Microbial Metabolic Engineering and Bioproduction

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 study redesigns bacterial catabolism by repurposing the shikimate pathway for aromatic compound production, achieving over 89% of theoretical yield.

This paper demonstrates a cybernetic control method for microbial co-cultures using temperature adjustments to stabilize composition without genetic engineering.

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.

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

The paper introduces MACAW, a workflow for detecting errors in genome-scale metabolic models, showing that error types vary with curation methods and that pathway-level errors are particularly challenging.

The paper compares methods for quantifying GFPuv yield in E. coli bioreactors, identifying optimal harvest times and highlighting the strengths of imaging over other techniques.

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.

This study engineering Rhodosporidium toruloides to produce punicic acid from glucose and wood residues, achieving 12% lipid accumulation and 451.6 mg/L titer in shake flasks.

The paper designs a high-activity CRISPR sgRNA library for Yarrowia lipolytica and uses it to identify genes involved in acetate and hydrocarbon tolerance.

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 paper develops sugar-inducible promoters in Thermoanaerobacter kivui to enable controlled gene knock-down and study metabolic pathways.

The paper identifies aroB as a bottleneck in pABA production in Pseudomonas putida by using combinatorial engineering and statistical modeling to optimize the shikimate pathway.

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.

The paper links cell growth rate to ribosome replication and macromolecular synthesis rates, showing how these constraints influence biomass composition in flux balance models.

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 paper uses self-supervised learning to create low-dimensional representations of genome-scale metabolic flux data, improving cell type classification and comparison across organisms.

The paper introduces a pseudo batch transformation method to simplify analysis of fed-batch fermentation data by mapping it to a constant volume batch process.

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.

The paper introduces a method to decompose flux vectors into elementary flux modes using geometric insights, focusing on low-degree decompositions in genome-scale metabolic networks.

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.

This study investigates the functional plasticity of HCO3- uptake and CO2 fixation in C. necator H16 by testing endogenous and heterologous genes, showing that DIC transporters can support autotrophic growth and higher cell densities.

This paper introduces a continuous dynamic genome-scale model to predict intracellular fluxes during batch fermentation, capturing all growth phases and secondary metabolism in Saccharomyces species.

The paper introduces a convex representation of metabolic networks using Michaelis-Menten kinetics to predict cellular functions while incorporating metabolite concentrations.