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Zhou Z, Li D, Zhao Z, Shi S, Wu J, Li J, Zhang J, Gui K, Zhang Y, Ouyang Q, Mei H, Hu Y, Li F. Dynamical modelling of viral infection and cooperative immune protection in COVID-19 patients. PLoS Comput Biol 2023; 19:e1011383. [PMID: 37656752 PMCID: PMC10501599 DOI: 10.1371/journal.pcbi.1011383] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 09/14/2023] [Accepted: 07/24/2023] [Indexed: 09/03/2023] Open
Abstract
Once challenged by the SARS-CoV-2 virus, the human host immune system triggers a dynamic process against infection. We constructed a mathematical model to describe host innate and adaptive immune response to viral challenge. Based on the dynamic properties of viral load and immune response, we classified the resulting dynamics into four modes, reflecting increasing severity of COVID-19 disease. We found the numerical product of immune system's ability to clear the virus and to kill the infected cells, namely immune efficacy, to be predictive of disease severity. We also investigated vaccine-induced protection against SARS-CoV-2 infection. Results suggested that immune efficacy based on memory T cells and neutralizing antibody titers could be used to predict population vaccine protection rates. Finally, we analyzed infection dynamics of SARS-CoV-2 variants within the construct of our mathematical model. Overall, our results provide a systematic framework for understanding the dynamics of host response upon challenge by SARS-CoV-2 infection, and this framework can be used to predict vaccine protection and perform clinical diagnosis.
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Affiliation(s)
- Zhengqing Zhou
- School of Physics, Center for Quantitative Biology, Peking University, Beijing, China
| | - Dianjie Li
- School of Physics, Center for Quantitative Biology, Peking University, Beijing, China
| | - Ziheng Zhao
- Department of Immunology, School of Basic Medical Sciences, NHC Key Laboratory of Medical Immunology, Peking University, Beijing, China
| | - Shuyu Shi
- Peking University Third Hospital, Peking University, Beijing, China
| | - Jianghua Wu
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jianwei Li
- School of Physics, Center for Quantitative Biology, Peking University, Beijing, China
| | - Jingpeng Zhang
- School of Physics, Center for Quantitative Biology, Peking University, Beijing, China
| | - Ke Gui
- Department of Immunology, School of Basic Medical Sciences, NHC Key Laboratory of Medical Immunology, Peking University, Beijing, China
| | - Yu Zhang
- Department of Immunology, School of Basic Medical Sciences, NHC Key Laboratory of Medical Immunology, Peking University, Beijing, China
| | - Qi Ouyang
- School of Physics, Center for Quantitative Biology, Peking University, Beijing, China
| | - Heng Mei
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yu Hu
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fangting Li
- School of Physics, Center for Quantitative Biology, Peking University, Beijing, China
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Avila-Ponce de León U, Vazquez-Jimenez A, Cervera A, Resendis-González G, Neri-Rosario D, Resendis-Antonio O. Machine Learning and COVID-19: Lessons from SARS-CoV-2. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1412:311-335. [PMID: 37378775 DOI: 10.1007/978-3-031-28012-2_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
Currently, methods in machine learning have opened a significant number of applications to construct classifiers with capacities to recognize, identify, and interpret patterns hidden in massive amounts of data. This technology has been used to solve a variety of social and health issues against coronavirus disease 2019 (COVID-19). In this chapter, we present some supervised and unsupervised machine learning techniques that have contributed in three aspects to supplying information to health authorities and diminishing the deadly effects of the current worldwide outbreak on the population. First is the identification and construction of powerful classifiers capable of predicting severe, moderate, or asymptomatic responses in COVID-19 patients starting from clinical or high-throughput technologies. Second is the identification of groups of patients with similar physiological responses to improve the triage classification and inform treatments. The final aspect is the combination of machine learning methods and schemes from systems biology to link associative studies with mechanistic frameworks. This chapter aims to discuss some practical applications in the use of machine learning techniques to handle data coming from social behavior and high-throughput technologies, associated with COVID-19 evolution.
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Affiliation(s)
- Ugo Avila-Ponce de León
- Programa de Doctorado en Ciencias Biológicas, Universidad Nacional Autónoma de México, Ciudad de México, Mexico
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico
| | - Aarón Vazquez-Jimenez
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico
| | - Alejandra Cervera
- Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico
| | - Galilea Resendis-González
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico
| | - Daniel Neri-Rosario
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico
| | - Osbaldo Resendis-Antonio
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico.
- Coordinación de la Investigación Científica - Red de Apoyo a la Investigación - Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México (UNAM), Ciudad de México, Mexico.
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de León UAP, Resendis-Antonio O. Macrophage Boolean networks in the time of SARS-CoV-2. Front Immunol 2022; 13:997434. [DOI: 10.3389/fimmu.2022.997434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 10/04/2022] [Indexed: 11/13/2022] Open
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