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Open Access Highly Accessed Research article

Modeling of leishmaniasis infection dynamics: novel application to the design of effective therapies

Bettina M Länger1, Cristina Pou-Barreto2, Carlos González-Alcón3, Basilio Valladares2, Bettina Wimmer1 and Néstor V Torres1*

Author Affiliations

1 Grupo de Tecnología Bioquímica. Departamento de Bioquímica y Biología Molecular. Universidad de La Laguna. 38206. San Cristóbal de La Laguna. Tenerife. Spain

2 Instituto Universitario de Enfermedades Tropicales y Salud Pública de Canarias. Universidad de La Laguna. 38206. San Cristóbal de La Laguna. Tenerife. Spain

3 Grupo de Tecnología Bioquímica. Departamento de Estadística, I.O. y Ciencias de la Computación. Universidad de La Laguna. 38206. San Cristóbal de La Laguna. Tenerife. Spain

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BMC Systems Biology 2012, 6:1  doi:10.1186/1752-0509-6-1

Published: 5 January 2012

Abstract

Background

The WHO considers leishmaniasis as one of the six most important tropical diseases worldwide. It is caused by parasites of the genus Leishmania that are passed on to humans and animals by the phlebotomine sandfly. Despite all of the research, there is still a lack of understanding on the metabolism of the parasite and the progression of the disease. In this study, a mathematical model of disease progression was developed based on experimental data of clinical symptoms, immunological responses, and parasite load for Leishmania amazonensis in BALB/c mice.

Results

Four biologically significant variables were chosen to develop a differential equation model based on the GMA power-law formalism. Parameters were determined to minimize error in the model dynamics and time series experimental data. Subsequently, the model robustness was tested and the model predictions were verified by comparing them with experimental observations made in different experimental conditions. The model obtained helps to quantify relationships between the selected variables, leads to a better understanding of disease progression, and aids in the identification of crucial points for introducing therapeutic methods.

Conclusions

Our model can be used to identify the biological factors that must be changed to minimize parasite load in the host body, and contributes to the design of effective therapies.