Shir Bahiri Elitzur

Abstract Detecting explosive compounds such as 2,4,6-trinitrotoluene (TNT) and its volatile byproduct 2,4-dinitrotoluene (DNT) is paramount for public health and environmental safety. In this study, we present the successful application of diverse computational and data analysis models toward developing a bacterial biosensor engineered to detect DNT with high sensitivity and specificity.

Abstract Selective pressure acts on the codon use, optimizing multiple, overlapping signals that are only partially understood. We trained artificial intelligence (AI) models to predict the codons given their amino acid sequence in the eukaryotes Saccharomyces cerevisiae and Schizosaccharomyces pombe and the bacteria Escherichia coli and Bacillus subtilis, to study the extent to which we can learn patterns in naturally occurring codons to improve predictions.

Abstract Selective pressure acts on the codon use, optimizing multiple, overlapping signals that are only partially understood. To study codon selection, we trained artificial intelligence (AI) models to predict the codons of genes given their amino acid sequence in four model organisms, the eukaryotes Saccharomyces cerevisiae and Schizosaccharomyces pombe and the bacteria Escherichia coli and Bacillus subtilis.