Complex diseases are due to an interplay between multiple parameters such as the genome, the transcriptome, the proteome and the environment. To begin to understand this, requires advances in accurate data acquisition, better ways of integrating data from different labs/centres and across different omic platforms and advances in data analysis and visualization. Here I will describe two examples arising from work conducted in our lab that highlight the need for advances in these areas. The first and most challenging is in studying the gene-environment interaction. Here studies are underway involving different genetic strains of flies, mice and humans to establish that there is clearly a diverse interaction between genes and the environment followed by a mechanistic explanation for this interaction. The second involves our studies of the biochemistry of exercise. Exercise is one of the most potent therapeutics for a number of complex diseases and there is considerable interest in mapping pathways triggered by exercise that encode the health benefits. We have used quantitative mass spectrometry to measure >1,000 changes in protein phosphorylation in response to one bout of exercise in humans. This data set provides a biochemical map with which to deconvolute the protein kinase network that is triggered by exercise to coordinate a series of changes that ultimately lead to improved health.