A Systematic Ensemble Approach to Thermodynamic Modeling of Gene Expression from Sequence Data

Abstract

To understand the relationship between 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 parameter space is not searched systematically. Here, we 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. We describe an approach that generates an ensemble of ind models; all are technically acceptable solutions to the sequence-to-expression problem in light of wild-type data; 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 effect ind expression; we validate these predictions in vivo through experiments using transgenic Drosophila embryos.

Complex cis-regulatory landscape of the insulin receptor gene underlies the broad expression of a central signaling regulator

“…In fact, even dynamic chromatin features may reflect off-target effects of transcription factors, rather than functional interactions (Kok et al, 2015). Although likely to be incomplete, our identification of the cisregulatory circuitry of InR represents a first step in enabling the construction of computational models, which can be tested in physiological settings to understand the impact of regulatory sequence variation and signaling (Samee et al, 2015;Sayal et al, 2016).…”

Gene regulation during Drosophila eggshell patterning

“…These models can predict how the spatiotemporal activity of the brL enhancer responds to changes in the inductive signals and changes within the transcriptional network, providing a compact summary for a large number of genetic perturbation experiments. At this point, models of eggshell patterning do not directly use the sequence-specific information, but these capabilities can be added as we acquire more knowledge about the connections between transcription factors and the brL sequence (54,55). In the future, we envision a unified model that accounts for multiple processes, from inductive signals to enhancers, and can generate all of the observed eggshell morphologies by variations of model parameters and sequence variations.…”

Quantitative Measurement and Thermodynamic Modeling of Fused Enhancers Support a Two-Tiered Mechanism for Interpreting Regulatory DNA

“…Of note, we had excluded all eve enhancers from this dataset. For this dataset, we first randomly sampled ~1 million points (each denoting a different parameterization of the model) from the parameter space (Samee et al, 2015). Then we considered each of the top 1000 models from the sampled collection, one at a time, as the initial parameterization of GEMSTAT and re-estimated parameters to optimize the model for the ~30 enhancers.…”