Author(s): Marino S, Kirschner DE
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Abstract A key issue for the study of tuberculosis is to understand why individuals infected with Mycobacterium tuberculosis (Mtb) experience different clinical outcomes. To better understand the dynamics of Mtb infection and immunity, we have previously developed a temporal mathematical model that qualitatively and quantitatively characterizes the cellular and cytokine control network during infection. In this work we extend that model to a two compartmental model to capture the important processes of cellular activation and priming that occur between the lung and the nearest draining lymph node. We are able to reproduce typical disease progression scenarios including primary infection, latency or clearance. Then we use the model to predict key processes determining these different disease trajectories (i.e. identify bifurcation parameters), suggesting directions for further basic science study and potential new treatment strategies.
This article was published in J Theor Biol
and referenced in Journal of Data Mining in Genomics & Proteomics