An Efficient Moments-Based Inference Method for Within-Host Bacterial Infection Dynamics

Price, David J.; Breuzé, Alexandre; Dybowski, Richard; Mastroeni, Piero; Restif, Olivier

doi:10.1101/116319

Cited by 2 publications

(3 citation statements)

References 39 publications

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“…To avoid the step of solving the probability density function characterizing the multivariate bacterial distribution, we use summary statistics to capture the necessary features of interest. Our models only incorporate zero-order dynamics, thus the first- (mean numbers of bacteria in each organ) and second-order (variance–covariance matrix of bacterial numbers in all organs) moments suffice to describe the distribution [44].…”

Section: Methodsmentioning

confidence: 99%

“…As the multivariate distribution is characterized by a set of first- and second-order moments, it is possible to explore parameter value combinations with the aim of reaching a distribution that approximates the experimentally observed bacterial distribution as closely as possible. This is achieved by minimizing a quantity (cost function) that cumulatively expresses the divergences between each experimentally observed and predicted pair of moments [44]. In this case, we choose the Kullback–Leibler (KL) divergence as the cost function to be minimized.…”

Section: Methodsmentioning

confidence: 99%

“…Parameter inference was performed using the freely available SPEEDI.R package [45] and the Nelder & Mead [46] optimization algorithm.…”

Section: Methodsmentioning

confidence: 99%

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A data-based mathematical modelling study to quantify the effects of ciprofloxacin and ampicillin on the within-host dynamics of Salmonella enterica during treatment and relapse

Vlazaki

Rossi

Price

et al. 2020

J. R. Soc. Interface.

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Antibiotic therapy has drastically reduced the mortality and sequelae of bacterial infections. From naturally occurring to chemically synthesized, different classes of antibiotics have been successfully used without detailed knowledge of how they affect bacterial dynamics in vivo . However, a proportion of patients receiving antimicrobial therapy develop recrudescent infections post-treatment. Relapsing infections are attributable to incomplete clearance of bacterial populations following antibiotic administration; the metabolic profile of this antibiotic-recalcitrant bacterial subpopulation, the spatio-temporal context of its emergence and the variance of antibiotic–bacterial interactions in vivo remain unclear. Here, we develop and apply a mechanistic mathematical model to data from a study comparing the effects of ciprofloxacin and ampicillin on the within-host dynamics of Salmonella enterica serovar Typhimurium in murine infections. Using the inferential capacity of our model, we show that the antibiotic-recalcitrant bacteria following ampicillin, but not ciprofloxacin, treatment belong to a non-replicating phenotype. Aligning with previous studies, we independently estimate that the lymphoid tissues and spleen are important reservoirs of non-replicating bacteria. Finally, we predict that post-treatment, the progenitors of the non-growing and growing bacterial populations replicate and die at different rates. Ultimately, the liver, spleen and mesenteric lymph nodes are all repopulated by progenitors of the previously non-growing phenotype in ampicillin-treated mice.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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A data-based mathematical modelling study to quantify the effects of ciprofloxacin and ampicillin on the within-host dynamics of Salmonella enterica during treatment and relapse

Vlazaki

Rossi

Price

et al. 2020

J. R. Soc. Interface.

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Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread

2019

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Infectious diseases are the primary cause of mortality worldwide. The dangers of infectious disease are compounded with antimicrobial resistance, which remains the greatest concern for human health. Although novel approaches are under investigation, the World Health Organization predicts that by 2050, septicaemia caused by antimicrobial resistant bacteria could result in 10 million deaths per year. One of the main challenges in medical microbiology is to develop novel experimental approaches, which enable a better understanding of bacterial infections and antimicrobial resistance. After the introduction of whole genome sequencing, there was a great improvement in bacterial detection and identification, which also enabled the characterization of virulence factors and antimicrobial resistance genes. Today, the use of in silico experiments jointly with computational and machine learning offer an in depth understanding of systems biology, allowing us to use this knowledge for the prevention, prediction, and control of infectious disease. Herein, the aim of this review is to discuss the latest advances in human health engineering and their applicability in the control of infectious diseases. An in-depth knowledge of host–pathogen–protein interactions, combined with a better understanding of a host’s immune response and bacterial fitness, are key determinants for halting infectious diseases and antimicrobial resistance dissemination.

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An Efficient Moments-Based Inference Method for Within-Host Bacterial Infection Dynamics

Cited by 2 publications

References 39 publications

A data-based mathematical modelling study to quantify the effects of ciprofloxacin and ampicillin on the within-host dynamics of Salmonella enterica during treatment and relapse

A data-based mathematical modelling study to quantify the effects of ciprofloxacin and ampicillin on the within-host dynamics of Salmonella enterica during treatment and relapse

Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread

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