The Inflammatory Twitch as a General Strategy for Controlling the Host Response

Pothen, Joshua; Poynter, Matthew E.; Bates, Jason H. T.

doi:10.4049/jimmunol.1202595

Cited by 14 publications

(27 citation statements)

References 26 publications

(50 reference statements)

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“…We have tested the plausibility of this hypothesis at the alveolarcapillary interface in the lung using an agent-based computational model that enabled us to investigate both the spatial and temporal behaviors of the various cellular players involved (3,12). Following calibration against the experimental data of Tanaka et al (21), we showed that this model can mimic the dynamics of allergic inflammation in mice (12), supporting the notion that there is a natural finite timescale to the normal inflammatory response.…”

mentioning

confidence: 82%

“…A key corollary of the inflammatory twitch hypothesis is that there should exist a refractory period during which the capacity to reinstigate inflammation is suppressed, akin to the refractory period associated with muscle twitches and neuronal action potentials. We have tested the plausibility of this hypothesis at the alveolarcapillary interface in the lung using an agent-based computational model that enabled us to investigate both the spatial and temporal behaviors of the various cellular players involved (3,12). Following calibration against the experimental data of Tanaka et al (21), we showed that this model can mimic the dynamics of allergic inflammation in mice (12), supporting the notion that there is a natural finite timescale to the normal inflammatory response.…”

mentioning

confidence: 83%

“…We have previously developed an agentbased computational model of the allergic inflammatory response in an alveolar-capillary cross section using NetLogo 4.1.3 freeware. Further details of this model are presented in a previous publication (12). Briefly, our model of the normal allergic inflammatory response includes separate agents representing mast cells, antigen-presenting cells (representing the combination of dendritic cells, B cells, and macrophages), helper T cells, T-regulatory cells, proinflammatory cells (representing the combination of neutrophils, eosinophils, and other cell types involved in causing local tissue damage), and antiinflammatory cells (representing fibroblasts and other cells involved in tissue repair).…”

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confidence: 99%

“…To this end, we have recently proposed a hypothesis about the nature of the normal allergic inflammatory response that centers on what we refer to as the "inflammatory twitch" (12).…”

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confidence: 99%

“…Investigating this question was, accordingly, the goal of the present study. Again, a previously calibrated agent-based computational model provides a convenient platform for addressing this question because it serves as a virtual laboratory in which we can rapidly test the consequences of behavioral defects in any of the cells represented in the model (3,12), hopefully helping to focus the intent of subsequent laboratory investigation. We focus in particular on defects that cause the twitch to either become prolonged in duration or to fail to resolve altogether, the ultimate hope being that this will identify therapeutic targets.…”

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confidence: 99%

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A computational model of unresolved allergic inflammation in chronic asthma

Pothen

Poynter

Bates

2015

American Journal of Physiology-Lung Cellular and Molecular Phys

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We have previously developed an agent-based computational model to demonstrate the feasibility of a novel hypothesis we term the inflammatory twitch. This hypothesis potentially explains the dynamics of the normal response to allergic inflammation in the lung (Pothen JJ, Poynter ME, Bates JH. J Immunol 190: 3510-3516, 2013) on the basis that antigenic stimulation sets in motion both the onset of inflammation and its subsequent resolution. The result is a self-limited inflammatory event that is similar in a formal sense to a skeletal muscle twitch. We hypothesize here that the chronic airway inflammation characteristic of asthma may represent the failure of the inflammatory twitch to resolve back to baseline. Our model provides a platform with which to perform virtual experiments aimed at investigating possible mechanisms leading to accentuation and/or prolongation of the inflammatory twitch. We used our model to determine how the inflammatory twitch is modified by knocking out certain cell types, interfering with cell activity, and altering cell lifetimes. Increasing the duration of activation of proinflammatory cells (considered to be chiefly neutrophils and eosinophils) markedly accentuated and prolonged the inflammatory twitch. This aberrant twitch behavior was largely abrogated by knocking out T-helper cells (simulating the effect of corticosteroids). The aberrant inflammatory twitch was also normalized by reducing the lifetime of the proinflammatory cells, suggesting that increasing apoptosis of these cells may be a therapeutic target in asthma.

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confidence: 82%

mentioning

confidence: 83%

mentioning

confidence: 99%

“…To this end, we have recently proposed a hypothesis about the nature of the normal allergic inflammatory response that centers on what we refer to as the "inflammatory twitch" (12).…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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A computational model of unresolved allergic inflammation in chronic asthma

Pothen

Poynter

Bates

2015

American Journal of Physiology-Lung Cellular and Molecular Phys

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Systems physiology of the airways in health and obstructive pulmonary disease

Bates

2016

WIREs Mechanisms of Disease

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Fresh air entering the mouth and nose is brought to the blood-gas barrier in the lungs by a repetitively branching network of airways. Provided the individual airway branches remain patent, this airway tree achieves an enormous amplification in cross-sectional area from the trachea to the terminal bronchioles. Obstructive lung diseases such as asthma occur when airway patency becomes compromised. Understanding the pathophysiology of these obstructive disease thus begins with a consideration of the factors that determine the calibre of an individual airway, which include the force balance between the inward elastic recoil of the airway wall, the outward tethering forces of its parenchymal attachments, and any additional forces due to contraction of airway smooth muscle. Other factors may also contribute significantly to airway narrowing, such as thickening of the airway wall and accumulation of secretions in the lumen. Airway obstruction becomes particularly severe when these various factors occur in concert. However, the effect of airway abnormalities on lung function cannot be fully understood only in terms of what happens to a single airway because narrowing throughout the airway tree is invariably heterogeneous and interdependent. Obstructive lung pathologies thus manifest as emergent phenomena arising from the way in which the airway tree behaves a system. These emergent phenomena are studied with clinical measurements of lung function made by spirometry, and by mechanical impedance measured with the forced oscillation technique. Anatomically-based computational models are linking these measurements to underlying anatomic structure in systems physiology terms.

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Agent‐based models of inflammation in translational systems biology: A decade later

Vodovotz

2019

WIREs Mechanisms of Disease

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Agent‐based modeling is a rule‐based, discrete‐event, and spatially explicit computational modeling method that employs computational objects that instantiate the rules and interactions among the individual components (“agents”) of system. Agent‐based modeling is well suited to translating into a computational model the knowledge generated from basic science research, particularly with respect to translating across scales the mechanisms of cellular behavior into aggregated cell population dynamics manifesting at the tissue and organ level. This capacity has made agent‐based modeling an integral method in translational systems biology (TSB), an approach that uses multiscale dynamic computational modeling to explicitly represent disease processes in a clinically relevant fashion. The initial work in the early 2000s using agent‐based models (ABMs) in TSB focused on examining acute inflammation and its intersection with wound healing; the decade since has seen vast growth in both the application of agent‐based modeling to a wide array of disease processes as well as methodological advancements in the use and analysis of ABM. This report presents an update on an earlier review of ABMs in TSB and presents examples of exciting progress in the modeling of various organs and diseases that involve inflammation. This review also describes developments that integrate the use of ABMs with cutting‐edge technologies such as high‐performance computing, machine learning, and artificial intelligence, with a view toward the future integration of these methodologies. This article is categorized under: Translational, Genomic, and Systems Medicine > Translational Medicine Models of Systems Properties and Processes > Mechanistic Models Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models Models of Systems Properties and Processes > Organismal Models

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The Inflammatory Twitch as a General Strategy for Controlling the Host Response

Cited by 14 publications

References 26 publications

A computational model of unresolved allergic inflammation in chronic asthma

A computational model of unresolved allergic inflammation in chronic asthma

Systems physiology of the airways in health and obstructive pulmonary disease

Agent‐based models of inflammation in translational systems biology: A decade later

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