An automated model test system for systematic development and improvement of gene expression models

Alexander C. Reis, Howard M. Salis

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Gene expression models greatly accelerate the engineering of synthetic metabolic pathways and genetic circuits by predicting sequence-function relationships and reducing trial- and-error experimentation. However, developing models with more accurate predictions remains a significant challenge. Here we present a model test system that combines advanced statistics, machine learning, and a database of 9862 characterized genetic systems to automatically quantify model accuracies, accept or reject mechanistic hypotheses, and identify areas for model improvement. We also introduce model capacity, a new information theoretic metric for correct cross-data-set comparisons. We demonstrate the model test system by comparing six models of translation initiation rate, evaluating 100 mechanistic hypotheses, and uncovering new sequence determinants that control protein expression levels. We then applied these results to develop a biophysical model of translation initiation rate with significant improvements in accuracy. Automated model test systems will dramatically accelerate the development of gene expression models, and thereby transition synthetic biology into a mature engineering discipline.

Original languageEnglish (US)
Pages (from-to)3145-3156
Number of pages12
JournalACS Synthetic Biology
Issue number11
StatePublished - Nov 20 2020

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)


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