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Updated: Sep 21, 2025

Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER
Published on: May 19, 2019
gcFront: a tool for determining a Pareto front of growth-coupled cell factory designs
Laurence Legon1,2, Christophe Corre2, Declan G Bates1
1Warwick Integrative Synthetic Biology Centre, School of Engineering, University of Warwick, Coventry CV4 7AL, UK.
Motivation:
A widely applicable strategy to create cell factories is to knockout (KO) genes or reactions to redirect cell metabolism so that chemical synthesis is made obligatory when the cell grows at its maximum rate. Synthesis is thus growth-coupled, and the stronger the coupling the more deleterious any impediments in synthesis are to cell growth, making high producer phenotypes evolutionarily robust. Additionally, we desire that these strains grow and synthesize at high rates. Genome-scale metabolic models can be used to explore and identify KOs that growth-couple synthesis, but these are rare in an immense design space, making the search difficult and slow.
Results:
To address this multi-objective optimization problem, we developed a software tool named gcFront-using a genetic algorithm it explores KOs that maximize cell growth, product synthesis and coupling strength. Moreover, our measure of coupling strength facilitates the search so that gcFront not only finds a growth-coupled design in minutes but also outputs many alternative Pareto optimal designs from a single run-granting users flexibility in selecting designs to take to the lab.
Availability And Implementation:
gcFront, with documentation and a workable tutorial, is freely available at GitHub: https://github.com/lLegon/gcFront and archived at Zenodo, DOI: 10.5281/zenodo.5557755.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
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