Molecular Engineering & Sciences Institute and Center for Synthetic Biology, University of Washington, Seattle, Washington 98195, United States.
Department of Chemistry, University of Washington, Seattle, Washington 98195, United States.
ACS Synth Biol. 2024 Sep 20;13(9):2643-2652. doi: 10.1021/acssynbio.4c00053. Epub 2024 Aug 9.
The CRISPR-Cas system has enabled the development of sophisticated, multigene metabolic engineering programs through the use of guide RNA-directed activation or repression of target genes. To optimize biosynthetic pathways in microbial systems, we need improved models to inform design and implementation of transcriptional programs. Recent progress has resulted in new modeling approaches for identifying gene targets and predicting the efficacy of guide RNA targeting. Genome-scale and flux balance models have successfully been applied to identify targets for improving biosynthetic production yields using combinatorial CRISPR-interference (CRISPRi) programs. The advent of new approaches for tunable and dynamic CRISPR activation (CRISPRa) promises to further advance these engineering capabilities. Once appropriate targets are identified, guide RNA prediction models can lead to increased efficacy in gene targeting. Developing improved models and incorporating approaches from machine learning may be able to overcome current limitations and greatly expand the capabilities of CRISPR-Cas9 tools for metabolic engineering.
CRISPR-Cas 系统通过使用向导 RNA 定向激活或抑制靶基因，实现了复杂的多基因代谢工程程序。为了优化微生物系统中的生物合成途径，我们需要改进模型来为转录程序的设计和实施提供信息。最近的进展产生了新的建模方法，用于确定基因靶标并预测向导 RNA 靶向的效果。基因组规模和通量平衡模型已成功应用于通过组合 CRISPR 干扰 (CRISPRi) 程序识别用于提高生物合成产量的目标。用于可调谐和动态 CRISPR 激活 (CRISPRa) 的新方法的出现有望进一步推进这些工程能力。一旦确定了适当的靶标，向导 RNA 预测模型就可以提高基因靶向的效果。开发改进的模型并结合机器学习方法，可能能够克服当前的限制，并极大地扩展 CRISPR-Cas9 工具在代谢工程中的应用能力。
