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

Maxim Miroshnichenko, Fedor Anatolyevich Kolpakov, Ilya Rinatovich Akberdin
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Abstract

The COVID-19 pandemic significantly transformed the field of mathematical modeling in immunology. International collaboration among numerous research groups yielded a substantial amount of experimental data, which greatly facilitated model validation and led to the development of new mathematical models. The aim of the study is an improvement of system understanding of the immune response to SARS-CoV-2 infection based on the development of a modular mathematical model which provides a foundation for further research on host-pathogen interactions. We utilized the open-source BioUML platform to develop a model using ordinary, delay and stochastic differential equations. The model was validated using experimental data from middle-aged individuals with moderate COVID-19 progression, including measurements of viral load, antibodies, CD4+ and CD8+ T cells, and interleukin-6 levels. Parameter optimization and sensitivity analysis were conducted to refine the model`s accuracy. The model effectively reproduces moderate, severe, and critical COVID-19 progressions, consistent with experimental observations. We investigated the efficiency and contributions of innate and adaptive immunity in response to SARS-CoV-2 infection and assessed immune system behavior during co-infection with HIV and organ transplantation. Additionally, we studied therapy methods, such as interferon administration. The developed model can be employed as a framework for simulating other infectious diseases taking into account follow-up immune response.
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将免疫反应模块化模型作为研究感染疾病发病机制的计算平台
COVID-19 大流行极大地改变了免疫学数学建模领域。众多研究小组之间的国际合作产生了大量的实验数据,这极大地促进了模型的验证,并导致了新数学模型的开发。本研究的目的是在建立模块化数学模型的基础上,加深系统对 SARS-CoV-2 感染免疫反应的理解,为进一步研究宿主与病原体之间的相互作用奠定基础。我们利用开源的 BioUML 平台开发了一个使用常微分方程、延迟微分方程和随机微分方程的模型。该模型利用中度 COVID-19 进展期中年人的实验数据进行了验证,包括病毒载量、抗体、CD4+ 和 CD8+ T 细胞以及白细胞介素-6 水平的测量数据。对模型进行了参数优化和敏感性分析,以提高模型的准确性。该模型有效地再现了 COVID-19 的中度、重度和临界进展,与实验观察结果一致。我们研究了先天性免疫和适应性免疫在应对 SARS-CoV-2 感染时的效率和贡献,并评估了与 HIV 共同感染和器官移植期间的免疫系统行为。此外,我们还研究了干扰素等治疗方法。所开发的模型可用作模拟其他传染病的框架,同时考虑后续免疫反应。
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