An integrative approach to infer dynamic protein-protein interaction networks: the case of Plasmodium falciparum interactomes

Data integration approaches use data from diverse high-throughput experiments (-omics data) to derive a unified model of a biological system by overcoming the limitation of individual data sets. These approaches make it possible to highlight relationships between biological entities facilitating the generation of new hypotheses and directions for future experimental work. In this project, a Bayesian data integration approach was applied to predict the protein-protein interaction network of the malaria parasite Plasmodium falciparum. We demonstrated that the integration of spatio-temporal information led to an improvement in the predictive performance of the approach. We derived interactomes at different moments of the parasite life cycle and, by grouping the proteins according to their functional categories, we showed how proteins involved in stage-specific processes form peripherally located modules, while proteins involved in constitutive processes form the central scaffold of the network. Furthermore, by using additional metadata to include “phenotype” information, we obtained encouraging results that, in principle, could be used to improve the prediction of protein/subnetwork function.