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Modelling pathogen load dynamics to elucidate mechanistic determinants of host–Plasmodium falciparum interactions


During infection, increasing pathogen load stimulates both protective and harmful aspects of the host response. The dynamics of this interaction are hard to quantify in humans, but doing so could improve understanding of the mechanisms of disease and protection. We sought to model the contributions of the parasite multiplication rate and host response to observed parasite load in individual subjects infected with Plasmodium falciparum malaria, using only data obtained at the time of clinical presentation, and then to identify their mechanistic correlates. We predicted higher parasite multiplication rates and lower host responsiveness in cases of severe malaria, with severe anaemia being more insidious than cerebral malaria. We predicted that parasite-growth inhibition was associated with platelet consumption, lower expression of CXCL10 and type 1 interferon-associated genes, but increased cathepsin G and matrix metallopeptidase 9 expression. We found that cathepsin G and matrix metallopeptidase 9 directly inhibit parasite invasion into erythrocytes. The parasite multiplication rate was associated with host iron availability and higher complement factor H levels, lower expression of gametocyte-associated genes but higher expression of translation-associated genes in the parasite. Our findings demonstrate the potential of using explicit modelling of pathogen load dynamics to deepen understanding of host–pathogen interactions and identify mechanistic correlates of protection.

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Fig. 1: Estimating the dynamics of parasite load and host response in malaria.
Fig. 2: Contribution of parasite-load dynamics to severe malaria phenotype.
Fig. 3: The protective effect of platelets is revealed by estimating PGI.
Fig. 4: Transcriptional correlates of PGI.
Fig. 5: Effects of cathepsin G and MMP9 on parasite growth and expression of erythrocyte invasion receptors.
Fig. 6: Parasite gene-expression modules associated with predicted m.

Data availability

Estimates of parameters determining within-host dynamics in the malariatherapy dataset were obtained from ref. 4, whose corresponding author may be contacted at RNA-seq data have been deposited in the ArrayExpress database at EMBL-EBI ( under the accession number E-MTAB-6413. Individual subject-level data is available within the paper and its Supplementary information files. All other data that support the findings of this study are available from the corresponding author on reasonable request.

Code availability

The source code for the model simulating Gambian child subjects and examples of its use are presented as Supplementary Code library file and Supplementary Code example file.


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We are grateful to K. Dietz for providing the original data and parameter estimates from malariatherapy patients, and his model, and to the St. Mary’s NHLI FACS core facility and Y. Guo for support and instrumentation. This work was supported by the Medical Research Council (MRC) UK via core funding to the malaria research programme at the MRC Unit, The Gambia; the UK MRC and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement that is also part of the EDCTP2 program supported by the European Union (MR/L006529/1 to A.J.C.); a Wellcome Trust Value In People Award to A.J.C. and the European Union’s seventh Framework program under EC-GA no. 279185 (EUCLIDS;

Author information




A.J.C., A.G., A.E.v.B., F.F., D.J.C., D.N. and M.W. collected the data used in the study. A.J.C., E.M.R., M.T.B., M.W. and D.J.C. designed the study. A.J.C. and M.T.B. developed the mathematical model. A.J.C., M.T.B., H.J.L., F.F., T.D.O. and A.E.v.B. analysed the data. D.W., T.W.K, D.N., U.D., E.M.R., M.L., L.J.C., A.G., D.J.C. and A.J.C. supervised aspects of the project. All authors contributed to the interpretation of the results and drafting of the manuscript.

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Correspondence to Aubrey J. Cunnington.

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The authors declare no competing interests.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–6, Supplementary Tables 1–12, Supplementary dataset legends.

Reporting Summary

Supplementary Data 1

Data from the Gambian children with malaria

Supplementary Code Example File

R example file for supplementary code for the mathematical model.

Supplementary Code Explanation File

README file explaining supplementary code.

Supplementary Code Library File

R library file with supplementary code for the mathematical model.

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Georgiadou, A., Lee, H.J., Walther, M. et al. Modelling pathogen load dynamics to elucidate mechanistic determinants of host–Plasmodium falciparum interactions. Nat Microbiol 4, 1592–1602 (2019).

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