所以，曼彻斯特大学的精细化合物和特种化合物合成生物中心的Nigel S. Scrutton的团队在这里设计和发展了一个简化自动化、综合的、适用于所有化合物，而且无需知道之前有否成功的策略的DBTL框架。作者们用统计取样的方法考虑了少数的设计参数，大量的途径里的变数，再加上自动的实验程序，就能够成功 地把框架的原型快速地制造出来。他们用这个方法来改善类黄酮被大肠杆菌生产的产量，考虑了生产类黄酮的分子途径里四个重要的酶，和每个酶的四个重要的参数和相关的变数，总共两千多个可能的组态。仅用了两个DBTL的周期，他们就把可能的组态的数目大规模地缩小，然后试验，最后把类黄酮的产量增加到原来的程序所能达到的500 倍。
作者们认为，这是把DBTL 周期自动化和发展微生物化学工程的重要一步。
摘要：The microbial production of fine chemicals provides a promising biosustainable manufacturing solution that has led to the successful production of a growing catalog of natural products and high-value chemicals. However, development at industrial levels has been hindered by the large resource investments required. Here we present an integrated Design–Build-Test–Learn (DBTL) pipeline for the discovery and optimization of biosynthetic pathways, which is designed to be compound agnostic and automated throughout. We initially applied the pipeline for the production of the flavonoid (2S)-pinocembrin in Escherichia coli, to demonstrate rapid iterative DBTL cycling with automation at every stage. In this case, application of two DBTL cycles successfully established a production pathway improved by 500-fold, with competitive titers up to 88 mg L−1. The further application of the pipeline to optimize an alkaloids pathway demonstrates how it could facilitate the rapid optimization of microbial strains for production of any chemical compound of interest.
