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WIREs Syst Biol Med
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A multifaceted approach to modeling the immune response in tuberculosis

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Abstract Tuberculosis (TB) is a deadly infectious disease caused by Mycobacterium tuberculosis (Mtb). No available vaccine is reliable and, although treatment exists, approximately 2 million people still die each year. The hallmark of TB infection is the granuloma, a self‐organizing structure of immune cells forming in the lung and lymph nodes in response to bacterial invasion. Protective immune mechanisms play a role in granuloma formation and maintenance; these act over different time/length scales (e.g., molecular, cellular, and tissue scales). The significance of specific immune factors in determining disease outcome is still poorly understood, despite incredible efforts to establish several animal systems to track infection progression and granuloma formation. Mathematical and computational modeling approaches have recently been applied to address open questions regarding host–pathogen interaction dynamics, including the immune response to Mtb infection and TB granuloma formation. This provides a unique opportunity to identify factors that are crucial to a successful outcome of infection in humans. These modeling tools not only offer an additional avenue for exploring immune dynamics at multiple biological scales but also complement and extend knowledge gained via experimental tools. We review recent modeling efforts in capturing the immune response to Mtb, emphasizing the importance of a multiorgan and multiscale approach that has tuneable resolution. Together with experimentation, systems biology has begun to unravel key factors driving granuloma formation and protective immune response in TB. WIREs Syst Biol Med 2011 3 479–489 DOI: 10.1002/wsbm.131 This article is categorized under: Models of Systems Properties and Processes > Cellular Models Models of Systems Properties and Processes > Mechanistic Models Physiology > Mammalian Physiology in Health and Disease

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Multiscale, multicompartment view of the immune response to Mycobacterium tuberculosis (Mtb) infection. Mtb introduced into the lung (left side of figure) is taken up by dendritic cells (DCs) and macrophages (Mϕs). DCs travel through the lymphatics to a draining lymph node (LN) bringing bacteria with them. In the LN, display of peptide‐major histocompatibility complexes (pMHCs) by DCs [antigen‐presenting cells (APCs)] leads to T cell priming. These cells travel to the lung via blood. Granulomas form in the lung and later, LNs (not shown). In both the lung and LN, molecular events (e.g., pMHC binding, IL‐10, and TNF receptor binding) influence cell behavior (e.g., display of pMHC complexes, cell survival, or activation) and cell behavior influences tissue‐level events (e.g., T‐cell priming and granuloma formation). Within a granuloma, T cells secrete cytokines such as IFN‐γ, which activates macrophages to destroy the bacteria with which they are infected.16 Cytotoxic T cells can also directly kill infected cells, by secreting perforin and granulysin.7 This leads to inhibition or killing of bacilli as well. Another feature of the granuloma in primates is the extensive cell death within the tissue, called necrosis, that develops in the center of the granuloma.17 Importantly, bacteria are not always eliminated within the granuloma, but can become dormant, resulting in a latent infection.1 Information flows both ‘bottom‐up’ and ‘top‐down’ (orange arrows) and ‘to and from’ compartments (blue arrows).

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Example of a typical granuloma. Note: the letter c denotes the central necrotic core and infected macrophages surrounded by a rim of lymphocytes (letter h).11,12

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Individual scale models to understand the role of tumor necrosis factor (TNF) in tuberculosis (TB). (a) 2‐D Agent‐based model (ABM) simulation of a granuloma showing baseline solid granuloma and a necrotic TNF−/− granuloma.30 (b) Virtual clinical trial of anti‐TNF therapy shows the number of reactivations per 100 virtual patients using two types of anti‐TNF treatments and two different reactivation thresholds.35 (c) Prediction of a TNF gradient in a granuloma due to secretion and uptake kinetics by different cell types.29

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Models of Systems Properties and Processes > Cellular Models
Models of Systems Properties and Processes > Mechanistic Models
Physiology > Mammalian Physiology in Health and Disease

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