Deciphering regulatory architectures from synthetic single-cell expression patterns.

Area of Science:

• Genomics
• Systems Biology
• Molecular Biology

Background:

• Gene regulation mechanisms remain largely unknown for most sequenced genes.
• Understanding gene regulation is crucial for quantitative analysis of physiological and evolutionary adaptation.
• Massively parallel reporter assays (MPRAs) are high-throughput methods for studying transcriptome sequence-phenotype relationships.

Purpose of the Study:

• To develop a "theory of the experiment" for MPRAs to improve data interpretation.
• To investigate the impact of biological and experimental parameters on MPRA outcomes.
• To enable the creation of accurate thermodynamic models of the transcriptome.

Main Methods:

• Generated synthetic single-cell gene expression data using equilibrium and out-of-equilibrium models.
• Modeled MPRA summary statistics, including information footprints and expression shift matrices.
• Utilized refined thermodynamic models with energy matrices to infer regulatory architecture.

Main Results:

• Simulations revealed significant effects of various parameters on MPRA data.
• Demonstrated the ability to optimize MPRA experimental designs for base-pair specificity.
• Showcased the utility of the approach for examining mutation-expression profile mappings.

Conclusions:

• The developed theoretical framework aids in optimizing MPRA design and data interpretation.
• This approach facilitates the generation of precise thermodynamic models for gene regulation.
• The methodology supports the exploration of regulatory evolution by analyzing mutation effects on expression.