Malaria is one of the most devastating infectious diseases in the world. Development of novel antimalarial strategies is urgently needed due to the rapid evolution and spread of drug resistance in malaria parasites Plasmodium. The long term goal of this proposed project is to develop a systems-level understanding of the molecular basis of parasitism, pathogenesis, and drug resistance. We will implement approaches that combine machine learning, probabilistic modeling, and genome-wide association analysis to develop more robust computational solutions and identify a comprehensive set of genes or gene products in biological networks that show an increase in genetic variability that can be associated with drug resistance, pathogenesis, virulence, responses to environmental challenges, or with other interesting phenotypes. The three specific aims are: 1. To identify network components using effective remote homology based methods. We will address a critical barrier in malaria research: our inability to assign functional annotation to over 60% of the predicted gene products in the genome of Plasmodium falciparum. We will use a machine learning approach to detect evolutionarily conserved characteristics of the genes/proteins for network inference. 2. To infer the topology and dynamic interplay of cellular networks. Robust models will be developed to reconstruct the gene regulatory networks, signaling cascades and metabolic pathways that define the genetic basis for disease phenotypes. 3. To identify evolutionary signatures of network models by genome-wide association studies (GWAS). GWAS including Single Nucleotide Polymorphism (SNP) screening of multiple strains with varying phenotypes will serve as an effective means for high throughput wet-lab validations of networks in response to drug treatment. Such networks are the cornerstones of a systems-level view of pathogen biology, a view that will allow us to transform disparate types of data into biological insights for drug development.

Public Health Relevance

Malaria remains one of the most important infectious diseases in the world today, infecting 300-500 million people yearly and resulting in 1-2 million deaths, primarily of young children. This study will develop computational solutions to problems that hitherto have prevented us from gaining a global view of how infection by the malaria parasite leads to the development of the disease. This global overview will help us develop specific solutions to the problems of preventing and treating malaria.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Enhancement Award (SC1)
Project #
Application #
Study Section
Special Emphasis Panel (ZGM1)
Program Officer
Barski, Oleg
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Texas Health Science Center San Antonio
Schools of Arts and Sciences
San Antonio
United States
Zip Code
Leopold Wager, Chrissy M; Hole, Camaron R; Campuzano, Althea et al. (2018) IFN-? immune priming of macrophages in vivo induces prolonged STAT1 binding and protection against Cryptococcus neoformans. PLoS Pathog 14:e1007358
Lee, Grace C; Dallas, Steven D; Wang, Yufeng et al. (2017) Emerging multidrug resistance in community-associated Staphylococcus aureus involved in skin and soft tissue infections and nasal colonization. J Antimicrob Chemother 72:2461-2468
Raphael, Itay; Webb, Johanna; Gomez-Rivera, Francisco et al. (2017) Serum Neuroinflammatory Disease-Induced Central Nervous System Proteins Predict Clinical Onset of Experimental Autoimmune Encephalomyelitis. Front Immunol 8:812
Cooper, Daniel J; Chen, I-Chung; Hernandez, Christine et al. (2017) Pluripotent cells display enhanced resistance to mutagenesis. Stem Cell Res 19:113-117
Wang, Peng; Yu, Zhuoteng; Santangelo, Thomas J et al. (2017) BosR Is A Novel Fur Family Member Responsive to Copper and Regulating Copper Homeostasis in Borrelia burgdorferi. J Bacteriol 199:
Shen-Gunther, Jane; Wang, Yufeng; Lai, Zhao et al. (2017) Deep sequencing of HPV E6/E7 genes reveals loss of genotypic diversity and gain of clonal dominance in high-grade intraepithelial lesions of the cervix. BMC Genomics 18:231
Zhou, Zhan; Sun, Ning; Wu, Shanshan et al. (2016) Genomic data mining reveals a rich repertoire of transport proteins in Streptomyces. BMC Genomics 17 Suppl 7:510
Chen, I-Chung; Hernandez, Christine; Xu, Xueping et al. (2016) Dynamic Variations in Genetic Integrity Accompany Changes in Cell Fate. Stem Cells Dev 25:1698-1708
Lee, Grace C; Hall, Ronald G; Boyd, Natalie K et al. (2016) A prospective observational cohort study in primary care practices to identify factors associated with treatment failure in Staphylococcus aureus skin and soft tissue infections. Ann Clin Microbiol Antimicrob 15:58
McCarrey, John R; Lehle, Jake D; Raju, Seetha S et al. (2016) Tertiary Epimutations - A Novel Aspect of Epigenetic Transgenerational Inheritance Promoting Genome Instability. PLoS One 11:e0168038

Showing the most recent 10 out of 26 publications