Microbial Metabolic Engineering and Bioproduction

This study reprograms Geobacillus thermoglucosidasius to produce high-purity lactic acid at 60°C using rational and semi-rational design strategies, achieving high yield and productivity.

The paper calculates the upper limit electrical-to-energy conversion efficiency for producing jet fuels using electromicrobial pathways.

This study engineers the complete reductive glycine pathway in yeast to enable formate and CO2 assimilation, achieving 8-fold improved growth through adaptive evolution and reverse-engineering.

The paper uses genome-scale metabolic models to identify metabolic differences between non-Saccharomyces yeasts, finding that Complex I in the electron transport chain distinguishes Metschnikowia pulcherrima from other species.

OptEnvelope identifies optimal knockout strategies for coupling growth and metabolite production using mixed-integer linear programming, benchmarked on E. coli and S. cerevisiae for acetate, glycerol, succinate, and ethanol.

This paper evolves a Saccharomyces cerevisiae strain tolerant to oxidative stress, leading to higher taxadiene production, and identifies metabolic changes contributing to this improvement.

The paper presents a framework using genome-scale metabolic reconstructions to interpret genotype-to-phenotype associations and identifies limitations in phenotype prediction in metabolism.

This study constructs a multi-organ maize metabolic model to analyze temperature stress responses, identifying a thermodynamics-reducing power-energy generation axis that differentiates heat and cold stress.

This study investigates how heterologous protein production affects metabolic fluxes in Pseudomonas putida, finding that it reshuffles periplasmic metabolism and decouples catabolism from anabolism.

This paper engineers Yarrowia lipolytica to produce geraniol, achieving a high titer of 1 g/L through metabolic pathway optimization and gene overexpression.

This study identifies how Yarrowia lipolytica metabolizes hydrocarbons and DP oil, revealing proteome reallocation for growth and chemical production, but also growth inhibition due to reduced protein biosynthesis.

This paper creates and validates five strain-specific genome-scale metabolic models for Corynebacterium striatum using in silico and in vitro methods, and introduces software for model curation.

This paper updates geckopy to improve enzyme-constraint modeling by integrating proteomics data and thermodynamic constraints, benchmarking against E. coli datasets.

This study expands the genetic toolbox of Escherichia coli Nissle 1917 to enhance its utility as a synthetic biology chassis through plasmid engineering, conjugation strategies, and cell-free protein synthesis.

The paper introduces a Markov constraint to uniquely identify elementary flux mode weights in unimolecular metabolic networks, demonstrating its application in analyzing lipid metabolism in Alzheimer's disease.

CAMEL predicts protein abundances by combining constraint-based modeling and machine learning, achieving high accuracy in E. coli and moderate performance in S. cerevisiae.

The paper analyzes proteome efficiency across metabolic pathways in E. coli, finding that efficiency increases along the nutrient flow, with costly pathways near optimal and transporters and central metabolism over-abundant.

This study shows that co-cultivating two engineered yeast strains reduces byproducts and improves ethanol yield in anaerobic fermentation.

The paper uses metabolic modeling to analyze energy and redox balance in Synechocystis sp. PCC 6803 for optimizing bio-alkene production.

This study investigates carbon catabolite repression in Pseudomonas putida M2 during simultaneous sugar and aromatic co-utilization, revealing simultaneous repression by sugars and aromatics and the role of crc and small RNAs.

This paper uses GPT-4 to extract data from synthetic biology papers, creating structured datasets that improve ML predictions for yeast fermentation.

The paper introduces Golden Standard, a modular cloning tool compatible with various bacterial hosts, enabling standardized genetic circuit assembly and validated for performance and portability.

The paper introduces a method to explore abundance-growth space for microbial communities, revealing abundance-dependent metabolic phenotypes through constraint-based modeling.

This paper introduces a hybrid control system for microbial bioprocesses that addresses process-model mismatch by combining in silico and in-cell controllers.

The paper develops growth-coupled microbial strains for high-yield indigoidine production using iterative engineering and computational evaluation.

This paper develops a coarse-grained resource allocation model to explain cellular growth laws and resource allocation strategies in unicellular microbes.

This paper analyzes the geometric properties of elementary flux modes in metabolic networks, showing that EFMs in the relative interior of the flux cone are rare and introducing a complexity measure based on their degree.

The paper reconstructs a high-quality genome-scale metabolic network for Penicillium rubens and identifies bottlenecks in defining specialised metabolites.

The paper demonstrates that genome engineering of E. coli improves TPAL retention and enables its conversion to valuable products like para-xylylenediamine.

This study uses genome-scale metabolic modeling to show that microbial metabolic hierarchies are constrained by central metabolism, limiting their evolutionary diversity.