ABSTRACT Plasmodium falciparum resistance to the antimalarial artemisinin threatens the great steps forward made in the last decade to control malaria infection. We lack a comprehensive understanding on the genetic determinants of drug resistance, and how the emergence of resistance conferring mutations impacts the cellular state of the P. falciparum malaria parasite. Experimental genetic crosses are an extremely precise means to identify the genetic determinants of resistance, and a powerful framework to examine the consequences of acquiring resistance at a genetic, transcriptional, proteomic and metabolomic level. By performing a series of inter-related genetic crosses in a groundbreaking humanized mouse model we will interrogate the systems biology of artemisinin resistance. The Genomics Core will further this goal by generating `omics data for this P01 (broadly combining genomics, transcriptomics, proteomics and metabolomics). We will perform whole genome sequencing of each of the progeny of the genetic crosses, mapping short reads to the 23 megabase genome of this eukaryotic pathogen to identify genetic markers for examining patterns of inheritance in P. falciparum (RP01), and quantitative trait loci (QTL) analysis of drug resistance phenotypes (RP02). Additionally, we will perform time course analyses of expression profiles and metabolite levels to identify expression QTLs and metabolite QTLs, and proteomic profiling to identify protein QTLs (RP03). Each of these disparate technologies has independent pitfalls and require highly specialized skills to generate reliable data. We have assembled a team of experts in genome and transcriptome sequencing, proteomics and metabolomics. This team will collaboratively tackle the formidable technical challenge of profiling malaria parasites across the `omics landscape. To translate these technologies into a usable output for the investigators across the P01, and for the malaria community at large, we will work closely with the Data Integration and Analysis Core (Core B) to generate an “analysis-ready” database containing the phenotypic, genetic, transcriptomic, proteomic and metabolic data for each of the progeny, and carry with it essential metadata and detailed workflows for robust flow of information throughout the project.