With the recent advances in genomic, transcriptomic and proteomic technologies, it is now possible to measure the changes in the levels of all mRNAs and proteins in the cell, under different environmental and genetic perturbations. The large amts. of data from transcriptomic and proteomic studies require development of efficient systems-wide math. and computational frameworks to analyze and interpret the data. We have developed a genome-wide, mechanistic, math. model for the translation machinery in S. cerevisiae which enables us to predict the changes in protein levels in response to changes in mRNA levels. Our studies show that the system-wide competition for the different components of the translation machinery, the ribosomes and the tRNAs, leads to a complex, not "one-to-one" relationship between mRNA and protein expression. We identify the "operating" regime of each of the mRNAs and provide insights into how the expression of proteins corresponding to each mRNA is regulated. Our results have implications in design of rational artificial protein prodn. systems, wherein quant. knowledge of responses of protein expression to changes in the cellular environment, can be used to optimize a cellular system towards the prodn. of a protein of interest. [on SciFinder (R)]