Microbial Metabolic Engineering and Bioproduction

The paper introduces a bond graph methodology to unify stoichiometry, thermodynamics, and kinetics in dynamic biomolecular modelling, demonstrating its application to E. coli.

The paper models the metabolic burden of producing hCDKL5 in bacteria and identifies targets for optimized production.

A CRISPRi screen identifies proteasome regulation as key to acetic acid tolerance in yeast, revealing gene targets for improving industrial yeast strains.

The paper integrates protein abundance, metabolic fluxes, and phenotypic traits in yeast to uncover metabolic bases of phenotypic variation.

The paper introduces the Marburg Collection, a Golden Gate DNA assembly framework for synthetic biology in Vibrio natriegens, providing standardized genetic parts and tools for plasmid construction.

SCOUR predicts metabolite-dependent regulatory interactions in metabolic networks using a stepwise machine learning framework, achieving high accuracy in identifying single-metabolite-controlled reactions.

The paper continuously evolves a synthetic auxotrophic E. coli strain and finds it remains contained over 100 days and in co-culture with mammalian cells.

The paper harmonizes metabolic reactions and compounds across databases by improving atom-level consistency and establishing hierarchical relationships.

The paper develops genome-scale metabolic models of human dopaminergic neurons to study metabolic dysfunction in Parkinson's disease using exometabolomic and transcriptomic data.

This paper introduces a method that integrates enzymatic and thermodynamic constraints into genome-scale metabolic models to improve pathway prediction accuracy.

The paper uses genome-scale models to compute proteome allocation under different conditions, revealing variability in cofactor use and protein composition for E. coli growth.

This study uses a kinetic 13C dilution strategy to analyze oleate metabolism in E. coli and shows that arcA knockout enhances oleate utilization and metabolic reprogramming.

This paper demonstrates the reconstruction of a genome-scale metabolic model for Hansenula polymorpha using the RAVEN toolbox through a homology-based approach.

This study improves intracellular flux predictions in CHO cells by incorporating maintenance energy, enhancing accuracy for TCA cycle fluxes.

The paper tests theoretical minimal genomes for Mycoplasma genitalium using a whole-cell model, finding that simulated minimal gene sets fail to produce dividing cells, highlighting the need for species-specific low essential gene identification.

This study improves the biosynthesis of the cannabinoid precursor olivetolic acid in Yarrowia lipolytica by overcoming rate-limiting steps, achieving an 83-fold increase in OLA titer.

The paper designs an irreversible metabolic switch in E. coli to reduce inducer usage for scalable microbial chemical production.

The paper introduces COMMIT, a method that improves microbial community models by considering metabolite leakage and community composition, leading to better gap filling and identification of microbial roles.

This paper introduces an evolutionary algorithm combining machine learning and dynamic flux balance analysis to design microbial communities by optimizing environmental conditions for target phenotypes.

The paper evolves mutant strains of Corynebacterium glutamicum that utilize fructose more efficiently, leading to increased biomass and L-lysine production.

The paper formulates abiotic constraints that shape the quantitative metabolome and validates them using Escherichia coli data.

This paper introduces two computational methods, BTW and HIP, to improve genome-scale metabolic models by accounting for environmental effects on biomass composition, showing differences in model performance and phenotypic predictions.

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.

The paper introduces a method to find metabolic pathways in large networks by using atom-conserving substrate-product pairs and graph search algorithms.

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.

The paper identifies gene deletions that enhance indigoidine production in Pseudomonas putida using fitness profiling and bioreactor experiments.

The paper introduces an online tool for techno-economic analysis of bioprocesses, enabling researchers to evaluate commercial competitiveness based on process parameters and strain engineering outcomes.

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

This paper introduces a constraint-based framework, Network Response Analysis (NRA), to improve metabolic engineering by incorporating physiological constraints and genome editing limitations.

The paper develops a genome-scale differential flux analysis method to identify metabolic changes in human lung cells infected with SARS-CoV-2, revealing deregulation in lipid metabolism.