Microbial Metabolic Engineering and Bioproduction

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

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 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.

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

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

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.

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.

The paper introduces OptoAMP circuits that amplify light-induced transcriptional responses in microbial metabolism, improving control over metabolic pathways in fermentations.

The paper develops a hybrid modeling framework integrating Boolean and enzyme-constrained networks to study the interplay between nutrient signaling and metabolism in yeast, revealing new insights into their regulatory effects.

The paper identifies nutrient uptake constraints in Lactococcus lactis using a proteome-constrained model and experimental data, showing how these constraints influence metabolic fluxes and fitness in nutrient-rich environments.

The paper constructs a genome-scale metabolic model of Thalassiosira pseudonana to show how nitrogen and sulfur metabolism help balance redox states and influence organic matter secretion.

The paper evaluates parameters for genome-scale metabolic models of cultured mammalian cells, finding that homeostasis, dynamic morphology, and biomass composition are critical for accurate predictions.

The paper introduces a synthetic promoter-based azide biosensor toolkit for in vivo detection of azide anions in E. coli, offering tunable GFP expression and low-cost protein expression.

The paper introduces a probabilistic framework for analyzing metabolic networks by integrating thermodynamics and flux constraints, revealing new insights into E. coli metabolism.

The paper systematically reduces a genome-scale metabolic model of E. coli to study how model complexity affects metabolic sensitivity coefficients and shows consistent metabolic engineering conclusions across different model sizes.

The paper analyzes G6PDH isozymes in Pseudomonas putida to show how cofactor specificity and gene duplication contribute to redox balance and metabolic strategies in bacteria.

This study develops a kinetic model of E. coli metabolism to explain acetate overflow, showing acetate regulates glucose metabolism and is not a wasteful by-product.

gapseq predicts bacterial metabolic pathways and reconstructs metabolic models with high accuracy using sequence homology and pathway topology, outperforming existing tools.

This study develops a protein allocation model to predict Escherichia coli wildtype and mutant phenotypes by integrating protein distribution with metabolic fluxes.

The paper develops a computational method to identify enzyme promiscuity-induced metabolic network disruptions in engineered microbial hosts and evaluates its application in two case studies.

The paper introduces multiTFA, a Python package that improves thermodynamic-based flux analysis by incorporating multivariate treatment of thermodynamic variables, reducing uncertainty in reaction Gibbs free energy ranges.

This paper describes a new NAD+ de novo biosynthesis pathway in E. coli that starts from chorismate and demonstrates its utility in cofactor engineering for chemical production.