Microbial Metabolic Engineering and Bioproduction

This study demonstrates the use of photosynthetic bacteria to power biocatalytic reactions using light, achieving high conversion rates of various substrates into valuable chemicals.

This paper introduces CommPhitting, a dynamic model that predicts microbial community phenotype abundances and growth kinetics using experimental data and optimization.

This study compares model accuracy and data efficiency across machine learning models for predicting protein expression from DNA sequences using experimental data from E. coli and S. cerevisiae.

This paper identifies prediction anomalies in a constraint-based metabolic model by incorporating enzyme compartmentalization, improving pathway structure accuracy and resolving constraint conflicts.

This paper engineers Pseudomonas putida KT2440 to produce medium- and long-chain free fatty acids and oleochemicals, achieving titers up to 670 mg/L.

The paper describes the directed evolution of biosensors to improve detection of phloroglucinol and related single-ring aromatics.

ChiMera is a pipeline that reconstructs, evaluates, and visualizes bacterial genome-scale metabolic models using efficient tools and a user-friendly interface.

The paper updates a genome-scale metabolic model for Xylella fastidiosa subsp. pauca, validating it against experimental data for aerobic metabolism and gum production.

This paper demonstrates that synthetic in vivo compartmentalisation of enzymes in yeast significantly increases product titre and modulates product ratios.

This study identifies an autonomous replication region in cyanobacteria and develops an expression vector system using it, demonstrating broad host compatibility and inducible gene expression.

The paper demonstrates how enzyme levels influence metabolic flux through three paradigms and introduces an algorithm to predict flux changes from enzyme expression in yeast and humans.

ImmCellFie infers cell metabolic capabilities from transcriptomic or proteomic data using genome-scale models and provides interactive analysis tools.

The paper identifies niche-specific metabolic targets in pathobionts by analyzing genome-scale metabolic networks and demonstrates their potential for targeted antimicrobial therapies.

NICEgame identifies and resolves metabolic gaps in genome-scale models by suggesting alternative reactions and genes, improving gene essentiality predictions in E. coli.

gMCStool identifies metabolic vulnerabilities in cancer by predicting synthetic lethal interactions using genome-scale metabolic networks and RNA-seq data.

This study optimizes fatty acid production in oleaginous yeasts by identifying optimal C/N ratios and temperatures using RSM and analyzing fatty acid composition changes.

The paper engineers genome-reduced Pseudomonas putida strains to assimilate formate as a carbon source using a modular approach and adaptive evolution, achieving faster growth and two distinct catabolic pathways.

The paper demonstrates that substrate pulsing in microbial co-cultures can stabilize interactions by creating periodic fitness advantages, validated through computational modeling and experimental testing.

The paper establishes a universal quantitative relationship between metabolic responses to nutrient conditions and metabolic inhibition, validated across models and applicable without prior knowledge of system details.

The paper investigates metabolic robustness and transcriptomic reprogramming in a cold-adapted bacterium at different temperatures, revealing key intermediates and pathway cross-talk involved in adaptation.

The paper engineers a synthetic one-carbon fixation pathway in Cupriavidus necator that achieves higher biomass yield than the native Calvin Cycle.

NEMETEX visualizes metabolic exchange networks from metagenomics and flux balance analysis data, integrating abundance, taxonomy, and exchange rates for easier interpretation.

This study shows that choosing the right objective function in flux balance analysis improves predictions of replicative lifespan in yeast.

MAMBA integrates semi-quantitative metabolomic data with gene-centric omics to improve metabolic network characterization and metabolite prediction in yeast under heat stress.

The paper develops a pipeline to predict cellular metabolism from gene expression data using genome-scale models, applied to study TAG overproduction in Chlamydomonas reinhardtii.

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 demonstrates the successful restoration of valine biosynthesis in mammalian cells by integrating synthetic pathways from E. coli, showing that ancient metabolic pathways can be reactivated in metazoans.

This paper engineers a synthetic C2 auxotrophy in Pseudomonas putida to enable alternative sugar catabolism via the phosphoketolase shunt, improving growth through metabolic rewiring.