Accepted_test

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

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