Plant-pathogen interactions are shaped by complex, multi-level signaling networks that ultimately control disease outcomes. By reconstructing the topology of these networks, we can begin to understand how signals are propagated and predict the genes that have the largest influence on plant phenotypes. To this end, we are using integrative multi-omics coupled with network biology to dissect the molecular basis for Fusarium Head Blight (FHB) disease of barley and wheat. We performed time course infection experiments in barley leaves and spikes to analyze host and pathogen signaling during disease progression. Tissue was collected and multi-omics profiling revealed differentially regulated transcripts, proteins, and phosphorylated peptides at specific stages of infection. Preliminary proteomics experiments quantified a total of 12,000 protein groups and 14,000 phospho-peptides, including 3,100 proteins and 4,100 phospho-peptides from F. graminearum. We use a systems-biology approach to integrate these orthogonal datasets into multi-level models of cellular signaling. Gene regulatory networks predict the transcription factors that regulate modules of differentially expressed transcripts. Phospho-signaling networks identify activated kinases and predict their phosphorylated targets. Proteins with high centrality in the reconstructed networks will be prioritized for functional validation. We are especially interested in host genes that contribute to susceptibility, as well as genes in Fusarium that regulate pathogenicity. This dual-organism, multi-omics strategy represents a powerful approach to predict and test the genes that regulate FHB and serves as a foundation for understanding and engineering disease resistance in cereal crops.