468746 Computational Modeling of Tuberculosis Granuloma Activation

Monday, November 14, 2016
Grand Ballroom B (Hilton San Francisco Union Square)
Minu R. Pilvankar, Steven M. Ruggiero and Ashlee N. Ford Versypt, School of Chemical Engineering, Oklahoma State University, Stillwater, OK

Tuberculosis (TB) is one of the most common infectious diseases and deadliest diseases worldwide. It is estimated that one-third of the world's population is infected with TB, and 1.5 million TB-related deaths were reported in 2014. TB is spread by aerosol droplets containing Mycobacterium tuberculosis (Mtb). The Mtb bacteria enter through the respiratory system and are attacked by the immune system in the lungs, primarily by aveolar macrophages. The bacteria are clustered and contained by the macrophages into cellular aggregates called granulomas. These granulomas can hold the bacteria dormant for long periods of time, even decades, in a condition called latent TB. However, the bacteria persist and can be activated when the granulomas are compromised by other immune response events in a host, such as cancer, HIV, or aging. The activation and subsequent spread of bacteria leads to active TB disease. It is difficult to study the activation process in humans because those with latent TB are asymptomatic and are often undiagnosed. Current animal models all have limitations. Computational and mathematical models can be useful tools for inexpensively conducting short- and long-term in silico experiments with multiple, interacting factors and can aid in generating and testing hypotheses. Several previous computational and mathematical models have been developed to describe the infection or granuloma formation stages of TB. No computational approach has been proposed considering the dynamics of matrix metalloproteinase 1 (MMP-1) regulation and its impact on TB activation. MMP-1 dysregulation has been recently implicated in TB 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 approach is proposed to fill this gap. The overall objective of the study is to predict TB cavity formation (a hallmark of activation) in response to the dynamics of MMP-1 dysregulation. We will report on the status of development of mathematical and computational tools to test the hypothesis that the dynamics of MMP-1 regulation play a key role in the transition from latent TB to active TB.

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