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WIREs Syst Biol Med
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Systems analysis of host–parasite interactions

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Parasitic diseases caused by protozoan pathogens lead to hundreds of thousands of deaths per year in addition to substantial suffering and socioeconomic decline for millions of people worldwide. The lack of effective vaccines coupled with the widespread emergence of drug‐resistant parasites necessitates that the research community take an active role in understanding host–parasite infection biology in order to develop improved therapeutics. Recent advances in next‐generation sequencing and the rapid development of publicly accessible genomic databases for many human pathogens have facilitated the application of systems biology to the study of host–parasite interactions. Over the past decade, these technologies have led to the discovery of many important biological processes governing parasitic disease. The integration and interpretation of high‐throughput ‐omic data will undoubtedly generate extraordinary insight into host–parasite interaction networks essential to navigate the intricacies of these complex systems. As systems analysis continues to build the foundation for our understanding of host–parasite biology, this will provide the framework necessary to drive drug discovery research forward and accelerate the development of new antiparasitic therapies. WIREs Syst Biol Med 2015, 7:381–400. doi: 10.1002/wsbm.1311 This article is categorized under: Developmental Biology > Developmental Processes in Health and Disease Models of Systems Properties and Processes > Organismal Models Biological Mechanisms > Regulatory Biology
Percentage of ‘hypothetical’ genes and relative community size for important unicellular human pathogens and their model organisms. (a) The percentage of ‘hypothetical’ genes for selected prokaryotic and eukaryotic pathogens compared to their relevant model organism, Escherichia coli and Saccharomyces cerevisiae, respectively. Percentages for each species were calculated from the number of genes including ‘hypothetical,’ ‘unknown,’ or ‘uncharacterized’ in the gene description compared to the total number of pathogen genes from the NCBI database for model organisms and bacterial pathogens, and from the corresponding EuPathDB databases for protozoan pathogens. (b) The relative community size for model organisms, and the mean relative community size for the bacterial and protozoan pathogens listed in A, based on the number of results generated from a Pubmed (http://www.ncbi.nlm.nih.gov/pubmed) search of the species name.
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Organization, integration, and analysis of ‐omic datasets in metabolic network reconstructions used in constraint‐based modeling. Moving from left to right in the figure, various ‐omic data types (transcriptomic, proteomic, and metabolomic) are mapped onto the different components of the model. This includes the genes, enzymes, or small metabolites within a network for every reaction in the reconstruction (for which such data are available). Host–pathogen models can be constructed by connecting (or infecting) a host cell with the intracellular parasite. Subsequent simulations may characterize differences in the flux states in the noninfected versus infected state of the host cell. A gene–protein‐reaction relationship for Plasmodium succinate dehydrogenase is highlighted in the figure.
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Distribution of transcriptomic and proteomic datasets uploaded to EuPathDB for selected Protozoan parasites. The number of transcriptomic and proteomic datasets submitted to the EuPathDB family of databases (see Table ) for each Protozoan parasite genus. The total number of datasets is plotted for each parasite group, with the proportion of transcriptomic datasets (colored in red) and proteomic datasets (colored in blue) displayed within each bar graph.
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Models of Systems Properties and Processes > Organismal Models
Developmental Biology > Developmental Processes in Health and Disease
Biological Mechanisms > Regulatory Biology

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