Deciphering regulatory architectures from synthetic single-cell expression patterns.

Area of Science:

• Genomics
• Systems Biology
• Computational Biology

Background:

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

Purpose of the Study:

• To develop a theoretical framework for interpreting MPRA data.
• To understand the impact of biological and experimental parameters on MPRA results.
• To optimize MPRA experimental design for accurate gene regulation modeling.

Main Methods:

• Generation of synthetic single-cell gene expression data using equilibrium and out-of-equilibrium models.
• Imitation of MPRA summary statistics (information footprints, expression shift matrices).
• Inference of regulatory architecture using refined thermodynamic models with energy matrices.

Main Results:

• Simulations revealed significant effects of various parameters on MPRA data.
• Demonstrated optimization of MPRA experimental designs for base-pair specific thermodynamic models.
• Enabled detailed examination of mutation-expression profile mappings.

Conclusions:

• The developed theory enhances MPRA versatility and scalability.
• This approach facilitates the creation of accurate thermodynamic models of the transcriptome.
• The methodology aids in designing better MPRAs and exploring regulatory evolution.