An Automated Model Test System for Systematic Development and Improvement of Gene Expression Models

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.