gcFront: a tool for determining a Pareto front of growth-coupled cell factory designs

Abstract

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.

Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling

“…Although growth coupled overproducing strategies are challenging due the low metabolic robustness of cyanobacteria ( Nogales et al, 2013 ; Gudmundsson and Nogales, 2015 ), which could result in unfeasible genetic designs under the current scenario, we cannot rule out the possibility that the lack of success using GDLS and OptKnock is due to an insufficiently scrutinized metabolic space. To address a more systematic search, we performed a new strain designing analysis by using gcFront ( Legon et al, 2022 ). gcFront is an algorithm that explores knockout strategies maximizing not only cell growth and product synthesis, but also the strength of production-to-growth coupling using a tri-level optimization.…”

“…In addition, the recently developed gcFront algorithm was also used to identify knockouts that growth-couple synthesis ( Legon et al, 2022 ). gcFront uses a multiobjective genetic algorithm that identifies a Pareto front of designs that maximize growth rate, product synthesis and coupling strength and finds combinations of gene/reaction knockouts that will enforce growth coupling ( Legon et al, 2022 ). Before applying this algorithm, GEMs need to be pre-processed to reduce the search space of reaction by removing the biomass reactions not assigned as objective and blocked reactions.…”

Design principles for engineering bacteria to maximise chemical production from batch cultures

“…Mathematically, this problem is defined in Equation (4) (Methods 4.3), with the scaling coe cients to the transcription rate of the respective enzyme's gene denoted sTX E , sTX Ep , sTX Tp . Growth ( ) and synthesis (r T p ) rates are calculated from simulations of the host-aware model of the single cell described in Supplementary Note SN1, i.e., the model ignoring dynamics of the batch culture described in Equation (1). We found a Pareto front of optimal designs exhibiting a trade-o↵ between growth rate and synthesis rate (green crosses Figure 1b).…”

“…Typically, engineered strains are selected based on their growth and/or synthesis rate. For one-stage bioprocesses, expression of some host enzymes is knocked out to couple and re-balance growth and synthesis [1, 2], while for two-stage bioprocesses genetic circuits can be engineered into the host cell that activate maximum product synthesis upon induction after a growth phase [3, 4, 5, 6, 7].…”