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Updated: Aug 30, 2025

Rapid Synthesis and Screening of Chemically Activated Transcription Factors with GFP-based Reporters
Published on: November 26, 2013
Automated model-predictive design of synthetic promoters to control transcriptional profiles in bacteria
Travis L LaFleur1, Ayaan Hossain2, Howard M Salis3,4,5,6
1Department of Chemical Engineering, Pennsylvania State University, University Park, PA, 16801, USA.
Abstract:
Transcription rates are regulated by the interactions between RNA polymerase, sigma factor, and promoter DNA sequences in bacteria. However, it remains unclear how non-canonical sequence motifs collectively control transcription rates. Here, we combine massively parallel assays, biophysics, and machine learning to develop a 346-parameter model that predicts site-specific transcription initiation rates for any σ70 promoter sequence, validated across 22132 bacterial promoters with diverse sequences. We apply the model to predict genetic context effects, design σ70 promoters with desired transcription rates, and identify undesired promoters inside engineered genetic systems. The model provides a biophysical basis for understanding gene regulation in natural genetic systems and precise transcriptional control for engineering synthetic genetic systems.
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