Mathematical modeling of bacterial virulence and host–pathogen interactions in the Dictyostelium/Pseudomonas system

Fumanelli, Laura; Iannelli, Mimmo; Janjua, Hussnain Ahmed; Jousson, Olivier

doi:10.1016/j.jtbi.2010.11.018

Cited by 7 publications

(20 citation statements)

References 12 publications

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“…Modelling approaches Biological processes FLA-bacteria interaction under laboratory conditions, that is, a single species of FLA is cultured together with P. aeruginosa bacteria, with homogenous mixing (Figure 2) (Fumanelli et al, 2011). The model is formulated as two differential equations as follows:…”

Section: Referencesmentioning

confidence: 99%

“…These infected active amoebae can then transition to a dormant state, resulting in a bacterium-infected cyst (BC). An additional facet of this interaction is that P. multocida can rupture the infected active amoebae, F I G U R E 2 Illustration of the interplay between predator-prey and pathogen-host relationships within an FLA-bacteria system, based on the model proposed by Fumanelli et al (2011). The predator-prey relationship is indicated by a dotted arrow pointing from bacteria to amoebae, highlighting the amoeba's feeding on bacteria.…”

Section: Referencesmentioning

confidence: 99%

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Modelling dynamics between free‐living amoebae and bacteria

Ali,

Rice,

Byrne

et al. 2024

Environmental Microbiology

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Free‐living amoebae (FLA) serve as hosts for a variety of endosymbionts, which are microorganisms that reside and multiply within the FLA. Some of these endosymbionts pose a pathogenic threat to humans, animals, or both. The symbiotic relationship with FLA not only offers these microorganisms protection but also enhances their survival outside their hosts and assists in their dispersal across diverse habitats, thereby escalating disease transmission. This review is intended to offer an exhaustive overview of the existing mathematical models that have been applied to understand the dynamics of FLA, especially concerning their interactions with bacteria. An extensive literature review was conducted across Google Scholar, PubMed, and Scopus databases to identify mathematical models that describe the dynamics of interactions between FLA and bacteria, as published in peer‐reviewed scientific journals. The literature search revealed several FLA–bacteria model systems, including Pseudomonas aeruginosa, Pasteurella multocida, and Legionella spp. Although the published mathematical models account for significant system dynamics such as predator–prey relationships and non‐linear growth rates, they generally overlook spatial and temporal heterogeneity in environmental conditions, such as temperature, and population diversity. Future mathematical models will need to incorporate these factors to enhance our understanding of FLA–bacteria dynamics and to provide valuable insights for future risk assessment and disease control measures.

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Section: Referencesmentioning

confidence: 99%

Section: Referencesmentioning

confidence: 99%

Modelling dynamics between free‐living amoebae and bacteria

Ali,

Rice,

Byrne

et al. 2024

Environmental Microbiology

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“…Two examples of oyster's pathogen are Ostreid herpersvirus 1 (OsHV-1) spotted in France from the abnormal mortality crisis of C. gigas oyster, especially spat and juvenile, and Perkinsus marinus, a parasite which causes Dermo disease. A variety of host-pathogen models have been proposed in the literature [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Host-pathogen interaction for larvae oysters with salinity dependent transmission

2019

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Mathematical models of host-pathogen interactions are proposed and analyzed. Here hosts are oyster population in a free-swimming larval stage and assumably live in the closed homogeneous environment. In terms of an epidemic, they are classified into two states, namely susceptible and infectious hosts. The epidemic model of oyster hosts with seasonal forced transmission is firstly described by the SIS model where the region of attraction, the existence of equilibrium points, their stability conditions, and upper and lower bounds on the attack rate are investigated. Then free-living pathogen is introduced in the oyster area. Numerical simulations are finally carried out by making use of the various salinity-dependent transmissions in support of the hypothesis that the lower the salinity level, the lower oyster’s immunity.

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“…The model indicates that the infection time course of both Bordetellae can be separated into three distinct phases based on the most active immune processes. Co-culture experiments were performed for clinical strains of the opportunistic human pathogen Pseudomonas aeruginosa with Dictyostelium amoebae, to investigate the interactions of two organisms, such as, bacteria’s pathogenic action against amoeba [19]. A mathematical model was also developed to infer how the populations of the amoeba-bacteria system evolve considering the features of this interaction.…”

Section: Introductionmentioning

confidence: 99%

A Model of an Integrated Immune System Pathway in Homo sapiens and Its Interaction with Superantigen Producing Expression Regulatory Pathway in Staphylococcus aureus: Comparing Behavior of Pathogen Perturbed and Unperturbed Pathway

Tomar

2013

PLoS ONE

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Response of an immune system to a pathogen attack depends on the balance between the host immune defense and the virulence of the pathogen. Investigation of molecular interactions between the proteins of a host and a pathogen helps in identifying the pathogenic proteins. It is necessary to understand the dynamics of a normally behaved host system to evaluate the capacity of its immune system upon pathogen attack. In this study, we have compared the behavior of an unperturbed and pathogen perturbed host system. Moreover, we have developed a formalism under Flux Balance Analysis (FBA) for the optimization of conflicting objective functions. We have constructed an integrated pathway system, which includes Staphylococcal Superantigen (SAg) expression regulatory pathway and TCR signaling pathway of Homo sapiens. We have implemented the method on this pathway system and observed the behavior of host signaling molecules upon pathogen attack. The entire study has been divided into six different cases, based on the perturbed/unperturbed conditions. In other words, we have investigated unperturbed and pathogen perturbed human TCR signaling pathway, with different combinations of optimization of concentrations of regulatory and signaling molecules. One of these cases has aimed at finding out whether minimization of the toxin production in a pathogen leads to the change in the concentration levels of the proteins coded by TCR signaling pathway genes in the infected host. Based on the computed results, we have hypothesized that the balance between TCR signaling inhibitory and stimulatory molecules can keep TCR signaling system into resting/stimulating state, depending upon the perturbation. The proposed integrated host-pathogen interaction pathway model has accurately reflected the experimental evidences, which we have used for validation purpose. The significance of this kind of investigation lies in revealing the susceptible interaction points that can take back the Staphylococcal Enterotoxin (SE)-challenged system within the range of normal behavior.

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Mathematical modeling of bacterial virulence and host–pathogen interactions in the Dictyostelium/Pseudomonas system

Cited by 7 publications

References 12 publications

Modelling dynamics between free‐living amoebae and bacteria

Modelling dynamics between free‐living amoebae and bacteria

Host-pathogen interaction for larvae oysters with salinity dependent transmission

A Model of an Integrated Immune System Pathway in Homo sapiens and Its Interaction with Superantigen Producing Expression Regulatory Pathway in Staphylococcus aureus: Comparing Behavior of Pathogen Perturbed and Unperturbed Pathway

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