Mathematical models for systems biology of infectious diseases

Mathematical models help make sense of complex biological systems. Models that are accurate enough can even predict how the system responds to perturbations. This can be used for example to devise a public health strategy to stop an infection from spreading across a population.

On March 15 we published a review on the mathematical modeling in infectious diseases. The review discusses many applications, not only tracking disease spread. Some of the examples we covered include the metabolism of a pathogen’s cells, transcriptomic networks, microbiome ecology, and understanding why antimicrobials that can kill a pathogen in vitro may fail to work in vivo.

The coauthors of this review span a wide array of modelers working on various systems. We came together through a Modeling Work Group of the NIAID Systems Biology for Infectious Diseases Research Program. It wasn’t easy to conceive a review to include all models because they were so different, but we found interesting parallels. Especially interesting was that many of the models use mass-action kinetics as their main assumption. The elements described by mass action range in scale, though. For example, metabolic network models describe the metabolites in a metabolic network, while epidemiology models describe individual people interacting socially in a population. We also discuss considerations for publishing a mathematical model. The suggestions come from our experiences in sharing models among the members of the Modeling Workgroup. Read the review at:

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, JoseBento, Marc A. Suchard, Mario L. Arrieta-Ortiz, Eliza J. R. Peterson, Nitin S. Baliga, ThomasStoeger, Felicia Ruffin, Reese A.K. Richardson, Catherine A. Gao, Thomas D. Horvath, Anthony M. Haag, Qinglong Wu, Tor Savidge, Michael R. Yeaman. iScience [online]

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