Microbial Metabolic Engineering and Bioproduction

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.

The paper optimizes a P. putida strain for indigoidine production using 13C-MFA and metabolomics, identifying key metabolic bottlenecks and engineering improvements.

The paper integrates chlorophyll fluorescence data with genome-scale metabolic models to improve predictions of photosynthetic metabolism in diatoms.

This study demonstrates a modular design approach to selectively produce isoamyl acetate in microbes, achieving high titer, yield, and selectivity through proteome reallocation.

This paper introduces REKINDLE, a deep learning framework that generates kinetic models of metabolic networks with dynamic properties, demonstrating its efficiency and versatility through three studies.

Galaxy-SynBioCAD provides an automated pipeline for synthetic biology design and engineering, achieving an 83% success rate in retrieving validated 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 extends an enzyme-constrained GEM framework to include amino acid usage in yeast, showing how metabolic proteome demands affect amino acid availability and metabolic robustness.

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 develops a computational model of fission yeast metabolism to explore resource allocation and metabolic strategies under different glucose conditions.

This study uses thermodynamically-constrained genome-scale models to simulate metabolic responses of a deep-sea bacterium to different temperatures, revealing changes in metabolic efficiency and pathway utilization.

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 studies the flexibility of the amination network in E. coli by identifying key enzymes and amino acids involved in ammonium assimilation and glutamate production.

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.

This paper introduces time-optimal adaptation (TOA), a constraint-based modeling approach that explains cellular metabolic responses to environmental changes and demonstrates its ability to explain experimental phenomena.

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

This paper evolves highly specific and sensitive biosensors for alkaloids using a combined screening and selection approach, enabling improved microbial production of therapeutic plant metabolites.

This paper describes a method to decouple bacterial growth and production by permanently removing the origin of replication, enabling continued protein synthesis without growth.

The paper introduces METIS, an active learning workflow for optimizing genetic and metabolic networks, demonstrating significant improvements in efficiency with minimal experiments.

The paper develops a flux-based machine learning model to predict drug interactions in E. coli, accounting for metabolic heterogeneity and growth conditions, identifying 24 synergistic combinations.

The paper upgrades the GECKO method to create enzyme-constrained models for yeast and human, revealing common metabolic adaptations under stress.

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.