Accurate prediction of gene expression by integration of DNA sequence statistics with detailed modeling of transcription regulation

TL;DR

This paper presents an integrated method combining DNA sequence statistics with biophysical modeling to accurately predict gene expression levels across various conditions, exemplified on the lac operon.

Contribution

It introduces a middle-ground approach that bridges empirical and biophysical models for gene regulation prediction, enhancing accuracy and understanding.

Findings

Predicts lac operon activity within 0.3-fold accuracy

Achieves reliable gene expression predictions over 10,000-fold range

Integrates statistical sequence data with detailed transcription regulation models

Abstract

Gene regulation involves a hierarchy of events that extend from specific protein-DNA interactions to the combinatorial assembly of nucleoprotein complexes. The effects of DNA sequence on these processes have typically been studied based either on its quantitative connection with single-domain binding free energies or on empirical rules that combine different DNA motifs to predict gene expression trends on a genomic scale. The middle-point approach that quantitatively bridges these two extremes, however, remains largely unexplored. Here, we provide an integrated approach to accurately predict gene expression from statistical sequence information in combination with detailed biophysical modeling of transcription regulation by multidomain binding on multiple DNA sites. For the regulation of the prototypical lac operon, this approach predicts within 0.3-fold accuracy transcriptional activity over a 10,000-fold range from DNA sequence statistics for different intracellular conditions.