Microbial Metabolic Engineering and Bioproduction

This study develops a genome-scale metabolic model for Anaerotignum neopropionicum to understand ethanol metabolism via the acrylate pathway and validate it with experimental data.

A substrate graph neural network plus protein-sequence CNN predicts enzyme kcat (test Pearson r 0.71), tracks mutation effects, and parameterizes enzyme-constrained models for 343 yeast/fungi species.

This paper reports the metabolic engineering of Rhodotorula toruloides to overproduce triacetic acid lactone (TAL) by enhancing acetyl-CoA flux through gene overexpression.

merlin v4.0 improves genome-scale metabolic model reconstruction with new algorithms and tools, supporting multiple organisms and offering competitive performance.

This paper uses multi-agent reinforcement learning to optimize microbial strain production, showing improved convergence and stability in synthetic biology applications.

gcFront identifies Pareto optimal growth-coupled cell factory designs by optimizing cell growth, product synthesis, and coupling strength using a genetic algorithm.

The paper optimizes and implements reverse beta-oxidation pathways in bacteria for carbon-negative synthesis of C4-C6 acids and alcohols, achieving high product titers in engineered strains.

This study uses genome-scale metabolic modeling with patient transcriptomic data to identify metabolic vulnerabilities in PDAC and suggest potential therapeutic targets and drug combinations.

The paper develops a genome-scale model of the yeast protein secretory pathway to identify targets for improving recombinant protein production, finding that the secretory pathway has more limited capacity than metabolism.

This study uses a synthetic yeast system to generate complex structural variations and demonstrates their impact on gene expression and astaxanthin yield.

The paper develops a Golden Gate Assembly platform (RtGGA) for Rhodotorula toruloides to enhance metabolic pathway engineering, resulting in a 41% increase in carotenoid production.

The paper develops a genome-scale metabolic model for Ustilago maydis to study metabolic adaptations and biotechnological applications.

IgemRNA is a transcriptome analysis tool that integrates genome-scale metabolic modeling, offering pre-processing, analysis, and visualization with a graphical interface.

The paper identifies a novel L-lysine exporter from cow gut microbiome metagenomic fragments, improving L-lysine tolerance and productivity in bacterial hosts.

This study shows that faster growth of Clostridium autoethanogenum improves gas fermentation yields and productivities for ethanol and 2,3-butanediol.

OptDesign identifies optimal strain design strategies for biochemical production in E. coli by combining regulation and knockout interventions using a two-step approach.

This study quantifies the proteome of Clostridium autoethanogenum under different gas conditions, revealing proteome allocation priorities and enzyme dynamics.

ARBRE predicts biosynthetic pathways to industrially important aromatic compounds using a comprehensive reaction network and computational tools.

The paper uses metabolic modeling and sequence analysis to show that coenzyme redundancy reduces protein costs and promotes coenzyme specificity in E. coli.

The paper introduces moped, a Python package for reproducible metabolic model construction, curation, and analysis, supporting both genome-based reconstruction and SBML import.

The paper reconstructs a genome-scale metabolic model for chicken and analyzes its connectivity and potential biomarkers using transcriptomics data.

The paper quantifies microbial robustness using a high-throughput strategy and finds that robustness is function-specific with trade-offs.

This study compares the metabolic and proteomic adaptations of E. faecalis wildtype and a glutamine synthase mutant under varying pH conditions, revealing energy demand as the key factor in adaptation.

The paper presents a comparative analysis of 192 protozoan parasite genomes using a metabolic network reconstruction pipeline to identify species-specific metabolic differences and improve experimental system selection.

The paper establishes an upper limit on the energy efficiency of electromicrobial protein production using a molecular-scale model.

The paper develops a whole-cell metabolic model for yeast to predict compartment-specific proteome constraints that explain metabolic strategies under different growth conditions.

INTEGRATE integrates metabolomics and transcriptomics data to identify metabolic flux regulation at the enzyme expression and metabolic levels in cancer cell lines.

The paper introduces a method to quantify genomic, biochemical, and phenotypic evidence for metabolic network reconstructions, improving accuracy by integrating continuous evidence.

This study engineered cyanobacteria to convert CO2 into secreted short-to medium-chain hydrocarbons using synthetic metabolic pathways, achieving 230 mg/L of 1-alkene with 15% carbon partitioning.

This study shows how deleting CRP in E. coli leads to metabolic and proteomic changes that allow growth recovery through mutations and pathway re-allocation.