Understanding the Immune Response in Tuberculosis Using Different Mathematical Models and Biological Scales

Gammack, David; Ganguli, Suman; Marino, Simeone; Segovia-Juárez, José; Kirschner, Denise E.

doi:10.1137/040603127

Cited by 50 publications

(42 citation statements)

References 71 publications

(116 reference statements)

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“…In contrast, our previous studies using an agent-based model approach demonstrated a major role for bacterial numbers in size of the granuloma (Ray et al, 2009), which one could interpret loosely as inflammation. This highlights the importance of using different mathematical approaches to explore a specific biological question to uncover the effects of different processes that are present in the system (Gammack et al, 2005). Further work to reconcile these findings is ongoing.…”

Section: Discussionmentioning

confidence: 99%

TNF and IL-10 are major factors in modulation of the phagocytic cell environment in lung and lymph node in tuberculosis: A next-generation two-compartmental model

Marino

Myers

Flynn

et al. 2010

Journal of Theoretical Biology

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102

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Tuberculosis (TB) is one of the earliest recorded human diseases and still one of the deadliest worldwide. Its causative agent is the bacteria Mycobacterium tuberculosis (Mtb). Cytokinemediated macrophage activation is a necessary step in control of bacterial growth, and early immunologic events in lymph node and lung are crucial to the outcome of infection, although the factors that influence these environments and the immune response are poorly understood.Our goal is to build the next-generation two-compartmental model of the immune response to provide a gateway to more spatial and mechanistic investigations of Mycobacterium tuberculosis infection in the LN and lung. Crucial immune factors emerge that affect macrophage populations and inflammation, namely TNF-dependent recruitment and apoptosis, and IL-10 levels. Surprisingly, bacterial load plays a less important role than TNF in increasing the population of infected macrophages and inflammation. Using a mathematical model, it is possible to distinguish the effects of pro-inflammatory (TNF) and anti-inflammatory (IL-10) cytokines on the spectrum of phagocyte populations (macrophages and dendritic cells) in the lung and lymph node. Our results suggest that TNF is a major mediator of recruitment of phagocytes to the lungs. In contrast, IL-10 is a factor in balancing the dominant macrophage phenotype in LN and lung.

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Section: Discussionmentioning

confidence: 99%

TNF and IL-10 are major factors in modulation of the phagocytic cell environment in lung and lymph node in tuberculosis: A next-generation two-compartmental model

Marino

Myers

Flynn

et al. 2010

Journal of Theoretical Biology

Self Cite

102

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“…Biological processes that are relevant to the immune response occur at different scales or levels of resolution, that is, molecular, cellular, and tissue levels [39, 40]. Development of multiscale, multicompartment models based on in vivo/in vitro experimental data is essential to create a computational system that reflects this biological behavior [40].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Modeling Innate Immune Response to EarlyMycobacteriumInfection

Carvalho

Kleijn

Meijer

et al. 2012

Computational and Mathematical Methods in Medicine

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In the study of complex patterns in biology, mathematical and computational models are emerging as important tools. In addition to experimental approaches, these modeling tools have recently been applied to address open questions regarding host-pathogen interaction dynamics, including the immune response to mycobacterial infection and tuberculous granuloma formation. We present an approach in which a computational model represents the interaction of the Mycobacterium infection with the innate immune system in zebrafish at a high level of abstraction. We use the Petri Net formalism to model the interaction between the key host elements involved in granuloma formation and infection dissemination. We define a qualitative model for the understanding and description of causal relations in this dynamic process. Complex processes involving cell-cell or cell-bacteria communication can be modeled at smaller scales and incorporated hierarchically into this main model; these are to be included in later elaborations. With the infection mechanism being defined on a higher level, lower-level processes influencing the host-pathogen interaction can be identified, modeled, and tested both quantitatively and qualitatively. This systems biology framework incorporates modeling to generate and test hypotheses, to perform virtual experiments, and to make experimentally verifiable predictions. Thereby it supports the unraveling of the mechanisms of tuberculosis infection.

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“…Further, we find very little mathematical modeling of the immune response in the lungs, except for an extensive and rich literature on M. tuberculosis infections (see [16, 30]). The peculiar nature of the TB infection makes comparison with other types of infections tenuous.…”

Section: Discussionmentioning

confidence: 99%

“…Detailed modeling of components of the immune system and their interactions has also been a subject of intense actvity [16, 42, 25, 41, 30]. A good example of this can be found in [24], where the complement system , a subsystem of the innate system consisting of enzymes (such as C3) rather than cells, is investigated with as much detail as possible.…”

Section: Discussionmentioning

confidence: 99%

A low dimensional dynamical model of the initial pulmonary innate response to infection

Young

Buckalew

May

et al. 2012

Mathematical Biosciences

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In order to gain a deeper understanding of the onset and progression of pulmonary infections we present and analyze a low dimensional, phenomenological model of infection and the innate immune response in the lungs. Because pulmonary innate immunity has features unique to itself, general mathematical models of the immune system may not be appropriate. The differential equations model that we propose is based on current knowledge of the biology of pulmonary innate immunity and accurately reproduces known features of the initial phase of the dynamics of pulmonary innate system as exhibited in recent experiments. Further, we propose to use the model as a starting point for interrogation with clinical data from a new noninvasive technique for sampling alveolar lining fluid.

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Understanding the Immune Response in Tuberculosis Using Different Mathematical Models and Biological Scales

Cited by 50 publications

References 71 publications

TNF and IL-10 are major factors in modulation of the phagocytic cell environment in lung and lymph node in tuberculosis: A next-generation two-compartmental model

TNF and IL-10 are major factors in modulation of the phagocytic cell environment in lung and lymph node in tuberculosis: A next-generation two-compartmental model

Modeling Innate Immune Response to EarlyMycobacteriumInfection

A low dimensional dynamical model of the initial pulmonary innate response to infection

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