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Synthetic Biology

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Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
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Related Experiment Video

Updated: May 31, 2026

A Multilayer Microfluidic Platform for the Conduction of Prolonged Cell-Free Gene Expression

A Multilayer Microfluidic Platform for the Conduction of Prolonged Cell-Free Gene Expression

Published on: October 6, 2019

Fully synthetic replication of complex real biological cell clusters using a novel cluster-based 'Rosetta-Routine'

Bradley Mason¹, Laura Justham¹, Liam Whitby²

¹Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, United Kingdom.

Plos Computational Biology

|May 29, 2026

Summary

This study introduces synthetic flow cytometry (FC) cell clusters to overcome validation challenges in FC data analysis. This approach provides reliable, traceable datasets for enhanced diagnostic accuracy and improved patient care.

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Area of Science:

Biotechnology
Computational Biology
Medical Diagnostics

Background:

Flow cytometry (FC) is crucial for cell analysis in diagnostics and therapeutics.
Validating FC data accuracy is challenging due to inherent data relativity and lack of ground truth.
Current methods struggle with reliable metrological and biological accuracy assessment of FC data.

Purpose of the Study:

To generate realistic synthetic flow cytometry cell clusters as substitutes for traditional FC data.
To demonstrate the suitability of synthetic data for validating FC data analysis methods.
To address the need for a controlled and reproducible validation framework in FC applications.

Main Methods:

Developed optimized synthetic cluster-generating benchmarking software.
Implemented the 'Rosetta-Routine' codebase to translate real data statistical properties into synthetic replicate coefficients.
Generated distributionally-equivalent replicate datasets with known cluster membership for each data point.

Main Results:

Successfully simulated realistic monocyte clusters using synthetic data.
Synthetic datasets accurately represent the statistical characteristics of real-world FC data.
The approach enables robust validation of clustering methods applied to FC data.

Conclusions:

Synthetic FC data offers a reliable alternative for validating FC analysis, reducing uncertainty.
This method enhances confidence in FC applications, including diagnostics and training.
Improved FC analysis confidence through synthetic data will lead to better clinical decisions and patient care.