Uncovering the effects of heterogeneity and parameter sensitivity on within-host dynamics of disease: malaria as a case study.

Autor: Horn S; Department of Biochemistry, Stellenbosch University, Private Bag X1, Matieland, 7602, Stellenbosch, South Africa., Snoep JL; Department of Biochemistry, Stellenbosch University, Private Bag X1, Matieland, 7602, Stellenbosch, South Africa.; Molecular Cell Physiology, Vrije Universiteit, De Boelelaan 1087, 1081 HV, Amsterdam, The Netherlands., van Niekerk DD; Department of Biochemistry, Stellenbosch University, Private Bag X1, Matieland, 7602, Stellenbosch, South Africa. ddvniekerk@sun.ac.za.
Jazyk: angličtina
Zdroj: BMC bioinformatics [BMC Bioinformatics] 2021 Jul 24; Vol. 22 (1), pp. 384. Date of Electronic Publication: 2021 Jul 24.
DOI: 10.1186/s12859-021-04289-z
Abstrakt: Background: The fidelity and reliability of disease model predictions depend on accurate and precise descriptions of processes and determination of parameters. Various models exist to describe within-host dynamics during malaria infection but there is a shortage of clinical data that can be used to quantitatively validate them and establish confidence in their predictions. In addition, model parameters often contain a degree of uncertainty and show variations between individuals, potentially undermining the reliability of model predictions. In this study models were reproduced and analysed by means of robustness, uncertainty, local sensitivity and local sensitivity robustness analysis to establish confidence in their predictions.
Results: Components of the immune system are responsible for the most uncertainty in model outputs, while disease associated variables showed the greatest sensitivity for these components. All models showed a comparable degree of robustness but displayed different ranges in their predictions. In these different ranges, sensitivities were well-preserved in three of the four models.
Conclusion: Analyses of the effects of parameter variations in models can provide a comparative tool for the evaluation of model predictions. In addition, it can assist in uncovering model weak points and, in the case of disease models, be used to identify possible points for therapeutic intervention.
(© 2021. The Author(s).)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje