ROC'n'Ribo: Characterizing a Riboswitching Expression System by Modeling Single-Cell Data.

Area of Science:

• Synthetic Biology
• Systems Biology
• Computational Biology

Background:

• RNA-engineered systems provide versatile control over gene expression.
• Riboswitches enable precise temporal and spatial gene regulation with low metabolic cost.
• Genetic circuits can be built using riboswitch-regulated devices for logical operations.

Purpose of the Study:

• To propose a hierarchical stochastic modeling approach for characterizing an in silico repressor gate.
• To calibrate the model using in vivo single-cell data, considering cell-to-cell variability.
• To assess the functionality of genetic logic gates at the single-cell level.

Main Methods:

• Developed a hierarchical stochastic modeling approach.
• Calibrated the model on transient in vivo single-cell data.
• Employed Receiver Operating Characteristic (ROC) analysis and derived a novel performance indicator for logic gates.

Main Results:

• Characterized an in silico repressor gate based on neomycin- and tetracycline-sensitive riboswitches.
• Accounted for cell-to-cell variability in gate performance analysis.
• Derived a novel performance indicator to assess single-cell logic gate functionality.

Conclusions:

• The developed model and performance indicator are crucial for evaluating genetic logic gates.
• This approach ensures correct functionality at the single-cell level, essential for genetic circuitry.
• Results can be applied to analyze and ensure the performance of other genetic devices amidst cell-to-cell variability.