Microbial Metabolic Engineering and Bioproduction

The paper integrates time-series transcriptomics with genome-scale metabolic models to analyze yeast response to doxorubicin, identifying flux clusters and pathway dynamics.

This study identifies a novel salvage pathway for coenzyme A in Bacillus subtilis, involving the uptake of cysteinopantetheine via a cystine transporter.

The paper uses multi-omics integration to identify mechanisms of ethanol stress response and respiro-fermentative metabolism in Kluyveromyces marxianus.

The paper shows cells prioritize restoring amino acids based on demand and biosynthetic cost, with arginine being the highest priority.

This paper develops a genomically integrated orthogonal translation system in E. coli, enabling non-canonical amino acid incorporation and creating a biosafe synthetic chassis for enhanced protein production.

This paper introduces Nox Ortho, an enzyme that enables precise control of redox reactions using NMN+ as a cofactor, achieving high specificity and enabling chiral-pure product synthesis.

This paper develops a novel flux balance analysis approach for light-limited cyanobacterial growth, incorporating photoinhibition and variable quantum yield to predict growth properties.

The paper develops a modular genetic toolkit for environmental Actinomycetes, enabling metabolic engineering and complex circuit design in Rhodococcus, Nocardia, and Gordonia.

PhysioFit quantifies cell growth parameters and extracellular fluxes using flexible growth models and provides a user-friendly software tool for non-computational biologists.

The paper measures the growth burden of 301 BioBricks in E. coli and finds that 19.6% reduce growth, with most burdens explained by gene expression resource diversion.

This study characterizes the ethanolamine utilization BMC in E. coli using genetic, omics, and modeling approaches, revealing ethanolamine as a dual carbon and nitrogen source.

The paper systematically analyzes genome-scale metabolic models of six microorganisms to identify multi-target drug candidates that selectively target specific organisms.

This study engineers Komagataella phaffii to produce itaconic acid from methanol, achieving titers of 55 g/L and a yield of 0.24 g/g.

The paper identifies cis-regulatory elements in algae to create synthetic promoters, validated through in vivo testing and high-throughput screening.

This paper shows that 6-phosphogluconolactonase is essential for efficient oxPPP function in E. coli, as its absence is compensated by an extracellular bypass pathway.

This paper introduces an accelerated adaptive laboratory evolution (aALE) method using divalent metal cations to improve microbial tolerance to high glycerol concentrations in Cupriavidus necator H16.

This study reprograms marine bacteria to convert lignin monomers into valuable chemicals with a CRISPRi system, achieving a 4.89-fold increase in protocatechuate yield.

The paper uses machine learning and metabolic modeling to show phosphoglycerate mutase is critical for Chlamydia trachomatis persistence under nutrient starvation.

This paper identifies mutations improving stress tolerance in E. coli for melatonin production using a data-driven approach integrating adaptive evolution data and multi-omics analysis.

The paper reconstructs pangenomes of Lactobacillaceae to generate metabolic models for 26 species, enabling species-specific metabolic trait predictions.

The paper introduces logistic PCA (LPCA) to compare genome-scale metabolic models by identifying mechanistic differences in metabolic pathways across diverse datasets.

Panera improves microbial community metabolic modeling by integrating 16S data with pan-genus models, reducing prediction error by 10%.

GENTANGLE is a computational tool for designing overlapping gene pairs in microbial genomes, enabling safer genetic engineering through gene entanglements.

The paper shows that non-consecutive TCA cycle enzymes MDH and ICD interact in droplets, enhancing ICD activity and affecting carbon-nitrogen metabolism in B. subtilis.

This study demonstrates a synergistic microbial system using anaerobic and aerobic bacteria to efficiently convert lignin-related compounds into valuable chemicals with minimal carbon loss.

This study uses CRISPRi screening to identify transcription factors crucial for growth and carbon utilization in Clostridium ljungdahlii, improving ethanol production through deoR optimization.

The paper develops a metabolic engineering technology to suppress multiple genes in yeast and applies it to enhance carotenoid production.

This study compares metabolic models of microbial communities generated by different automated tools and a consensus approach, finding that reconstruction methods significantly affect model structure and functionality.

This paper engineers methionine-auxotroph E. coli by evolving enzymes from C. glutamicum to enable growth in minimal medium.

PromoGen generates species-specific functional promoters using transfer-learning based deep learning, outperforming other models and showing experimental activity in Bacillus subtilis.