Microbial Metabolic Engineering and Bioproduction

The paper introduces PROXIMAL2, an improved algorithm for predicting degradation pathways of phenolic compounds in the human gut microbiota, showing higher accuracy than PROXIMAL and RetroPath RL.

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.

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

The paper develops a genome-scale metabolic model for Rothia mucilaginosa to predict its metabolic capabilities and identify potential antimicrobial targets for cystic fibrosis treatment.

This paper presents a computational workflow and software package to robustly estimate enzyme kinetic parameters, reconciling inconsistencies in data from in vitro and in vivo experiments.

The paper develops 2-stage microfermentation protocols for high-throughput evaluation of engineered E. coli strains for producing various products.

This paper shows that horizontal gene transfer can enable dynamic division of labor in microbes to stabilize complex metabolic pathways.

This paper engineers Yarrowia lipolytica to produce gastrodin, achieving 13.22 g/L in a fermenter, the highest reported level.

The paper uses machine learning to optimize p-coumaric acid production in yeast through DBTL cycles, achieving a 68% increase in production.

This study investigates how Pseudomonas putida adapts to xylose by combining metabolic engineering and adaptive evolution, revealing genomic and metabolic plasticity that enables growth on the non-native sugar.

This study optimizes itaconic acid production by Ustilago maydis through co-feeding glucose and acetate, combining experimental data, design of experiments, and metabolic modeling.

The paper develops a computational workflow to predict metabolite-gene-pathway associations in cancers using genome-scale metabolic models and mutation data, showing significant associations with specific somatic mutations.

This paper develops a spatial flux balance analysis method to simulate single-cell metabolism by incorporating diffusion effects across a grid-based system.

The paper introduces FlowGAT, a hybrid FBA-machine learning method to predict gene essentiality in E. coli by leveraging metabolic network structure and knock-out fitness data.

This paper develops an LC-UV/RI-MS2 method to analyze metabolites secreted by Ustilago trichophora during BDO degradation, identifying malic acid, succinic acid, erythritol, and mannitol with 33% carbon yield.

This study optimizes p-coumaric acid production in Saccharomyces cerevisiae by combining statistical experimental design with process and genetic factors, achieving a 168-fold increase in titer.

This study identifies a non-canonical fumarate-based ammonium assimilation pathway in B. subtilis that compensates for glutamate and aspartate biosynthesis defects.

This study integrates the MEP pathway into E. coli to enhance terpene production, finding that balancing pathway intermediates maximizes geraniol yield.

The paper uses genome-wide host-pathway interaction screens to identify metabolic targets for optimizing cis-cis-muconic acid production in yeast.

The paper introduces SAMBA, a computational method that predicts metabolite changes under metabolic perturbations using genome-scale metabolic network modeling and compares predictions with experimental data.

This paper introduces ARCTICA, a framework combining constraint-based modeling and machine learning to predict metabolic flux control in cyanobacteria, identifying key enzymes and reactions that limit photosynthetic activity.

The paper introduces MIGRENE, a computational framework for constructing personalized microbiome metabolism models using metagenomic data.

This paper constructs a comprehensive pangenome-scale metabolic model for the Klebsiella pneumoniae species complex, showing improved accuracy in growth predictions compared to previous models.

COSMIC-dFBA predicts cell density, antibody titer, and metabolite concentrations in bioreactors by integrating machine learning with genome-scale metabolic models.

This paper streamlines the genome of Synechococcus elongatus UTEX 2973 to improve growth and productivity using CRISPR/Cas3 and computational tools.

The paper derives analytic solutions for optimizing enzyme allocation in unbranched metabolic pathways using convex optimization and applies the results to bacterial growth.

Snowprint predicts regulator:DNA interactions and designs promoters for uncharacterized regulators, enabling the discovery of new biosensors for various compounds.