Computational Biology of Viruses: From Molecules to Epidemics, Volume 2)
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