Synthetic biology enables the reprogramming of cellular functions for various applications. However, challenges in scalability and predictability persist due to context-dependent performance and complex circuit-host interactions. This study introduces an iModulon-based engineering approach, utilizing machine learning-defined co-regulated gene groups (iModulons) as design parts containing essential genes for specific functions. This approach identifies the necessary components for genetic circuits across different contexts, enhancing genome engineering by improving target selection and predicting module behavior. We demonstrate several distinct uses of iModulons: (i) discovery of unknown iModulons to increase protein productivity, heat tolerance and fructose utilization; (ii) an iModulon boosting approach, which amplifies the activity of specific iModulons, improved cell growth under osmotic stress with minimal host regulation disruption; (iii) an iModulon rebalancing strategy, which adjusts the activity levels of iModulons to balance cellular functions, significantly increased oxidative stress tolerance while minimizing trade-offs and (iv) iModulon-based gene annotation enabled natural competence activation by predictably rewiring iModulons. Comparative experiments with traditional methods showed our approach offers advantages in efficiency and predictability of strain engineering. This study demonstrates the potential of iModulon-based strategies to systematically and predictably reprogram cellular functions, offering refined and adaptable control over complex regulatory networks.

中文翻译：

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利用转录调控模块知识库调节细菌功能


合成生物学能够为各种应用重新编程细胞功能。然而，由于上下文相关的性能和复杂的电路-主机交互，可扩展性和可预测性方面的挑战仍然存在。本研究引入了一种基于 iModulon 的工程方法，利用机器学习定义的共调控基因组 （iModulons） 作为包含特定功能必需基因的设计部分。这种方法确定了不同环境中遗传回路的必要组成部分，通过改进靶标选择和预测模块行为来增强基因组工程。我们展示了 iModulons 的几种不同用途：（i） 发现未知的 iModulon 以提高蛋白质生产率、耐热性和果糖利用率;（ii） iModulon 增强方法，可放大特定 iModulon 的活性，在渗透应激下改善细胞生长，同时将宿主调节干扰降至最低;（iii） iModulon 再平衡策略，调整 iModulons 的活性水平以平衡细胞功能，显著提高氧化应激耐受性，同时最大限度地减少权衡和 （iv） 基于 iModulon 的基因注释通过可预测的重新布线 iModulons 实现了自然能力激活。与传统方法的比较实验表明，我们的方法在应变工程的效率和可预测性方面具有优势。这项研究证明了基于 iModulon 的策略在系统且可预测地重编程细胞功能方面的潜力，为复杂的调节网络提供精细且适应性强的控制。