Accepted_test

A modular model of immune response as a computational platform to investigate a pathogenesis of infection disease

by Miroshnichenko Maxim Igorevich | Kolpakov Fedor Anatolyevich | Akberdin Ilya Rinatovich | Department of Computational Biology, Scientific Center for Genetics and Life Sciences, Sirius University of Science and Technology, Sirius, Russia | Department of Computational Biology, Scientific Center for Genetics and Life Sciences, Sirius University of Science and Technology, Sirius, Russia | Department of Computational Biology, Scientific Center for Genetics and Life Sciences, Sirius University of Science and Technology, Sirius, Russia

Abstract ID: 297

Event: BGRS-abstracts

Sections: [Sym 12] Section “Mathematical immunology”

The COVID-19 pandemic facilitated global collaboration, resulting in abundant experimental data and novel mathematical models. We aimed to address existing model limitations by developing a modular model of SARS-CoV-2 infection and immune responses. Utilizing the open-source platform BioUML, we employed ordinary and delay differential equations to construct the model. Through calibration and optimization using numerous time-series experimental data we successfully validated the model. With the developed model, we successfully replicated various disease severity scenarios and investigated the impact of age and SARS-CoV-2 variants on disease outcomes. Additionally, we modeled immune responses in HIV-infected individuals. The developed modular model, comprising 31 ODEs and 114 parameters, offers insights into disease progression and treatment strategies. It serves as a foundational tool for simulating infectious diseases and is publicly available on GitLab.