Nucleotide-Level Chemical Reaction Network Modeling Enables Quantitative Prediction of Reconstituted Cell-Free...

Area of Science:

• Synthetic Biology
• Biochemistry
• Computational Biology

Background:

• Cell-free expression systems enable rapid DNA circuit prototyping and protein synthesis.
• PURE systems offer defined components for predictable protein production.
• Limited experimental data exist for PURE-based protein expression modeling.

Purpose of the Study:

• To generalize the PURE translation model for diverse protein compositions and lengths.
• To develop a chemical reaction network (CRN) for PURE transcription.
• To couple transcription and translation models for a comprehensive PURE protein synthesis model.

Main Methods:

• Generalized PURE detailed translation model development.
• CRN construction for PURE transcription.
• Validation of transcription models using malachite-green aptamer (MGapt) RNA production.
• Coupling of transcription and generalized translation models.
• Mass-action reaction-based PURE protein synthesis modeling.

Main Results:

• A generalized PURE translation model was developed.
• A CRN for PURE transcription was successfully built and validated.
• The coupled model accurately captured the kinetics of MGapt and deGFP expression from plasmids.
• The model demonstrated predictive power across various DNA concentrations.

Conclusions:

• The developed PURE model provides a robust framework for computational prediction of protein synthesis.
• This work bridges the gap between computational modeling and experimental validation in PURE systems.
• The generalized model enhances the utility of PURE for synthetic biology applications.