Microbial Metabolic Engineering and Bioproduction

The paper studies non-native cargo loading into HO shell variants and finds that activity depends on cargo amount, not shell permeability, with simpler variants showing robust assembly.

This study improves lipid production in Yarrowia lipolytica using a DBTL approach by combining metabolic model predictions with genetic and medium modifications.

The paper introduces a proteogeometric constraint that integrates cell geometry with proteome allocation in metabolic flux balance analysis.

The paper develops an ANN-based reduced-order model to accelerate FBA integration with RTMs, enabling efficient simulation of microbial metabolic switching in Shewanella oneidensis.

The paper presents a computational workflow to design enzyme selection systems (ESSs) that couple enzyme activity to microbial metabolism, creating a database of 25,505 ESS designs for Escherichia coli.

This study engineers C. glutamicum for efficient L-tryptophan production using metabolic modeling and pathway optimization, achieving 50.5 g/L in 48h.

APOLLO provides 247,092 genome-scale metabolic models of human microbes, enabling strain-level taxonomic prediction and disease-associated metabolite analysis.

This study engineers Methanosarcina acetivorans to produce bisabolene from one-carbon compounds, achieving 10.6 mg/liter, demonstrating its potential for isoprenoid bioproduction.

MetaDAG generates and visualizes metabolic networks from KEGG data, enabling analysis of organism-specific and reaction-based metabolic pathways.

This study engineered Saccharomyces cerevisiae to grow on xylose via oxidative pathways, achieving 1.5 g/L ethylene glycol production and enabling xylose utilization through both Dahms and Weimberg pathways.

This study identifies allosteric regulation of pyruvate kinase in Bacillus subtilis as critical for efficient gluconeogenesis by preventing metabolic conflicts and carbon overflow.

The paper develops a temperature-dependent enzyme-constrained metabolic model for Arabidopsis thaliana to predict growth responses and identify genes affecting thermal growth.

This paper introduces AAMN, an automated method to control microbial co-cultures by adjusting metabolic niches, showing its effectiveness in stabilizing both cooperative and competitive cultures.

This study identifies fumarate hydratase activity as a bottleneck in a genome-scale metabolic model design for bioproduction from para-coumarate, requiring optimization for growth and product formation.

GEMCAT predicts metabolic changes from transcriptomics or proteomics data and validates them with metabolomics, demonstrating its utility in human cell lines and inflammatory bowel disease patients.

CIFR enables iterative genome engineering in Gram-negative bacteria by allowing stable DNA insertions without antibiotic resistance cassettes.

This paper demonstrates the use of engineered Pseudomonas putida minicells to convert organic acids into short-chain methyl ketones through pathway optimization and whole-cell biocatalysis.

The paper develops a mathematical model showing microbial proteins adapt to maintain redox balance, improving growth by aligning biomass redox state with nutrients.

This study identifies design principles for engineering bacteria to maximize chemical production in batch cultures by optimizing gene expression and metabolic resource allocation.

The paper develops AraRoot, a genome-scale metabolic model for Arabidopsis root system, identifying key metabolic pathways and stress responses.

This study identifies oxygen's role in enabling renewable n-caprylate production from ethanol and acetate via intermediate metabolite synthesis in bioreactors.

The paper infers metabolic objectives and tradeoffs in single cells during embryogenesis using integrated omics data and a novel framework combining metabolic modeling and machine learning.

This paper develops a genome-scale metabolic model for Umbelopsis sp. WA50703 to identify genes and reactions important for polyunsaturated fatty acid production.

The paper models how growth rate affects selection for costly toxin production in microbes, finding stronger selection at higher growth rates in spatially-structured environments.

This study introduces MINN, a metabolic-informed neural network that integrates multi-omics data to predict metabolic fluxes in E. coli, outperforming pure ML and pFBA methods.

This paper demonstrates that active learning improves regulatory DNA sequence optimization for yeast promoters, outperforming one-shot methods in complex landscapes.

This study develops and validates artificial 5' regulatory sequences for enhancing thermostable protein expression in Thermus thermophilus.

This paper develops a genome-scale metabolic model for Clostridium luticellarii to improve syngas fermentation, showing its potential for producing value-added biochemicals.

This paper introduces a computational framework, AdaptUC, to predict metabolic engineering strategies for microbial adaptation to unadapted carbon sources, showing improved driving forces and reduced substrate requirements in case studies.

This paper engineers Corynebacterium glutamicum to produce glycolic acid from ethylene glycol, achieving high titers through promoter engineering and a two-stage biotransformation strategy.