With the advent of complete genome sequences, large-scale functional analyses are generating new excitement in biology and medicine. To facilitate genomewide functional analyses, we developed a high-density cell array with quantitative and automated readout of cell fitness. Able to print at > x 10 higher density on a standard microtiter plate area than currently possible, our cell array allows single-plate screening of the complete set of Saccharomyces cerevisiae gene-deletion library and significantly reduces the amount of small molecules and other materials needed for the study. We used this method to map the relation between genes and cell fitness in response to rapamycin, a medically important natural product that targets the eukaryotic kinase Tor. We discuss the implications for pharmacogenomics and the uncharted complexity in genotype-dependent drug response in molecularly targeted therapies. Our analysis leads to several basic findings, including a class of gene deletions that confer better fitness in the presence of rapamycin. This result provides insights into possible therapeutic uses of rapamycin/CCI-779 in the treatment of neurodegenerative diseases (including Alzheimer's, Parkinson's, and Huntington's diseases), and cautions the possible existence of similar rapamycin-enhanceable mutations in cancer. It is well established in yeast that although TOR2 has a unique rapamycin-insensitive function, TOR1 and TOR2 are interchangeable in the rapamycin-sensitive functions. We show that even the rapamycin-sensitive functions are distinct between TOR1 and TOR2 and map the functional difference to a approximately 120-aa region at the N termini of the proteins. Finally, we discuss using cell-based genomic pattern recognition in designing electronic or optical biosensors.