Skip Navigation

About the Mathematical Modeling and Computational Analysis Team

Pi is presented to represent the computational modeling core

The Mathematical Modeling and Computational Analysis Team mines information from the rich biological datasets coming from MaHPIC’s Experimental groups and integrates this information, together with scientific knowledge from the literature, into static and dynamic mathematical, graphical, and computational models. These models are expected to aid our understanding of the disease, lead to novel, testable hypotheses, and ultimately assist in the development of new treatment strategies.

The team uses statistical and machine learning techniques to build correlative models that identify patterns within the data, as well as correlations to relevant phenotypes such as disease severity. Informed by these patterns, the team establishes static models that elucidate the steady-state interactions between components at different biological levels of organization, from genomes, proteomes, and different metabolic pathway systems, to cell populations in the hematopoietic and immune systems or in malaria affected tissues and organs, and finally to the drivers governing the interplay between hosts and parasites.

Based on insights gained with these efforts, develop dynamic models are developed that, in addition to static interactions, account for detailed regulatory features, multi-level control mechanisms, and changes in interaction patterns over time and throughout the progression of the disease. Models are developed for each host-pathogen combination, in order to enable an assessment of the similarities and differences between model systems and to explain in finer detail the characteristics of human infections where such detailed data are not available. The models have the potential of providing unprecedented insights into the systemic and dynamic aspects of malaria, as well as other infectious diseases.   


Mideo, N., Day, T and Read, A.F. (2008). "Modelling malaria pathogenesis." Cellular Microbiology, 10(10):1947–1955. View in PubMed

Olszewski, K. L., Morrisey, J. M., Wilinski, D., Burns, J. M., Vaidya, A. B., Rabinowitz, J. D., Llinás, M. (2009). "Host parasite interactions revealed by Plasmodium falciparum metabolomics." Cell Host Microbe, 5(2):191-9. View in PubMed

Su, Y., Ruan, S., & Wei, J. (2011). "Periodicity and synchronization in blood-stage malaria infection." Journal of Mathematical Biology, 63(3): 557-574. View in PubMed

Tewa, J.J., Fokouop, R., Mewoli, B., Bowong, S. (2012). "Mathematical analysis of a general class of ordinary differential equations coming from within-hosts models of malaria with immune effectors." Applied Mathematics and Computation, 218(14): 7347-7361.

Voit, E.O. (2000). Computational Analysis of Biochemical Systems. A Practical Guide for Biochemists and Molecular Biologists, xii + 530 pp. Cambridge, U.K.: Cambridge University Press.

Voit, E.O. (2012).  A First Course in Systems Biology. Garland Science. New York, NY.