Microbial Metabolic Engineering and Bioproduction

The paper presents a unified theory of optimal metabolic states by formulating metabolic behavior as an economic problem with fitness objectives, linking existing models through shared optimality conditions.

This paper introduces a framework to unify metabolic modeling by defining value structures that represent metabolic trade-offs as economic variables.

The paper engineers a synthetic C1-assimilation cycle in E. coli to enable growth on formate, demonstrating the feasibility of synthetic carbon fixation from CO2.

This paper designs and tests a minimal artificial glycolytic pathway that efficiently converts various monosaccharides to acetyl-CoA with high yield.

This study shows acetate enhances Escherichia coli growth by compensating for reduced glycolytic flux through co-utilization with glucose.

This study uses resource balance analysis to evaluate proteome allocation and metabolic fluxes in yeast under different environmental conditions, identifying key limitations in biosynthetic pathways.

This paper optimizes an E. coli strain for formate-based bioproduction using the reductive glycine pathway, achieving improved growth and lactate production.

The paper develops a genome-scale metabolic model for Drosophila melanogaster to study metabolic disorders and Parkinson's disease.

The paper identifies that the Entner-Doudoroff pathway provides a metabolic advantage in dynamic nutrient environments by enabling faster response times during nutrient upshifts.

The paper introduces a modular expression platform for screening heterologous protein expression across diverse prokaryotic hosts, enabling data-driven host selection.

This study analyzes the adaptability and robustness of Mycoplasma pneumoniae's central carbon metabolism using metabolomics data and a dynamic model, identifying key control hubs.

This paper integrates external biomass reactions into metabolic models to improve prediction accuracy for gene essentiality and growth.

The paper uses metabolic modeling to compare plant-associated Pseudomonas strains and identifies metabolic differences between beneficial and pathogenic species.

The paper uses metabolic network models to analyze the Gardnerella pangenome and identifies core metabolic functions in the vaginal environment.

The paper describes an automated workflow for generating high-throughput microbial multi-omics datasets and evaluates its effectiveness with three model organisms.

This paper demonstrates the bioconversion of cellulose into bisabolene, a biojet fuel candidate, using rumen bacteria and non-conventional yeast in a sequential bioprocess.

mergem merges and compares genome-scale metabolic models using universal identifiers to enable comparison across different pipelines and namespaces.

The paper uses kinetic metabolic modeling to study how Saccharomyces cerevisiae adapts its carbohydrate storage metabolism to feast/famine conditions, identifying enzyme adjustments and unexplained kinetic phenomena.

The paper introduces CHESHIRE, a deep learning method to predict missing reactions in metabolic networks using topology, outperforming existing methods and improving phenotypic predictions.

The paper develops a physiologically informed kinetic model of yeast glycolysis connected to central metabolism, showing it captures experimental dynamics and explains pathway contributions under glucose perturbations.

This study improves (2S)-naringenin production in yeast by fine-tuning p-coumaric acid synthesis, reducing its extracellular accumulation and increasing yield to 933 mg/L.

scFLUX predicts single-cell metabolic fluxome and variations using transcriptomics data, offering a web server and local tools for individual cell or sample-level analysis.

The paper develops an RNA switch-based selection system in yeast to enable enzyme evolution by linking compound production to selection gene expression.

The paper systematically investigates synthetic operon designs to predict and control protein expression levels in bacterial hosts using machine learning.

The paper investigates how antimicrobial exposure affects bacterial metabolism using transcriptomics and computational modeling, finding that experimental conditions influence metabolic networks more than antimicrobial type, but common metabolic strategies exist.

The paper constructs and analyzes a modular T7 RNA polymerase-based gene expression system in Pseudomonas putida to improve heterologous protein production.

The paper develops METAFlux to infer metabolic fluxes from bulk and single-cell RNA-seq data, showing improved accuracy over existing methods in cancer TME analysis.

This paper develops a reactive species module for genome-scale metabolic models to identify metabolic targets in colorectal cancer and highlights ferroptosis as a therapeutic target.

The paper develops a framework to analyze optimal enzyme operation modes under varying conditions, showing that enzyme efficiency depends on reactant concentrations and mechanism type.

The paper develops a platform for global metabolic reprogramming in Vibrio FA2 using CRISPRi-mediated dual-gene knockdown and metabolic interaction analysis to enhance glycine production and assess antibiotic resistance.