Sedgwick Ruby, Goertz John P, Stevens Molly M, Misener Ruth, van der Wilk Mark
Department of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London, London.
Department of Computing, Imperial College London, London.
Biotechnol Bioeng. 2025 Jan;122(1):189-210. doi: 10.1002/bit.28854. Epub 2024 Oct 16.
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.
随着工程化生物分子装置的兴起，对定制生物序列的需求日益增加。通常，为了特定应用需要制备许多相似的生物序列，这意味着需要进行大量（有时成本高得令人望而却步）的实验室实验来对其进行优化。本文提出了一种实验工作流程的迁移学习设计，以使这种开发可行。通过将迁移学习替代模型与贝叶斯优化相结合，我们展示了如何通过在优化任务之间共享信息来减少实验总数。我们使用用于基于扩增的诊断分析的DNA竞争物开发数据来证明实验次数的减少。我们使用交叉验证来比较不同迁移学习模型的预测准确性，然后比较模型在单目标和惩罚优化任务中的性能。
