Malaria, prevalent in over 100 countries, is one of the most widespread infectious diseases. It is caused by unicellular parasites of the genus Plasmodium, and usually begins with a bite from an infected mosquito. Despite a need, no effective vaccines currently exist and resistance has developed to several antimalarial drugs. Much of the molecular and systems-scale work studying malaria to date has used in vitro systems, limiting the applicability of results as such systems do not account for host-pathogen interactions. In order to improve our understanding of the molecular, cellular, immunological, and pathogenesis mechanisms of two major human malaria pathogens, namely P. falciparum and P. vivax, we propose here to use mathematical and computational modeling approaches to analyze and integrate different types of “-omic” data sets, including genomic, proteomic, lipidomic and metabolomics data that are generated by our collaborators from non-human primate model systems and their corresponding malaria parasites. Machine learning techniques will be used to reveal patterns in the data as well as to build up predictive models. Bayesian networks will be inferred to identify connectivities and correlation among each “-omic” data set and between different data sets. The resulting network structures will then be compared and interpreted for different parasite and host species, and will be used to guide the construction of mathematical models of different processes in the host and pathogen, as well as the design of subsequent experiments.
Related Publications:
- Comparative transcriptomics and metabolomics in a rhesus macaque drug administration study
- A tree-like Bayesian structure learning algorithm for small-sample datasets from complex biological model systems
- From genome-scale data to models of infectious disease: A Bayesian network-based strategy to drive model development
- Integrative analysis associates monocytes with insufficient erythropoiesis during acute Plasmodium cynomolgi malaria in rhesus macaques