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Many thousands of proteins encoded by the genome of  Plasmodium falciparum,  the causal organism of the deadliest form of human malaria, are of unknown function. It is of utmost importance that these proteins be characterized if we are to develop combative strategies against malaria based on the biology of the parasite. In an attempt to infer protein function on a genome-wide scale, we computationally modeled the  P. falciparum  interactome, elucidating local and global functional relationships between gene products. The resulting interaction network, reconstructed by integrating in silico and experimental functional genomics data within a Bayesian framework, covers ∼68% of the parasite genome and provides functional inferences for more than 2000 uncharacterized proteins, based on their associations. Network reconstruction involved the use of a novel strategy, where we incorporated continuously updated, uniform reference priors in our Bayesian model. This method for generating interaction maps is thus also well suited for application to other genomes, where pre-existing interactome knowledge is sparse. Additionally, we superimposed this map on genomes of three apicomplexan pathogens— Plasmodium yoelii,  Toxoplasma gondii,  and  Cryptosporidium parvum —describing relationships between these organisms based on retained functional linkages. This comparison provided a glimpse of the highly evolved nature of  P. falciparum ; for instance, a deficit of nearly 26% in terms of predicted interactions is observed against  P. yoelii,  because of missing ortholog partners in pairs of functionally linked proteins.

Computational modeling of the Plasmodium falciparum interactome reveals protein function on a genome-wide scale