Prediction of whole-cell transcriptional response with machine learning

Abstract

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, E. coli and B. subtilis, using new experiments conducted post training. Finally, while the HRM is trained on 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 to focus experiments. Availability 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.

Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop

“…The design and build aspects of DART have not been previously used in combination. The design/prediction tools are Dynamic Signatures Generated by Regulatory Networks (DSGRN, (20; 29; 30)) and Combinatorial Design Model (CDM (31; 32)). The build tools are DNA Assembly (DASi, (33; 34; 35)), the computer-aided process planning tool Terrarium (36; 35), and the lab software Aquarium (14; 37).…”

Collaborative methods to enhance reproducibility and accelerate discovery

“…Using machine learning, the experimental conditions, and a vectorized representation of a gene's role in the network that represents the organism, SD2 performers were able to achieve greater than 90% accuracy in predicting whether the gene would be dysregulated, and an R 2 ∼ 0.6 in quantifying the level of the gene's dysregulation. 53 In the materials chemistry thrust, work focused on accelerating the Edisonian trial-and-error process by using data at scale. A variety of experiment planning algorithms were tested for their ability to support interpolation of results, 54 extrapolation to new chemical systems, 36 combination of model predictions to identify anomalies, 55 as well as active-learning 54 and active-meta learning approaches 56 for crystal growth control.…”

“…Using machine learning, the experimental conditions, and a vectorized representation of a gene's role in the network that represents the organism, SD2 performers were able to achieve greater than 90% accuracy in predicting whether the gene would be dysregulated, and an R 2 ∼ 0.6 in quantifying the level of the gene's dysregulation. 53…”

Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop

“…The design and build aspects of DART have not been previously used in combination. The design/prediction tools are Dynamic Signatures Generated by Regulatory Networks (DSGRN, ( 20 , 29 , 30 )) and Combinatorial Design Model (CDM, ( 31 ; 32 )). The build tools are DNA Assembly (DASi, ( 33–35 )), the computer-aided process planning tool Terrarium ( 36 ; 35 ) and the lab software Aquarium ( 14 ; 37 ).…”