A multi-scale model to predict outcomes of immunomodulation and drug therapy during tuberculosis Mycobacterium tuberculosis (Mtb) is the most successful pathogen known to humans;it is responsible for ~2 million deaths/year and infects an estimated 1/3 of the world. Despite decades of study, our understanding of the interplay of various pathogen and immune processes that allow for different outcomes in tuberculosis (TB), i.e. primary TB, latency and reactivation TB, remains incomplete. The hallmark of TB is the formation of a spherical collection of immune cells in the lung and lymph node that both immunologically restrains and physically contains the bacteria. Yet bacilli can survive within granuloma for years. Current therapy requires 6 months of treatment with multiple antibiotics;immunomodulation may be able to augment this treatment, shortening treatment time and reducing side effects. There is a crucial need for an in silico platform to provide a cost-effective means of predicting the outcome of new treatment strategies. The long-term goals of this project are to integrate knowledge about immune system dynamics in these organs into a realistic, multi-scale, multi-organ model of the immune response during Mtb infection and to use this model to identify optimal approaches for immunomodulation/antibiotic therapy.
The specific aims are:
Aim 1 : Incorporate new components (IL-10, bacterial population dynamics) into our existing multi-scale lung granuloma model, and use the model to predict factors affecting control of infection in the lung.
Aim 2 : Incorporate new information (lymph node anatomy, key cytokines, and bacterial populations) into our existing multi-scale lymph node model, and use the model to predict factors leading to initiation of the immune response and granuloma formation and maintenance in a lymph node.
Aim 3. Build a multi-compartment, multi-scale model that includes the models of Aims 1 and 2 and trafficking events between the organs, and use this model to predict infection control and pathology at the level of individual granulomas during immunodulation/antibiotic therapy. Data generated herein from non-human primates will inform our models and be used to validate predictions. Our systems biology approach - incorporating both computational and experimental tools - will allow us to predict and test hypotheses regarding key mechanisms that influence immunity to TB. Our interdisciplinary approach will also serve the broader community of researchers investigating areas related to TB, immunity and multi-scale modeling by providing data and tools that will be made readily available.
Using a combined experimental/computational systems biology approach, we will develop a realistic multi-scale multi-compartment model that describes the immune response to infection with the bacteria that causes tuberculosis. The model will be used to predict the outcome of treatment strategies that boost immunity during antibiotic treatment, providing a cost-effective means of evaluating therapeutic interventions.
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