Using predictive models to engineer biology: a case study in codon optimization

Given recent advances in synthetic biology and DNA synthesis, there is an increasing need for carefully engineered biological parts (e.g. genes, promoter sequences or enzymes) and circuits. However, forward engineering approaches are thus far rarely used in biology due to lack of detailed knowledge of the biological mechanisms. We describe a framework that enables forward engineering in biology by constructing models predictive of properties of interest, then inverting and using these models to design biological parts. We demonstrate the applicability of the proposed framework on the problem of codon optimization, concerned with optimizing gene coding sequences for efficient translation. Results suggest that our d ata-driv e n cod on o ptimizatio n (DECODON) method simultaneously considers the effects multiple translation mechanisms to produce optimal sequences, in contrast to existing codon optimization techniques.