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

Abstract

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.

A Colorful Activity to Introduce Kinetics and the Rate-Determining Step: Application to Metabolism and Metabolic Control Analysis

“…Metabolic control analysis involves systematic variation of the parameters–such as substrate, effector, inhibitor, or enzyme concentration–and measuring the resulting change in metabolite concentration or flux through the pathway. One goal of such studies is to construct or improve upon mathematical models that accurately simulate the pathway or pathways of interest. ,,, Recently metabolic control analysis has been applied to the study of a diverse array of phenomena such as the generation of circadian rhythms in metabolic pathways, the major control points for metabolism in cancer cells, − the response of heart cells to anaerobic conditions, amino acid biosynthesis in bacteria, glycogen synthesis in the liver, the response of the host to malaria infection, the identification of potential drug targets in Trypanosoma cruzi , and the optimization of biotechnology products produced in microbes to name a few . Additionally, the type of thinking one does in MCA is applicable to the analysis and improvement of virtually any complex system.…”