Department of Automation, Tsinghua University, Beijing, China.
PLoS One. 2022 Apr 11;17(4):e0266783. doi: 10.1371/journal.pone.0266783. eCollection 2022.
Metabolic pathway design is an essential step in the course of constructing an efficient microbial cell factory to produce high value-added chemicals. Meanwhile, the computational design of biologically meaningful metabolic pathways has been attracting much attention to produce natural and non-natural products. However, there has been a lack of effective methods to perform metabolic network reduction automatically. In addition, comprehensive evaluation indexes for metabolic pathway are still relatively scarce. Here, we define a novel uniform similarity to calculate the main substrate-product pairs of known biochemical reactions, and develop further an efficient metabolic pathway design tool named PyMiner. As a result, the redundant information of general metabolic network (GMN) is eliminated, and the number of substrate-product pairs is shown to decrease by 81.62% on average. Considering that the nodes in the extracted metabolic network (EMN) constructed in this work is large in scale but imbalanced in distribution, we establish a conditional search strategy (CSS) that cuts search time in 90.6% cases. Compared with state-of-the-art methods, PyMiner shows obvious advantages and demonstrates equivalent or better performance on 95% cases of experimentally verified pathways. Consequently, PyMiner is a practical and effective tool for metabolic pathway design.
代谢途径设计是构建高效微生物细胞工厂生产高附加值化学品的过程中的一个重要步骤。同时，生物有意义的代谢途径的计算设计也吸引了人们的关注，以生产天然和非天然产物。然而，目前缺乏自动执行代谢网络简化的有效方法。此外，代谢途径的综合评价指标仍然相对缺乏。在这里，我们定义了一种新的统一相似度来计算已知生化反应的主要底物-产物对，并进一步开发了一种名为 PyMiner 的高效代谢途径设计工具。结果表明，通用代谢网络（GMN）的冗余信息被消除，底物-产物对的数量平均减少了 81.62%。考虑到本工作中构建的提取代谢网络（EMN）的节点规模较大但分布不平衡，我们建立了一种条件搜索策略（CSS），在 90.6%的情况下减少了搜索时间。与最先进的方法相比，PyMiner 在 95%的实验验证途径案例中表现出明显的优势，并且性能相当或更好。因此，PyMiner 是一种实用且有效的代谢途径设计工具。
