Microbial Metabolic Engineering and Bioproduction

The paper introduces RBC-GEM, a comprehensive metabolic reconstruction of human red blood cells, curated from proteomic data and expanded significantly over previous models.

This study builds tissue-specific metabolic models for Drosophila to identify metabolic dysregulation in muscle caused by a high sugar diet.

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.

This study evolves methylotrophic E. coli to achieve faster growth on methanol than native methylotrophs using dynamic copy number variation.

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 compares anaerobic growth and metabolism of three E. coli strains using 13C-MFA, revealing differences in growth rates, glucose uptake, and pathway activity.

The paper develops an analytical model to predict microbial resource allocation patterns under different nutrient conditions and identifies a metabolite-based regulatory mechanism for optimal growth.

The paper reports an improved genome sequence and annotation of Cutaneotrichosporon oleaginosus ATCC 20509 to support its use as a biotechnological platform for microbial oil production.

This study develops a novel modeling approach to analyze metabolic adaptation in E. coli under osmotic stress, revealing prioritization of thermodynamically favorable pathways.

This paper improves microbial oil production in C. oleaginosus by identifying genetic targets and medium supplements through a DBTL approach, achieving up to 56% lipid content.

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.

The paper introduces an algebraic framework to define and assess redundant forms of biochemical networks using reactant-dependent stoichiometry matrices.

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.

VaMP integrates neural networks and metabolic models to link genetic variants with cancer phenotypes, identifying significant genes and metabolic signatures across five cancer types.

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.

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

This paper demonstrates a hybrid in silico/in-cell controller that reduces toxicity from excessive enzyme overexpression by adapting to process-model mismatch in engineered E. coli.

This study develops an automated method to quantify metabolic activity from isotope tracing data, revealing reprogrammed mitochondria-cytosol exchange cycles in tumor tissue.

This study investigates the challenges in producing 1,2-propanediol using solventogenic Clostridium species, finding that recombinant strains fail to convert intermediates efficiently and that regulatory mechanisms may limit methylglyoxal pathway activity.

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.