Transfer learning Bayesian optimization for competitor DNA molecule design for use in diagnostic assays

• Published in: Biotechnology and bioengineering 2025-01, Vol.122 (1), p.189-210
• Main Authors: Sedgwick, Ruby, Goertz, John P., Stevens, Molly M., Misener, Ruth, van der Wilk, Mark
• Format: Article
• Language: English
• Subjects: Bayes Theorem; Bayesian analysis; Bayesian optimization; Deoxyribonucleic acid; design of experiments; Design optimization; DNA; DNA - chemistry; DNA design; Gaussian processes; Gene sequencing; Machine Learning; Mathematical models; Nucleotide sequence; polymerase chain reaction; Transfer learning; Workflow
• ISSN: 0006-3592; 1097-0290; 1097-0290
• DOI: 10.1002/bit.28854

With the rise in engineered biomolecular devices, there is an increased need for tailor‐made biological sequences. Often, many similar biological sequences need to be made for a specific application meaning numerous, sometimes prohibitively expensive, lab experiments are necessary for their optimization. This paper presents a transfer learning design of experiments workflow to make this development feasible. By combining a transfer learning surrogate model with Bayesian optimization, we show how the total number of experiments can be reduced by sharing information between optimization tasks. We demonstrate the reduction in the number of experiments using data from the development of DNA competitors for use in an amplification‐based diagnostic assay. We use cross‐validation to compare the predictive accuracy of different transfer learning models, and then compare the performance of the models for both single objective and penalized optimization tasks. Often, many similar biological sequences need to be made for a specific application meaning numerous, sometimes prohibitively expensive, lab experiments are necessary for their optimization. This paper presents a transfer learning design of experiments workflow to make this development feasible. By combining a transfer learning surrogate model with Bayesian optimization, we show how the total number of experiments can be reduced by sharing information between optimization tasks.