Mathematical models to study the biology of pathogens and the infectious diseases they cause

Joao B. Xavier; Jonathan M. Monk; Saugat Poudel; Charles J. Norsigian; Anand V. Sastry; Chen Liao; Jose Bento; Marc A. Suchard; Mario L. Arrieta-Ortiz; Eliza J.R. Peterson; Nitin S. Baliga; Thomas Stoeger; Felicia Ruffin; Reese A.K. Richardson; Catherine A. Gao; Thomas D. Horvath; Anthony M. Haag; Qinglong Wu; Tor Savidge; Michael R. Yeaman

doi:10.1016/j.isci.2022.104079

Mathematical models to study the biology of pathogens and the infectious diseases they cause

Joao B. Xavier^*, Jonathan M. Monk, Saugat Poudel, Charles J. Norsigian, Anand V. Sastry, Chen Liao, Jose Bento, Marc A. Suchard, Mario L. Arrieta-Ortiz, Eliza J.R. Peterson, Nitin S. Baliga, Thomas Stoeger, Felicia Ruffin, Reese A.K. Richardson, Catherine A. Gao, Thomas D. Horvath, Anthony M. Haag, Qinglong Wu, Tor Savidge, Michael R. Yeaman

^*Corresponding author for this work

Medicine, Pulmonary Division

Research output: Contribution to journal › Review article › peer-review

13 Scopus citations

Abstract

Mathematical models have many applications in infectious diseases: epidemiologists use them to forecast outbreaks and design containment strategies; systems biologists use them to study complex processes sustaining pathogens, from the metabolic networks empowering microbial cells to ecological networks in the microbiome that protects its host. Here, we (1) review important models relevant to infectious diseases, (2) draw parallels among models ranging widely in scale. We end by discussing a minimal set of information for a model to promote its use by others and to enable predictions that help us better fight pathogens and the diseases they cause.

Original language	English (US)
Article number	104079
Journal	iScience
Volume	25
Issue number	4
DOIs	https://doi.org/10.1016/j.isci.2022.104079
State	Published - Apr 15 2022

Funding

This work was supported in part by the National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH) grants U01 AI124275 (MSKCC), U01 AI124290 (Baylor), U01 AI124302 (Boston College), U01 AI124319 (UCLA), U01 AI124316 (UCSD), U19 AI135995 (Scripps), U19 AI135976 (Omics4TB), U19-AI135964 (Northwestern). We acknowledge the NIAID/DMID Systems Biology Consortium for Infectious Diseases Modeling Working Group for the platform for method sharing and for critical feedback on the manuscript. We thank Reed Shabman for helpful comments and careful revisions of the paper and Liliana Brown for the support of the Program this paper originated from. All authors contributed to the concept and writing of the manuscript. JBX made the figures. This work was supported in part by the National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health ( NIH ) grants U01 AI124275 ( MSKCC ), U01 AI124290 ( Baylor ), U01 AI124302 ( Boston College ), U01 AI124319 ( UCLA ), U01 AI124316 ( UCSD ), U19 AI135995 ( Scripps ), U19 AI135976 (Omics4TB), U19-AI135964 (Northwestern). We acknowledge the NIAID/DMID Systems Biology Consortium for Infectious Diseases Modeling Working Group for the platform for method sharing and for critical feedback on the manuscript. We thank Reed Shabman for helpful comments and careful revisions of the paper and Liliana Brown for the support of the Program this paper originated from.

Keywords

Computer modeling
Infection control in health technology
Microbiology

ASJC Scopus subject areas

General

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10.1016/j.isci.2022.104079

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Xavier, J. B., Monk, J. M., Poudel, S., Norsigian, C. J., Sastry, A. V., Liao, C., Bento, J., Suchard, M. A., Arrieta-Ortiz, M. L., Peterson, E. J. R., Baliga, N. S., Stoeger, T., Ruffin, F., Richardson, R. A. K., Gao, C. A., Horvath, T. D., Haag, A. M., Wu, Q., Savidge, T., & Yeaman, M. R. (2022). Mathematical models to study the biology of pathogens and the infectious diseases they cause. iScience, 25(4), Article 104079. https://doi.org/10.1016/j.isci.2022.104079

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abstract = "Mathematical models have many applications in infectious diseases: epidemiologists use them to forecast outbreaks and design containment strategies; systems biologists use them to study complex processes sustaining pathogens, from the metabolic networks empowering microbial cells to ecological networks in the microbiome that protects its host. Here, we (1) review important models relevant to infectious diseases, (2) draw parallels among models ranging widely in scale. We end by discussing a minimal set of information for a model to promote its use by others and to enable predictions that help us better fight pathogens and the diseases they cause.",

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year = "2022",

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doi = "10.1016/j.isci.2022.104079",

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Xavier, JB, Monk, JM, Poudel, S, Norsigian, CJ, Sastry, AV, Liao, C, Bento, J, Suchard, MA, Arrieta-Ortiz, ML, Peterson, EJR, Baliga, NS, Stoeger, T, Ruffin, F, Richardson, RAK, Gao, CA, Horvath, TD, Haag, AM, Wu, Q, Savidge, T & Yeaman, MR 2022, 'Mathematical models to study the biology of pathogens and the infectious diseases they cause', iScience, vol. 25, no. 4, 104079. https://doi.org/10.1016/j.isci.2022.104079

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AU - Xavier, Joao B.

AU - Monk, Jonathan M.

AU - Poudel, Saugat

AU - Norsigian, Charles J.

AU - Sastry, Anand V.

AU - Liao, Chen

AU - Bento, Jose

AU - Suchard, Marc A.

AU - Arrieta-Ortiz, Mario L.

AU - Peterson, Eliza J.R.

AU - Baliga, Nitin S.

AU - Stoeger, Thomas

AU - Ruffin, Felicia

AU - Richardson, Reese A.K.

AU - Gao, Catherine A.

AU - Horvath, Thomas D.

AU - Haag, Anthony M.

AU - Wu, Qinglong

AU - Savidge, Tor

AU - Yeaman, Michael R.

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AB - Mathematical models have many applications in infectious diseases: epidemiologists use them to forecast outbreaks and design containment strategies; systems biologists use them to study complex processes sustaining pathogens, from the metabolic networks empowering microbial cells to ecological networks in the microbiome that protects its host. Here, we (1) review important models relevant to infectious diseases, (2) draw parallels among models ranging widely in scale. We end by discussing a minimal set of information for a model to promote its use by others and to enable predictions that help us better fight pathogens and the diseases they cause.

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Mathematical models to study the biology of pathogens and the infectious diseases they cause

Abstract

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Keywords

ASJC Scopus subject areas

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