Malaria is a global infectious disease that threatens the lives of millions of 
people. Transcriptomics, proteomics and functional genomics studies, as well as 
sequencing of the Plasmodium falciparum and Homo sapiens genomes, have shed new 
light on this host-parasite relationship. Recent advances in accurate mass 
measurement mass spectrometry, sophisticated data analysis software, and 
availability of biological pathway databases, have converged to facilitate our 
global, untargeted biochemical profiling study of in vitro P. 
falciparum-infected (IRBC) and uninfected (NRBC) erythrocytes. In order to 
expand the number of detectable metabolites, several key analytical steps in our 
workflows were optimized. Untargeted and targeted data mining resulted in 
detection of over one thousand features or chemical entities. Untargeted 
features were annotated via matching to the METLIN metabolite database. For 
targeted data mining, we queried the data using a compound database derived from 
a metabolic reconstruction of the P. falciparum genome. In total, over one 
hundred and fifty differential annotated metabolites were observed. To 
corroborate the representation of known biochemical pathways from our data, an 
inferential pathway analysis strategy was used to map annotated metabolites onto 
the BioCyc pathway collection. This hypothesis-generating approach resulted in 
over-representation of many metabolites onto several IRBC pathways, most 
prominently glycolysis. In addition, components of the "branched" TCA cycle, 
partial urea cycle, and nucleotide, amino acid, chorismate, sphingolipid and 
fatty acid metabolism were found to be altered in IRBCs. Interestingly, we 
detected and confirmed elevated levels for cyclic ADP ribose and phosphoribosyl 
AMP in IRBCs, a novel observation. These metabolites may play a role in 
regulating the release of intracellular Ca(2+) during P. falciparum infection. 
Our results support a strategy of global metabolite profiling by untargeted data 
acquisition. Untargeted and targeted data mining workflows, when used together 
to perform pathway-inferred metabolomics, have the benefit of obviating MS/MS 
confirmation for every detected compound.