The currently used mathematical models for medical treatment at the individual or population level are largely phenomenological and have limited quantitative predictive power. It is usually not possible to predict the effect of an intervention in a specific process or to predict the effect of a pharmaceutical drug since the step or enzyme on which the intervention/drug works is not explicit in the model.
Taking HIV pathogenesis as an example, the immune system response, vaccine exposure, and drug
Hypoglycaemia and lactic acidosis are key diagnostics for poor chances of survival in malaria patients. In this project we aim to test to what extent the metabolic activity of Plasmodium falciparum contributes to a changed glucose metabolism in malaria patients. The approach is to start with detailed bottom up models for the parasite and then merge these with more coarse grained models at the whole body level.
We propose a hierarchical modelling approach to construct models for disease states at the whole-body level. Such models can simulate effects of drug-induced inhibition of reaction steps on the whole-body … physiology. We illustrate the approach for glucose metabolism in malaria patients, by merging two detailed kinetic models for glucose metabolism in the parasite Plasmodium falciparum and the human red blood cell with a coarse-grained model for whole-body glucose metabolism. In addition we use a genome-scale metabolic model for the parasite to predict amino acid production profiles by the malaria parasite that can be used as a complex biomarker.