Motivation Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we present the host response model (HRM), a machine learning approach that maps response of single perturbations to transcriptional response of the combination of perturbations. Results The HRM combines high-throughput sequencing with machine learning to infer links between experimental context, prior knowledge of cell regulatory networks, and RNASeq data to predict a gene’s dysregulation. We find that the HRM can predict the directionality of dysregulation to a combination of inducers with an accuracy of >90% using data from single inducers. We further find that the use of prior, known cell regulatory networks doubles the predictive performance of the HRM (an R2 from 0.3 to 0.65). The model was validated in two organisms, Escherichia coli and Bacillus subtilis, using new experiments conducted after training. Finally, while the HRM is trained with gene expression data, the direct prediction of differential expression makes it possible to also conduct enrichment analyses using its predictions. We show that the HRM can accurately classify >95% of the pathway regulations. The HRM reduces the number of RNASeq experiments needed as responses can be tested in silico prior to the experiment. Availability and implementation The HRM software and tutorial are available at https://github.com/sd2e/CDM and the configurable differential expression analysis tools and tutorials are available at https://github.com/SD2E/omics_tools. Supplementary information Supplementary data are available at Bioinformatics online.

中文翻译：

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用机器学习预测全细胞转录反应

合成和系统生物学中的动机应用可以受益于测量全细胞对生化扰动的反应。执行涵盖所有可能的扰动组合的实验是不可行的。在本文中，我们介绍了宿主响应模型 (HRM)，这是一种机器学习方法，可将单个扰动的响应映射到扰动组合的转录响应。结果 HRM 将高通量测序与机器学习相结合，以推断实验背景、细胞调控网络的先验知识和 RNASeq 数据之间的联系，以预测基因的失调。我们发现 HRM 可以使用来自单个诱导剂的数据预测诱导剂组合失调的方向性，准确度＞90%。我们进一步发现使用先验，已知的细胞调节网络使 HRM 的预测性能加倍（R2 从 0.3 到 0.65）。使用训练后进行的新实验，该模型在两种生物体（大肠杆菌和枯草芽孢杆菌）中得到验证。最后，虽然 HRM 使用基因表达数据进行训练，但差异表达的直接预测使得使用其预测进行富集分析成为可能。我们表明，HRM 可以准确分类＞95% 的途径调节。HRM 减少了所需的 RNASeq 实验次数，因为响应可以在实验前通过计算机进行测试。可用性和实施​​ HRM 软件和教程可在 https://github.com/sd2e/CDM 获得，可配置的差异表达分析工具和教程可在 https://github.com/SD2E/omics_tools 获得。