Global sensitivity analysis of biological multiscale models

Renardy, Marissa; Hult, Caitlin; Evans, Stephanie; Linderman, Jennifer J.; Kirschner, Denise E.

doi:10.1016/j.cobme.2019.09.012

Cited by 51 publications

(41 citation statements)

References 40 publications

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“…We recorded the location and status of every macrophage in the GranSim simulation at five-day intervals and used the radius of the granuloma to determine if a macrophage agent was located in a central or peripheral location within a granuloma. To determine the radius, the granuloma boundary was determined using our previously published algorithm [ 32 ]. Macrophage agents located between the granuloma boundary and 0.8*radius away from the boundary were considered to be "peripheral", those outside of the boundary were classed as not within a granuloma (i.e.…”

Section: Methodsmentioning

confidence: 99%

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Systems biology predicts that fibrosis in tuberculous granulomas may arise through macrophage-to-myofibroblast transformation

et al. 2020

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Mycobacterium tuberculosis (Mtb) infection causes tuberculosis (TB), a disease characterized by development of granulomas. Granulomas consist of activated immune cells that cluster together to limit bacterial growth and restrict dissemination. Control of the TB epidemic has been limited by lengthy drug regimens, antibiotic resistance, and lack of a robustly efficacious vaccine. Fibrosis commonly occurs during treatment and is associated with both positive and negative disease outcomes in TB but little is known about the processes that initiate fibrosis in granulomas. Human and nonhuman primate granulomas undergoing fibrosis can have spindle-shaped macrophages with fibroblast-like morphologies suggesting a relationship between macrophages, fibroblasts, and granuloma fibrosis. This relationship has been difficult to investigate because of the limited availability of human pathology samples, the time scale involved in human TB, and overlap between fibroblast and myeloid cell markers in tissues. To better understand the origins of fibrosis in TB, we used a computational model of TB granuloma biology to identify factors that drive fibrosis over the course of local disease progression. We validated the model with granulomas from nonhuman primates to delineate myeloid cells and lung-resident fibroblasts. Our results suggest that peripheral granuloma fibrosis, which is commonly observed, can arise through macrophage-to-myofibroblast transformation (MMT). Further, we hypothesize that MMT is induced in M1 macrophages through a sequential combination of inflammatory and anti-inflammatory signaling in granuloma macrophages. We predict that MMT may be a mechanism underlying granuloma-associated fibrosis and warrants further investigation into myeloid cells as drivers of fibrotic disease.

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Section: Methodsmentioning

confidence: 99%

“…A positive PRCC indicates that the parameter is associated with an increase in numbers of fibroblasts while negative PRCCs are parameters are associated with a decrease in the number of fibroblasts. The use of LHS and PRCCs in sensitivity analysis is reviewed in [ 32 , 36 ].…”

Section: Methodsmentioning

confidence: 99%

Systems biology predicts that fibrosis in tuberculous granulomas may arise through macrophage-to-myofibroblast transformation

et al. 2020

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“…We used uncertainty and sensitivity analysis techniques to explore model parameter space. In particular, we used Latin hypercube sampling (LHS) (65,66) to generate 1000 parameter sets by varying a range of input parameters that are listed in Supplemental Table II. We then simulated the model with each parameter set for three replications with IL-10 present and three with IL-10 removed from the system for a total time of 150 d, yielding 6000 virtual granulomas, 3000 with IL-10 and 3000 without.…”

Section: Computational Platform and Postrun Analysismentioning

confidence: 99%

IL-10 Impairs Local Immune Response in Lung Granulomas and Lymph Nodes during EarlyMycobacterium tuberculosisInfection

Wong

Evans

Kraus

et al. 2020

The Journal of Immunology

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Tuberculosis (TB), caused by Mycobacterium tuberculosis, continues to be a major global health problem. Lung granulomas are organized structures of host immune cells that function to contain the bacteria. Cytokine expression is a critical component of the protective immune response, but inappropriate cytokine expression can exacerbate TB. Although the importance of proinflammatory cytokines in controlling M. tuberculosis infection has been established, the effects of anti-inflammatory cytokines, such as IL-10, in TB are less well understood. To investigate the role of IL-10, we used an Ab to neutralize IL-10 in cynomolgus macaques during M. tuberculosis infection. Anti-IL-10-treated nonhuman primates had similar overall disease outcomes compared with untreated control nonhuman primates, but there were immunological changes in granulomas and lymph nodes from anti-IL-10treated animals. There was less thoracic inflammation and increased cytokine production in lung granulomas and lymph nodes from IL-10-neutralized animals at 3-4 wk postinfection compared with control animals. At 8 wk postinfection, lung granulomas from IL-10-neutralized animals had reduced cytokine production but increased fibrosis relative to control animals. Although these immunological changes did not affect the overall disease burden during the first 8 wk of infection, we paired computational modeling to explore late infection dynamics. Our findings support that early changes occurring in the absence of IL-10 may lead to better bacterial control later during infection. These unique datasets provide insight into the contribution of IL-10 to the immunological balance necessary for granulomas to control bacterial burden and disease pathology in M. tuberculosis infection.

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“…For each plasma PK parameter set, we calculated the average iDIS over the first day of dosing over all non-replicating Mtb. Finally, we evaluated the partial ranked correlation coefficient (PRCC) between each plasma PK parameter and the predicted iDIS to determine the impact each parameter has on the drug interactions 50,51 .…”

Section: Plasma Pk Sensitivity Analysis On Interaction Strengthmentioning

confidence: 99%

A multi-scale pipeline linking drug transcriptomics with pharmacokinetics predictsin vivointeractions of tuberculosis drugs

Cicchese

Sambarey

Kirschner

et al. 2020

Preprint

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Tuberculosis (TB) is the deadliest infectious disease worldwide. The design of new treatments for TB is hindered by the large number of candidate drugs, drug combinations, dosing choices, and complex pharmaco-kinetics/dynamics (PK/PD). Here we study the interplay of these factors in designing combination therapies by linking a machine-learning model, INDIGO-MTB, which predicts in vitro drug interactions using drug transcriptomics, with a multi-scale model of drug PK/PD and pathogen-immune interactions called GranSim. We calculate an in vivo drug interaction score (iDIS) from dynamics of drug diffusion, spatial distribution, and activity within lesions against various pathogen sub-populations. The iDIS of drug regimens evaluated against non-replicating bacteria significantly correlates with efficacy metrics from clinical trials. Our approach identifies mechanisms that can amplify synergistic or mitigate antagonistic drug interactions in vivo by modulating the relative distribution of drugs. Our mechanistic framework enables efficient evaluation of in vivo drug interactions and optimization of combination therapies.

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Global sensitivity analysis of biological multiscale models

Cited by 51 publications

References 40 publications

Systems biology predicts that fibrosis in tuberculous granulomas may arise through macrophage-to-myofibroblast transformation

Systems biology predicts that fibrosis in tuberculous granulomas may arise through macrophage-to-myofibroblast transformation

IL-10 Impairs Local Immune Response in Lung Granulomas and Lymph Nodes during EarlyMycobacterium tuberculosisInfection

A multi-scale pipeline linking drug transcriptomics with pharmacokinetics predictsin vivointeractions of tuberculosis drugs

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