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

Area of Science:

• Molecular Biology
• Systems Biology
• Genetics

Background:

• Quantitative gene expression is often modeled using thermodynamics-driven mathematical approaches.
• Incomplete or incorrect regulatory relationships can arise from unsystematic parameter space searches in sequence-to-expression models.

Purpose of the Study:

• To demonstrate how systematic parameter space searches enhance understanding of gene regulatory mechanisms.
• To resolve ambiguities and generate testable hypotheses regarding enhancer function.
• To investigate the regulation of the Drosophila gene 'ind'.

Main Methods:

• Generating an ensemble of mathematical models for the 'ind' gene enhancer.
• Systematically searching the parameter space of sequence-to-expression models.
• Restricting models to biologically plausible hypotheses using in vivo perturbation data.
• Validating model predictions through site mutagenesis in transgenic Drosophila embryos.

Main Results:

• A systematic search yielded an ensemble of technically acceptable models for 'ind' gene expression.
• Some models represented mechanistically distinct regulatory hypotheses.
• Biologically plausible models made specific predictions about enhancer sequence effects.
• In vivo validation confirmed predictions derived from site-directed mutagenesis.

Conclusions:

• Systematic exploration of model parameter space provides a comprehensive view of gene regulatory mechanisms.
• This approach can resolve ambiguities in sequence-to-expression relationships.
• Mathematical modeling combined with experimental validation is crucial for understanding gene regulation.