Machine learning for synthetic biology: Methods and applications

Automated high-throughput DNA synthesis and assembly

“…The core of EGF's capabilities resides in a highly automated robotic platform, distinguished as the most extensive integrated synthetic biology facility established within the academic realm. Notably, this platform is underpinned by the deployment of three autonomous robotic arms [ 89 , 90 ]. The EGF has developed and shared more than 20 industrial-grade software packages ( https://cuba.genomefoundry.org/ ) for the design and construction of large DNA fragments and combinatorial DNA libraries, including sculpt a sequence, cloning simulation, design Golden Gate overhangs, transfer genbank features and so on.…”

“… >42 per day >90% [ [99] , [101] ] Edinburgh Genome Foundry Golden Gate, Gibson Assembly large DNA fragments (>5 kb). 2000 per week 40–90% [ [89] , [90] , [91] , [92] ] London BioFoundry BASIC Automated design, build and verification of large DNA fragments using robotic equipment. >96 per day 90–99% [ [94] , [99] ] Shenzhen Institute of Synthetic Biology Three platforms, “synthetic test” platform, “user detector” platform, “design learning” platform.…”

Quantifying to simplicity, engineering to complexity: Quantitative engineering biology

“…黑箱模型研究的核心在于高通量地获取标准化实验数据, 并辅 以机器学习等人工智能手段, 分析获得笼统的因果关系. 本专辑中, 乔宇团队 [11] 介绍了机器学习领域广泛应用的几个模型及方法, 及其 在启动子预测、酶催化设计、代谢途径构建、基因线路设计等合成 生物学方面的应用; 司同团队 [12] 介绍了自动化设施对于整个"设计-合成-测试-学习"闭环的高通量试错的支撑, 以及自动化设施的未来 发展方向. 另一方面, 凭借目前我们对生物系统的理解, 已可以在某些方 面做到部分的可预测性设计.…”