Fundamental computer science concepts have inspired novel information-processing molecular systems in test tubes(1)(-13) and genetically encoded circuits in live cells(14)(-21). Recent research has shown that digital information storage in DNA, implemented using deep sequencing and conventional software, can approach the maximum Shannon information capacity(22) of two bits per nucleotide(23). In nature, DNA is used to store genetic programs, but the information content of the encoding rarely approaches this maximum(24). We hypothesize that the biological function of a genetic program can be preserved while reducing the length of its DNA encoding and increasing the information content per nucleotide. Here we support this hypothesis by describing an experimental procedure for compressing a genetic program and its subsequent autonomous decompression and execution in human cells. As a test-bed we choose an RNAi cell classifier circuit(25) that comprises redundant DNA sequences and is therefore amenable for compression , as are many other complex gene circuits(15)(,18,26-28). In one example, we implement a compressed encoding of a ten-gene four-input AND gate circuit using only four genetic constructs. The compression principles applied to gene circuits can enable fitting complex genetic programs into DNA delivery vehicles with limited cargo capacity, and storing compressed and biologically inert programs in vivo for on-demand activation.