An important application of Synthetic Biology is the engineering of the host cell system to yield useful products. However, an increase in the scale of the host system leads to huge design space and requires a large number of validation trials with high experimental costs. A comprehensible machine learning approach that efficiently explores the hypothesis space and guides experimental design is urgently needed for the Design-Build-Test-Learn (DBTL) cycle of the host cell system. We introduce a novel machine learning framework ILP-iML1515 based on Inductive Logic Programming (ILP) that performs abductive logical reasoning and actively learns from training examples. In contrast to numerical models, ILP-iML1515 is built on comprehensible logical representations of a genome-scale metabolic model and can update the model by learning new logical structures from auxotrophic mutant trials. The ILP-iML1515 framework 1) allows high-throughput simulations and 2) actively selects experiments that reduce the experimental cost of learning gene functions in comparison to randomly selected experiments.

翻译：合成生物学的一个重要应用是工程化宿主细胞系统以生产有用产物。然而，宿主系统规模的扩大导致设计空间巨大，且需要大量高实验成本的验证试验。在宿主细胞系统的设计-构建-测试-学习（DBTL）循环中，亟需一种能高效探索假设空间并指导实验设计的可理解机器学习方法。我们提出了一种基于归纳逻辑编程（ILP）的新型机器学习框架ILP-iML1515，该框架能够进行溯因逻辑推理并从训练样本中主动学习。与数值模型不同，ILP-iML1515建立在基因组规模代谢模型的可理解逻辑表示之上，能够通过从营养缺陷型突变体试验中学习新的逻辑结构来更新模型。ILP-iML1515框架能够：1）实现高通量模拟；2）与随机选择的实验相比，主动选择可降低基因功能学习实验成本的实验。