iScience (Apr 2022)
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
Affiliations
- Joao B. Xavier
- Program for Computational and Systems Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA; Corresponding author
- Jonathan M. Monk
- Department of Bioengineering, UC San Diego, San Diego, CA, USA
- Saugat Poudel
- Department of Bioengineering, UC San Diego, San Diego, CA, USA
- Charles J. Norsigian
- Department of Bioengineering, UC San Diego, San Diego, CA, USA
- Anand V. Sastry
- Department of Bioengineering, UC San Diego, San Diego, CA, USA
- Chen Liao
- Program for Computational and Systems Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
- Jose Bento
- Computer Science Department, Boston College, Chestnut Hill, MA, USA
- Marc A. Suchard
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA
- Mario L. Arrieta-Ortiz
- Institute for Systems Biology, Seattle, WA, USA
- Eliza J.R. Peterson
- Institute for Systems Biology, Seattle, WA, USA
- Nitin S. Baliga
- Institute for Systems Biology, Seattle, WA, USA
- Thomas Stoeger
- Department of Chemical and Biological Engineering; Northwestern University, Evanston, IL 60208, USA; Successful Clinical Response in Pneumonia Therapy (SCRIPT) Systems Biology Center, Northwestern University, Chicago, IL, USA
- Felicia Ruffin
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
- Reese A.K. Richardson
- Department of Chemical and Biological Engineering; Northwestern University, Evanston, IL 60208, USA; Successful Clinical Response in Pneumonia Therapy (SCRIPT) Systems Biology Center, Northwestern University, Chicago, IL, USA
- Catherine A. Gao
- Successful Clinical Response in Pneumonia Therapy (SCRIPT) Systems Biology Center, Northwestern University, Chicago, IL, USA; Division of Pulmonary and Critical Care, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Thomas D. Horvath
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA; Department of Pathology, Texas Children’s Microbiome Center, Texas Children’s Hospital, Houston, TX 77030, USA
- Anthony M. Haag
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA; Department of Pathology, Texas Children’s Microbiome Center, Texas Children’s Hospital, Houston, TX 77030, USA
- Qinglong Wu
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA; Department of Pathology, Texas Children’s Microbiome Center, Texas Children’s Hospital, Houston, TX 77030, USA
- Tor Savidge
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA; Department of Pathology, Texas Children’s Microbiome Center, Texas Children’s Hospital, Houston, TX 77030, USA
- Michael R. Yeaman
- David Geffen School of Medicine at UCLA & Lundquist Institute for Infection & Immunity at Harbor UCLA Medical Center, Los Angeles, CA, USA
- Journal volume & issue
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Vol. 25,
no. 4
p. 104079
Abstract
Summary: 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.
