Dataset Information


A Biomathematical Model of Pneumococcal Lung Infection and Antibiotic Treatment in Mice.

ABSTRACT: Pneumonia is considered to be one of the leading causes of death worldwide. The outcome depends on both, proper antibiotic treatment and the effectivity of the immune response of the host. However, due to the complexity of the immunologic cascade initiated during infection, the latter cannot be predicted easily. We construct a biomathematical model of the murine immune response during infection with pneumococcus aiming at predicting the outcome of antibiotic treatment. The model consists of a number of non-linear ordinary differential equations describing dynamics of pneumococcal population, the inflammatory cytokine IL-6, neutrophils and macrophages fighting the infection and destruction of alveolar tissue due to pneumococcus. Equations were derived by translating known biological mechanisms and assuming certain response kinetics. Antibiotic therapy is modelled by a transient depletion of bacteria. Unknown model parameters were determined by fitting the predictions of the model to data sets derived from mice experiments of pneumococcal lung infection with and without antibiotic treatment. Time series of pneumococcal population, debris, neutrophils, activated epithelial cells, macrophages, monocytes and IL-6 serum concentrations were available for this purpose. The antibiotics Ampicillin and Moxifloxacin were considered. Parameter fittings resulted in a good agreement of model and data for all experimental scenarios. Identifiability of parameters is also estimated. The model can be used to predict the performance of alternative schedules of antibiotic treatment. We conclude that we established a biomathematical model of pneumococcal lung infection in mice allowing predictions regarding the outcome of different schedules of antibiotic treatment. We aim at translating the model to the human situation in the near future.


PROVIDER: S-EPMC4873198 | BioStudies | 2016-01-01

REPOSITORIES: biostudies

Similar Datasets

2020-01-01 | S-EPMC7714238 | BioStudies
2019-01-01 | S-EPMC6465676 | BioStudies
2010-01-01 | S-EPMC2923075 | BioStudies
2020-01-01 | S-EPMC7248076 | BioStudies
2019-01-01 | S-EPMC6331587 | BioStudies
1000-01-01 | S-EPMC2800239 | BioStudies
2016-01-01 | S-EPMC5446595 | BioStudies
2018-07-25 | GSE117580 | GEO
2019-01-01 | S-EPMC6397308 | BioStudies
2017-01-01 | S-EPMC5510101 | BioStudies