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产生有用产品。然而，宿主系统规模的扩大导致巨大的设计空间，并需要大量实验成本高昂的验证试验。在宿主细胞系统的"设计-构建-测试-学习"（DBTL）循环中，迫切需要一种能够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。我们提出了一个基于归纳逻辑编程（ILP）的新型机器学习框架ILP-iML1515，该框架可进行溯因逻辑推理，并从训练样本中主动学习。与数值模型不同，ILP-iML1515基于基因组规模代谢模型的可理解逻辑表示构建，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，主动选择可降低学习基因功能实验成本的实验。