Thermodynamic Modeling of Gene Expression from Sequence Data

Thermodynamic Modeling of Gene Expression from Sequence Data

To understand the relationship between an enhancer DNA sequence and quantitative gene expression, thermodynamics-driven mathematical models of transcription are often employed.

These “sequence-to-expression” models can describe an incomplete or even incorrect set of regulatory relationships if the parameter space is not searched systematically.

Investigators focus on an enhancer of the Drosophila gene ind and demonstrate how a systematic search of parameter space can reveal a more comprehensive picture of a gene’s regulatory mechanisms, resolve outstanding ambiguities, and suggest testable hypotheses.

Authors describe an approach that generates an ensemble of ind models; all of these models are technically acceptable solutions to the sequence-to-expression problem in light of wild-type data, and some represent mechanistically distinct hypotheses about the regulation of ind.

This ensemble can be restricted to biologically plausible models using requirements gleaned from in vivo perturbation experiments.

Biologically plausible models make unique predictions about how specific ind enhancer sequences affect ind expression; authors validate these predictions in vivo through site mutagenesis in transgenic Drosophila embryos.