Prediction of Whole-Cell Transcriptional Response with Machine Learning

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

doi:10.1093/bioinformatics/btab676

BioinformaticsBioinformaticsSep 22, 2021

Peer-reviewed journal article

Prediction of Whole-Cell Transcriptional Response with Machine Learning

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

Abstract

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 takes the cell response to single perturbations as the input and predicts the whole cell transcriptional response to the combination of inducers. We find that the HRM is able to qualitatively 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 known prior, known cell regulatory networks doubles the predictive performance of the HRM (an R2 from 0.3 to 0.65). This tool will significantly reduce the number of high-throughput sequencing experiments that need to be run to characterize the transcriptional impact of the combination of perturbations on the host.

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