Motivation A widely applicable strategy to create cell factories is to knock out (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 synthesise 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 maximise 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 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 (Legon et al., 2022). Supplementary information Supplementary data are available at Bioinformatics online.

Motivation: A widely applicable strategy for developing evolutionarily robust cell factories is to knock out (KO) genes or reactions to couple chemical synthesis with cell growth. Genome-scale metabolic models enable their rational design, but KOs that provide growth-coupling (gc) are rare in the immense design space, making searching difficult and slow, and though several measures determine the utility of those strains, few drive the search. Results: To address these issues we developed a software tool named gcFront - using a genetic algorithm it explores KOs that maximise key performance objectives: cell growth, product synthesis, and coupling strength. Our measure of coupling strength facilitates the search, so gcFront not only finds a gc-design in minutes but also outputs many alternative Pareto optimal gc-designs from a single run - granting users freedom to select designs to take to the lab.