Mathematical model of mycobacterium–host interaction describes physiology of persistence

Pedruzzi, Gabriele; Rao, Kanury V. S.; Chatterjee, Samrat

doi:10.1016/j.jtbi.2015.03.031

Cited by 15 publications

(6 citation statements)

References 49 publications

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“…Through mechanistic modelling, TB has been studied across multiple life scales and mathematical formalisms 10 , from host–pathogen interactions at the cellular level 11 to single hybrid multi-compartment models of granuloma formation 12 . Thus, researchers have been able to describe some physiological determinants behind the persistence of a mycobacterial infection 13 and predict how certain cellular interactions lead to different disease outcomes 14 .…”

Section: Introductionmentioning

confidence: 99%

Bifurcation analysis of a tuberculosis progression model for drug target identification

Flores-Garza,

Hernández-Pando,

García-Zárate

et al. 2023

Sci Rep

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Tuberculosis (TB) is a major cause of morbidity and mortality worldwide. The emergence and rapid spread of drug-resistant M. tuberculosis strains urge us to develop novel treatments. Experimental trials are constrained by laboratory capacity, insufficient funds, low number of laboratory animals and obsolete technology. Systems-level approaches to quantitatively study TB can overcome these limitations. Previously, we proposed a mathematical model describing the key regulatory mechanisms underlying the pathological progression of TB. Here, we systematically explore the effect of parameter variations on disease outcome. We find five bifurcation parameters that steer the clinical outcome of TB: number of bacteria phagocytosed per macrophage, macrophages death, macrophage killing by bacteria, macrophage recruitment, and phagocytosis of bacteria. The corresponding bifurcation diagrams show all-or-nothing dose–response curves with parameter regions mapping onto bacterial clearance, persistent infection, or history-dependent clearance or infection. Importantly, the pathogenic stage strongly affects the sensitivity of the host to these parameter variations. We identify parameter values corresponding to a latent-infection model of TB, where disease progression occurs significantly slower than in progressive TB. Two-dimensional bifurcation analyses uncovered synergistic parameter pairs that could act as efficient compound therapeutic approaches. Through bifurcation analysis, we reveal how modulation of specific regulatory mechanisms could steer the clinical outcome of TB.

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Section: Introductionmentioning

confidence: 99%

Bifurcation analysis of a tuberculosis progression model for drug target identification

Flores-Garza,

Hernández-Pando,

García-Zárate

et al. 2023

Sci Rep

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“…Using LHS, 10,000 sets of parameters were generated and PRCC was evaluated varying between −1 and 1. A cut off ± 0.3 was set to the sensitivity index acquired above for refining sensitive parameters [ 47 , 48 ].…”

Section: Resultsmentioning

confidence: 99%

“… Each parameter was sampled for 10 fold up and down from the baseline value and obtained sensitivity indexes using PRCC algorithm. A cut-off ± 0.3 was applied over sensitivity indexes and were marked sensitive [ 47 , 48 ]. * : represents the value beyond the cut-off ± 0.3 …”

Section: Resultsmentioning

confidence: 99%

Understanding doxorubicin associated calcium remodeling during triple-negative breast cancer treatment: an in silico study

Arora

Ghosh

Chatterjee

2021

Exploration of Targeted Anti-Tumor Therapy

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Aim: Triple-negative breast cancer (TNBC) is the most malignant subtype of breast cancer with high heterogeneity, rapid progression, and paucity of treatment options. The most effective chemotherapeutic drug used to treat TNBC is doxorubicin (Doxo) which is an anthracycline antibiotic. However, Doxo treatment alters cytosolic calcium dynamics leading to drug-resistance condition. The aim of this study is to capture the alterations in the activity of various calcium channels and pumps during Doxo treatment and their consequences on cytosolic calcium dynamics that ultimately result in drug resistance. Methods: In the present study, a mathematical model is proposed to capture the complex dynamical landscape of intracellular calcium during Doxo treatment. This study provides an insight into Doxo remodeling of calcium dynamics and associated drug-resistance effect. The model was first analyzed analytically and then explored through numerical simulation using techniques like global sensitivity analysis, parameter recalibration, etc. Results: The model is used to predict the potential combination therapy for Doxo that can overcome Doxo associated drug resistance. The results show targeting the dysregulated Ca2+ channels and pumps might provide efficient chemotherapy in TNBC. It was also observed that the indispensability of calcium influx rate is paramount in the Doxo drug resistance. Finally, three drugs were identified from existing literature that could be used as a combination therapy along with Doxo. Conclusions: The investigation highlights the importance of integrating the calcium signaling of various calcium regulating compounds for their effective anti-tumor effects deliverance along with chemotherapeutic agents. The results from this study might provide a new direction to the experimental biologists to explore different combination therapies with Doxo to enhance its anti-tumor effect.

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“…The general computational framework is reviewed in (148) and may sometimes rely on development of individual level models, e.g. single cell models (58, 83, 86, 87, 149), for incorporation into a multi-scale framework. These models were supported and spurred by novel NHP data, including data on individual granulomas, as well as questions from the experimental literature surrounding the roles of specific pro- and anti-inflammatory factors.…”

Section: Overview Of In-host Computational Modelsmentioning

confidence: 99%

A review of computational and mathematical modeling contributions to our understanding of Mycobacterium tuberculosis within-host infection and treatment

Kirschner

Pienaar

Marino

et al. 2017

Current Opinion in Systems Biology

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Tuberculosis (TB) is an ancient and deadly disease characterized by complex host-pathogen dynamics playing out over multiple time and length scales and physiological compartments. Computational modeling can be used to integrate various types of experimental data and suggest new hypotheses, mechanisms, and therapeutic approaches to TB. Here, we offer a first-time comprehensive review of work on within-host TB models that describe the immune response of the host to infection, including the formation of lung granulomas. The models include systems of ordinary and partial differential equations and agent-based models as well as hybrid and multi-scale models that are combinations of these. Many aspects of M. tuberculosis infection, including host dynamics in the lung (typical site of infection for TB), granuloma formation, roles of cytokine and chemokine dynamics, and bacterial nutrient availability have been explored. Finally, we survey applications of these within-host models to TB therapy and prevention and suggest future directions to impact this global disease.

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Mathematical model of mycobacterium–host interaction describes physiology of persistence

Cited by 15 publications

References 49 publications

Bifurcation analysis of a tuberculosis progression model for drug target identification

Bifurcation analysis of a tuberculosis progression model for drug target identification

Understanding doxorubicin associated calcium remodeling during triple-negative breast cancer treatment: an in silico study

A review of computational and mathematical modeling contributions to our understanding of Mycobacterium tuberculosis within-host infection and treatment

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