Dataset Information

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.

SUBMITTER: Legon L

PROVIDER: S-EPMC9272801 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Our research aims to help industrial biotechnology develop a sustainable economy using green technology based on microorganisms and synthetic biology through two case studies that improve metabolic capacity in yeast models Yarrowia lipolytica (Y. lipolytica) and Saccharomyces cerevisiae (S. cerevisiae). We aim to increase the production capacity of beta-carotene (β-carotene) and succinic acid, which are among the highest market demands due to their versatile use in numerous consumer products. We performed simulations to identify in silico ranking of strains based on multiple objectives: the growth rate of yeast microorganisms, the number of used chromosomes, and the production capability of β-carotene (for Y. lipolytica) and succinate (for S. cerevisiae). Our multiobjective optimization methodology identified notable gene deletions by searching a vast solution space to highlight near-optimal strains on Pareto Fronts, balancing the above-cited three objectives. Moreover, preserving the metabolic constraints and the essential genes, this study produced robust results: seven significant strains of Y. lipolytica and seven strains of S. cerevisiae. We examined gene knockout to study the function of genes and pathways. In fact, by studying the frequently silenced genes, we found that when the GPH1 gene is knocked out in S. cerevisiae, the isocitrate lyase enzyme is activated, which converts the isocitrate into succinate. Our goals are to simplify and facilitate the in vitro processes. Hence, we present strains with the least possible number of knockout genes and solutions in which the genes are turned off on the same chromosome. Therefore, we present results where the constraints mentioned above are met, like the strains where only two genes are switched off and other strains where half of the knockout genes are on the same chromosome. This study offers solutions for developing an efficient in vitro mutagenesis for microorganisms and demonstrates the efficiency of multiobjective optimization in automatizing metabolic engineering processes.

Project description:To stem the increasing incidence of non-communicable diseases (NCDs) and obesity, front-of-pack labels (FOPLs) have been developed since 1989. Whereas several countries have already adopted one voluntarily, the European Community wants to harmonize an FOPL system that will be mandatory for all member states. The purpose of this narrative review is to describe what could be achieved or not by FOPLs, and to discuss if there is enough evidence to establish whether such labels are effective in modifying purchasing behavior, in directing individual dietary patterns towards a healthy and sustainable diet, and in reformulating food products by the food industry. Non-directive FOPLs, which are still under study, appear to be informative and well-accepted by consumers even if they require a cognitive effort. Conversely, directive FOPLs are supported by several studies, but they are mostly conducted in simulated scenarios and/or performed as retrospective studies. Nevertheless, directive FOPLs are rated as an intuitive tool, and they have demonstrated a high capacity to help consumers rank food products as more or less healthy. In conclusion, directive and non-directive FOPLs convey different messages. No FOPL individually can be considered exhaustive in relation to all the objectives outlined in this narrative review, and therefore, the development of a model synthesizing both messages is advisable. Many questions remain open, such as the possibility of reformulating pre-packaged products, how to deal with traditional products, and the impact on the incidence of NCDs and obesity. In the light of the complexity of factors that condition consumption choices and health, none of the current FOPLs can be considered a health policy tool on its own. The possibility of development remains open, but as the state of the art, these tools do not seem to be able to achieve all the European Community goals together. We can speculate that they could meet these goals only if they are integrated into a multi-tiered, structured health policy intervention.