Back

Preventing production escape during scale-up using an engineered glucose-inducible genetic circuit

Tavares, L. F.; Ribeiro, N. V.; Zocca, V. F. B.; Correa, G. G.; Amorim, L. A. d. S.; Lins, M. R. d. C. R.; Pedrolli, D. B.

2023-03-06 synthetic biology
10.1101/2023.03.06.530450 bioRxiv
Show abstract

A proper balance of metabolic pathways is crucial for engineering microbial strains that can efficiently produce biochemicals at an industrial scale while maintaining cell fitness. High production loads can negatively impact cell fitness and hinder industrial-scale production. To address this, fine-tuning of gene expression using engineered promoters and genetic circuits can promote control over multiple targets in pathways and reduce the burden. We took advantage of the robust carbon catabolite repression system of Bacillus subtilis to engineer a glucose-inducible genetic circuit that supports growth and production. By simulating cultivation scale-up under repressive conditions, we preserved the production capacity of cells, which could be fully accessed by switching to glucose in the final production step. The circuit is also resilient, enabling a quick switch in the metabolic status of the culture. Furthermore, the simulated scale-up process selected best-growing cells without compromising their production capability, leading to higher product formation at the end of the process. As a pathwayindependent circuit activated by the preferred carbon source, our engineered glucose-inducible genetic circuit is broadly useful and imposes not additional cost to traditional production processes. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=144 SRC="FIGDIR/small/530450v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@f02f6corg.highwire.dtl.DTLVardef@b9631dorg.highwire.dtl.DTLVardef@11a2309org.highwire.dtl.DTLVardef@f255ac_HPS_FORMAT_FIGEXP M_FIG C_FIG

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.