Volume 2,Issue 9
科研问题驱动的合成生物学项目式教学创新与实践
针对合成生物学课程中知识模块割裂、工程闭环思维薄弱、真实科研问题驱动不足等教学痛点，本文提出了科研问题驱动的项目式教学创新模式。该模式以“设计-构建-测试-学习”（DBTL）循环为教学主线，以“谷氨酸棒杆菌生产紫色杆菌素”这一真实科研问题为载体，将代谢网络分析、基因回路设计、菌株构建与表型测试等环节整合为贯穿课程始终的工程迭代流程。教学实践表明，该模式有效促进了学生将分散知识整合为系统化工程设计方案的能力，显著强化了DBTL工程闭环思维，提升了学生在代谢通路分析、策略设计和综合表达等方面的综合素养。紫色杆菌素的天然呈色特性为“测试”环节提供了直观的可视化表型标记，增强了教学过程的参与感与成果获得感。本改革为合成生物学及相关课程从知识传授向能力培养转型提供了可借鉴的实践路径。
[1] 丁明珠, 等., 合成生物学重要研究方向进展[J].合成生物学, 2020. 1 (1): 7-28.
[2]Smanski, M.J., et al., Synthetic biology to access and expand nature’s chemical diversity. Nature Reviews Microbiology, 2016. 14 ((3)).
[3] 赵国屏, 合成生物学：从“造物致用”到产业转化[J].生物工程学报, 2022. 38 (11): 4001-4011.
[4]Nielsen, J. and J.D. Keasling, Engineering Cellular Metabolism. Cell, 2016. 164 ((6)): 1185-1197.
[5] 王凯峰、纪晓俊, 科研反哺教学驱动的“代谢工程”课程教学改革探索[J].生物工程学报, 2026. 42 (3): 1394-1403.
[6] Freeman, S., et al., Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences of the United States of America, 2014. 111 ((23)): 8410-8415.
[7]García Martín, H., S. Mazurenko, and H. Zhao, Special Issue on Artificial Intelligence for Synthetic Biology. ACS Synthetic Biology, 2024. 13 (2): 408-410.
[8]Keasling, J.D., Manufacturing molecules through metabolic engineering. Science, 2010. 330 ((6009)): 1355-1358.
[9]Zhou, H., et al., One-pot production of colored bacterial cellulose. Trends in Biotechnology, 2025.
[10]Yao, J., et al., Developing a highly efficient hydroxytyrosol whole-cell catalyst by de-bottlenecking rate-limiting steps. Nature Communications, 2020. 11 (1).
[11]Zheng, C., et al., Spatial enzyme assembly for microbial pharmaceutical synthesis. Current Opinion in Biotechnology, 2026. 99: 103475.
[12]Dong, Y. and Z. Chen, Systems metabolic engineering of Corynebacterium glutamicum for efficient l-tryptophan production. Synthetic and Systems Biotechnology, 2025.10 (2): 511-522.
[13]Zhu, H. and Y. Li, Biomanufacturing driven by engineered organisms. Sheng Wu Gong Cheng Xue Bao, 2025. 41 (1): 1-78.
[14]Carbonell, P., T. Radivojevic, and H. Garcia Martin, Opportunities at the Intersection of Synthetic Biology, Machine Learning, and Automation. ACS Synthetic Biology,2019. 8 (7): 1474-1477.