Prediction of whole-cell transcriptional response with machine learning

Mohammed Eslami, Amin Espah Borujeni, Hamed Eramian, Mark Weston, George Zheng, Joshua Urrutia, Carolyn Corbet, Diveena Becker, Paul Maschhoff, Katie Clowers, Alexander Cristofaro, Hamid Doost Hosseini, D. Benjamin Gordon, Yuval Dorfan, Jedediah Singer, Matthew Vaughn, Niall Gaffney, John Fonner, Joe Stubbs, Christopher A. VoigtEnoch Yeung

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish
Pages (from-to)404-409
Number of pages6
Issue number2
StatePublished - 15 Jan 2022
Externally publishedYes

Bibliographical note

Funding Information:
This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) and the Air Force Research Laboratory under Contract No. FA8750-17-C- 0231.

Publisher Copyright:
© 2021 The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail:


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