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Ribosomes translate genetic information encoded by messenger RNA (mRNA) into proteins. Both prokaryotic and eukaryotic cells have ribosomes. Cells that synthesize large quantities of protein—such as secretory cells in the human pancreas—can contain millions of ribosomes.
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Related Experiment Video

Updated: Apr 14, 2026

Refinement of OnePot PURE and Crude Ribosome Production for Reproducible Cell-free Protein Synthesis

Refinement of OnePot PURE and Crude Ribosome Production for Reproducible Cell-free Protein Synthesis

Published on: August 22, 2025

A Simple Protein Synthesis Model for the PURE System Operation.

Fabio Mavelli¹, Roberto Marangoni, Pasquale Stano

¹Chemistry Department, University of Bari, Via Orabona 4, Bari, Italy, fabio.mavelli@uniba.it.

Bulletin of Mathematical Biology

|April 26, 2015

Summary

We developed a simplified mathematical model for the PURE system, a cell-free transcription-translation (TX-TL) kit. This model accurately predicts protein synthesis dynamics and aids in understanding component variations in synthetic cell construction.

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Area of Science:

Synthetic Biology
Biophysics
Biochemistry

Background:

Encapsulating transcription-translation (TX-TL) machinery in lipid vesicles is crucial for synthetic cell development.
The PURE system, a well-characterized TX-TL kit with tunable components and modular architecture, is widely used in synthetic biology.
Understanding the dynamics of TX-TL systems is essential for designing and controlling synthetic cells.

Purpose of the Study:

To develop a simplified mathematical model for simulating the PURE system's TX-TL operations.
To quantitatively predict protein synthesis dynamics within the PURE system.
To analyze the impact of component concentrations on protein synthesis and energy consumption.

Main Methods:

Utilized Michaelis-Menten kinetics and differential equations to model protein synthesis.
Defined the model using 9 chemical species, 6 reactions, and 16 kinetic parameters.
Simulated the time course of messenger RNA and protein production.

Main Results:

The model accurately predicts the temporal dynamics of mRNA and protein production.
Quantitative predictions of PURE system behavior are enabled by the model.
The model allows for the determination of response coefficients for all TX-TL species.
Analysis of chemical energy consumption across the transcription, translation, and aminoacylation modules is facilitated.

Conclusions:

The developed mathematical model provides a valuable tool for understanding and predicting PURE system behavior in synthetic cell applications.
The model aids in comprehending how variations in PURE system component concentrations influence protein synthesis.
This work facilitates the optimization of synthetic cell designs by providing insights into TX-TL mechanisms and energy usage.