Sponsored Research: MaHPIC

This is a project of the Biomathematics Research Group

The MaHPIC combines Emory investigators’ interdisciplinary experience in malaria research, metabolomics, lipidomics and human and non-human primate immunology and pathogenesis with UGA’s expertise in pathogen bioinformatics and large database systems, and Georgia Tech’s experience in mathematical modeling and systems biology. The CDC has provided support in proteomics and malaria research, including nonhuman primate and vector/mosquito infections. NIAID Grant HHSN272201200031C

Funding agency:	National Institute of Allergy and Infectious Diseases
Research Team:	Mary Galinski (PI), Emory University Alberto Moreno (Co-PI), Emory University Jessica Kissinger (Co-PI), University of Georgia (Co-PI) Co-Investigators from the BRG Mehdi Asefi,  Jessica Brady, Juan B. Gutierrez,  Seyedamin Pouriyeh, Saeid Safaei,  Elizabeth Trippe, Zerotti Woods, Yi Heng Yan
Collaborators:	University of Georgia Emory University Georgia Institute of Technology Eight experimental and analytical research teams comprise the core infrastructure of the MaHPIC: Malaria, Immune Profiling, Functional Genomics, Proteomics, Metabolomics, Lipidomics, Informatics Core, Mathematical Modeling and Computational Analysis.
Funding:	$1.8M (UGA) of $19.5M (Project Total)
Project Period:	2012-2017
Media:	Emory news center:  NIH awards Georgia malaria research consortium up to $19.4 million contract Atlanta Business Chronicle:  Emory, GT, UGA partnership gets $19.5M for malaria UGA TODAY:  UGA partners with Emory, Georgia Tech and CDC Foundation on malaria research center NPR (WABE90.1 Where ATL meets NPR):  19.4 Million Dollar Grant Forms Georgia Malaria Research Team TheRed&Black:  University, state continues history of malaria research Vaccine News (VN daily):  Researchers to open new malaria center in Georgia Downtown.11alive.com (NBS):  UGA, Ga. Tech, Emory team up for malaria research The Augusta Chronicle:  Malaria researchers in Ga. get federal contract

 

MaHPIC involves the multidisciplinary study of malaria infections, immunity and pathogenesis of P. falciparum, P. vivax and P. knowlesi in the context of host-pathogen interactions, in humans and nonhuman primates, using a systems biology approach. Three nonhuman primate malaria species will be studied: P. coatneyi to model P. falciparum, P. cynomolgi to model P. vivax, and P. knowlesi, a monkey malaria species that has been causing illness and cases of death in humans in Southeast Asia.

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.