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Mathematical Modeling of Tuberculosis Granuloma Activation

School of Chemical Engineering, Oklahoma State University, Stillwater, OK 74078, USA
Oklahoma Center for Respiratory and Infectious Diseases, Oklahoma State University, Stillwater, OK 74078, USA
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 23 October 2017 / Revised: 28 November 2017 / Accepted: 4 December 2017 / Published: 11 December 2017
(This article belongs to the Special Issue Biological Networks)
Tuberculosis (TB) is one of the most common infectious diseases worldwide. It is estimated that one-third of the world’s population is infected with TB. Most have the latent stage of the disease that can later transition to active TB disease. TB is spread by aerosol droplets containing Mycobacterium tuberculosis (Mtb). Mtb bacteria enter through the respiratory system and are attacked by the immune system in the lungs. The bacteria are clustered and contained by macrophages into cellular aggregates called granulomas. These granulomas can hold the bacteria dormant for long periods of time in latent TB. The bacteria can be perturbed from latency to active TB disease in a process called granuloma activation when the granulomas are compromised by other immune response events in a host, such as HIV, cancer, or aging. Dysregulation of matrix metalloproteinase 1 (MMP-1) has been recently implicated in granuloma activation through experimental studies, but the mechanism is not well understood. Animal and human studies currently cannot probe the dynamics of activation, so a computational model is developed to fill this gap. This dynamic mathematical model focuses specifically on the latent to active transition after the initial immune response has successfully formed a granuloma. Bacterial leakage from latent granulomas is successfully simulated in response to the MMP-1 dynamics under several scenarios for granuloma activation. View Full-Text
Keywords: latent tuberculosis; immune system; cytokine signaling network; dynamic systems; collagen remodeling latent tuberculosis; immune system; cytokine signaling network; dynamic systems; collagen remodeling
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MDPI and ACS Style

Ruggiero, S.M.; Pilvankar, M.R.; Ford Versypt, A.N. Mathematical Modeling of Tuberculosis Granuloma Activation. Processes 2017, 5, 79.

AMA Style

Ruggiero SM, Pilvankar MR, Ford Versypt AN. Mathematical Modeling of Tuberculosis Granuloma Activation. Processes. 2017; 5(4):79.

Chicago/Turabian Style

Ruggiero, Steve M., Minu R. Pilvankar, and Ashlee N. Ford Versypt. 2017. "Mathematical Modeling of Tuberculosis Granuloma Activation" Processes 5, no. 4: 79.

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