Mathematical Modeling of RNA-Based Architectures for Closed Loop Control of Gene Expression

Area of Science:

• Synthetic Biology
• Biomolecular Engineering
• Control Theory

Background:

• Biological systems utilize feedback for precise gene expression control.
• Synthetic biologists engineer feedback loops to improve gene expression dynamics, robustness, and predictability.
• Current experimental biomolecular control systems lag behind electrical/mechanical counterparts in performance.

Purpose of the Study:

• To present mathematical models for biomolecular controllers enabling reference tracking, disturbance rejection, and temporal response tuning of gene expression.
• To introduce closed-loop control using RNA transcriptional regulators and molecular sequestration for feedback.
• To identify key parameters influencing gene expression response and determine biologically plausible values for perfect reference tracking.

Main Methods:

• Development of mathematical models for biomolecular controllers.
• Implementation of RNA transcriptional regulators for closed-loop feedback via molecular sequestration.
• Sensitivity analysis to identify influential parameters for transient and steady-state responses.
• Quantification of performance using control theory metrics.

Main Results:

• Models enable reference tracking, disturbance rejection, and temporal response tuning in gene expression.
• Sensitivity analysis identified critical parameters for precise control.
• Biologically plausible parameter values were found to enable perfect reference tracking.
• RNA regulators are suitable for robust and precise gene expression feedback controllers.

Conclusions:

• Mathematical models and RNA regulators offer a pathway to high-performance biomolecular feedback control.
• The developed controllers enhance the robustness and predictability of gene expression.
• The study provides quantitative methods for assessing biomolecular feedback control system performance.
• This approach advances the field of synthetic biology by improving control over gene expression dynamics.