Microbial Metabolic Engineering and Bioproduction

The paper introduces MOFA, an add-on tool for the COBRA Toolbox that enables multi-objective flux analysis of metabolic trade-offs using genome-scale models.

This paper predicts the evolution of fitness-costly traits by designing selection niches using metabolic models and validates it in yeast for increased aroma compound secretion.

TranSyT automates the integration of transport reactions into genome-scale metabolic models by compiling curated transporter data and providing rapid genome annotation.

The paper constructs a genome-scale metabolic model for Geobacillus sp. LC300 and uses it to predict metabolic engineering strategies for industrial compound production.

This study investigates how growth rate and nutrient quality affect gene expression capacity in bacteria, showing that metabolism governs resource usage and that low growth in high-quality medium maximizes synthetic gene expression.

This paper develops the first multi-tissue diel genome-scale metabolic model for Quercus suber, highlighting suberin biosynthesis pathways and enabling analysis of tissue interactions.

The paper develops a dynamic genome-scale model to compare redox balancing strategies in yeast species during fermentation, revealing novel metabolic pathways in cryotolerant strains.

The paper introduces and analyzes elementary growth modes and vectors for kinetic models of cellular growth, showing their relationships to other concepts and deriving bounds on growth rates.

This paper develops a dynamic genome-scale model to analyze redox balance strategies in yeasts during batch cultures, showing how cryotolerant strains achieve metabolic stability.

CASTLE is a database of synthetic lethal sets predicted from metabolic networks of over 130 organisms using the Fast-SL algorithm.

The paper engineers a lactate biosensor system that functions in glucose-rich and anaerobic environments by modifying a natural promoter.

This study integrates gene expression and nutrient availability data to model metabolism in hepatocellular carcinoma, showing glucose affects proliferation and lactate production more than glutamine.

The paper repurposes chloramphenicol acetyltransferases to create efficient ester biosynthesis enzymes, demonstrating high titer and conversion in microbial hosts.

The paper introduces a method to reconstruct genome-scale metabolic networks with local reactions and transcriptional regulation, achieving accurate phenotype predictions in multiple organisms.

This paper engineers Escherichia coli to synthesize squalene by optimizing squalene synthase and mevalonate pathway regulation.

This paper develops numerical methods to optimize the growth rate of genome-scale metabolic models by treating enzyme concentrations as variables and clarifying simplifying assumptions in the optimization framework.

This paper develops a Python tool for studying string chemistries and introduces a network-pruning algorithm to generate minimal metabolic networks.

ComMet is a method for analyzing and comparing metabolic flux states in genome-scale models, identifying distinct network modules and differences in human adipocyte metabolism under varying amino acid uptake conditions.

The paper shows that substrate concentrations in thermodynamically constrained reactions reflect upstream fluxes and can transmit information about metabolic rates in E. coli.

The paper presents an analysis pipeline using machine learning and network analysis to interpret metabolic reaction fluxes from gene knockouts in a whole-cell model of Mycoplasma genitalium.

This paper introduces minimal pathways for scalable metabolic pathway analysis in genome-scale models, improving gene importance predictions and metabolite exchange analysis in microbial and mammalian systems.

The paper demonstrates that minicells can express engineered plasmids and support substantial peptide bond formation, suggesting potential for lineage-agnostic organism engineering.

The paper shows that promoter strength varies depending on gene context, as demonstrated through plasmid construction and polymer production experiments.

This study optimizes benzylglucosinolate production in yeast by comparing plasmid and genome integration methods and enhancing key enzymes in the biosynthetic pathway.

This paper introduces an unbiased method for metabolic flux inference by integrating thermodynamic constraints and 13C labeling data, revealing novel flux patterns in budding yeast.

The paper constructs a synthetic promoter library in E. coli and uses machine learning to predict promoter strength, finding that an XgBoost model achieves optimal performance.

The paper constructs a hybrid EMP pathway in Corynebacterium glutamicum by replacing the first phase with non-oxidative glycolysis, enabling efficient carbon and phosphate metabolism under limited phosphate conditions.

The paper predicts enzyme regulation policies using statistical thermodynamics and hybrid optimization-reinforcement learning to maintain solvent capacity in E. coli central metabolism.

This study repurposes the yeast peroxisome to compartmentalize a toxic enzyme, improving (S)-reticuline production by reducing NCS toxicity and increasing BIA titer.

The paper develops an FBA-based model using gene expression data to predict carbon sources for Bacillus subtilis, showing good agreement with 13C metabolic flux data.