Paying less and getting more is not just a goal of wise consumers. Living cells, too, strive to make the most of their resources. This is particularly true when it comes to the most expensive cellular process in terms of energy expenditure: gene expression, or the translation of the genetic code into proteins. In my talk I will present a new study, done jointly with my students Idan Frumkin, Dvir Schirman and others, to decipher the economy of gene expression in living cells. We used a synthetic DNA collection of 14,000 versions of the same gene, each introduced into a different bacterial cell. We competed these cells in the lab in order to measure effects on doubling time and general fitness in each of them, while they are enforced to express their foreign gene. We found that while high expression of the protein is generally growth-reducing, many strategies allow cells to express the gene at high level and still enjoy high fitness, as if they were not expressing the gene. Employing diverse molecular modeling means we found genetic elements embedded within the genes that allow either economic and wasteful production of the gene. Machine learning techniques allow us to predict the cost-effectiveness of expression of genes' sequences so as to better analyze natural genes and for the design of bio technologically important genes.
Prof. Yitzhak Pilpel, Weizmann Institute of Science.