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Abstract：
Quantifying and predicting growth rate phenotype given variation in gene expression and environment is complicated by epistatic interactions and the vast combinatorial space of possible perturbations. We developed an approach for mapping expression-growth rate landscapes that integrates sparsely sampled experimental measurements with an interpretable machine learning model. We used mismatch CRISPRi across pairs and triples of genes to create over 8,000 titrated changes in E. coli gene expression under varied environmental contexts, exploring epistasis in up to 22 distinct environments. Our results show that a pairwise model previously used to describe drug interactions well-described these data. The model yielded interpretable parameters related to pathway architecture and generalized to predict the combined effect of up to four perturbations when trained solely on pairwise perturbation data. We anticipate this approach will be broadly applicable in optimizing bacterial growth conditions, generating pharmacogenomic models, and understanding the fundamental constraints on bacterial gene expression. A record of this paper\'s transparent peer review process is included in the supplemental information.
摘要：
由于上位性相互作用和可能扰动的巨大组合空间，定量和预测给定基因表达和环境变化的生长速率表型变得复杂。我们开发了一种映射表达增长率景观的方法，该方法将稀疏采样的实验测量与可解释的机器学习模型集成在一起。我们使用错配CRISPRi跨基因对和三元组，在不同的环境背景下，在大肠杆菌基因表达中创建了超过8,000个滴定的变化。在多达22个不同的环境中探索上位。我们的结果表明，先前用于描述药物相互作用的成对模型很好地描述了这些数据。该模型产生了与路径结构相关的可解释参数，并进行了推广，以预测仅在成对扰动数据上训练时多达四个扰动的综合影响。我们预计这种方法将广泛适用于优化细菌生长条件，生成药物基因组学模型，并了解细菌基因表达的基本限制。补充信息中包含了本文透明的同行评审过程的记录。
