Microbial Metabolic Engineering and Bioproduction

The paper shows that adapting bacterial strains to growth conditions improves the stability of engineered functions across generations without genomic changes.

The paper examines carbon overflow in yeast by linking proteome overabundance to respiration and energy efficiency, showing ribosome allocation determines growth rate and energy constraints trigger carbon overflow.

This paper engineers methanol assimilation pathways in yeast and identifies a latent methylotrophic metabolism, improving growth and metabolite labeling through genetic modifications.

The paper develops a method to simulate cellular metabolism using scRNA-seq data and validates it with NAD+ biosynthesis flux measurements in mouse tissues.

This study benchmarks kinetic models of Escherichia coli metabolism to evaluate their performance in predicting metabolic fluxes under genetic and environmental perturbations.

This paper describes a yeast platform that significantly increases the production of tetrahydroisoquinoline alkaloids, enabling the synthesis of natural and unnatural derivatives.

This paper demonstrates that metabolic engineering can convert the yeast Pichia pastoris from heterotrophic to autotrophic growth by rewiring its carbon assimilation pathway to resemble the Calvin-Benson-Bassham cycle.

This paper develops OptoLAC circuits to control gene expression in E. coli using blue light, improving metabolic engineering and protein production compared to IPTG induction.

KOPTIC predicts enzyme kinetics and regulation using in silico data, achieving high correlation with input flux data and identifying regulatory interactions.

This paper presents a synthetic small RNA-based system for modulating gene expression in P. putida to redesign its metabolism for industrial biocatalysis.

This paper introduces OptFill, a tool for automated gapfilling of genome-scale metabolic models that avoids thermodynamically infeasible cycles, applied to both fictional and published models.

This paper establishes the mathematical foundations for extending the Resource Balance Analysis framework to eukaryotic cells, showing it can be solved via Linear Programming.

This study introduces PROTEAN, a method to analyze metabolic capabilities of Lactobacillus strains, revealing site-specific differences in predicted metabolic production.

The paper improves a mathematical model of the E. coli folate network's response to trimethoprim and uses it to discover new drug targets.

The paper identifies potential global benefit enzymes in E. coli by analyzing the impact of KEGG reactions on theoretical product yields using an integrated model and flux balance analysis.

ChassiDex provides a database of model organisms for synthetic biology, offering tools to generate codon tables and facilitating host organism switching for researchers.

This study integrates computational modeling and experimental validation to refine a genome-scale metabolic model of S. aureus, improving its accuracy in predicting gene essentiality and metabolic capabilities.

The paper introduces PyGDS, a Python framework for in-silico genome design that enables large-scale experiments across different computational clusters.

FALCONET is an R package that automates the visualization of genome-scale metabolic models and multi-omics data using CellDesigner.

RMetD2 integrates differentially expressed genes into genome-scale metabolic models to reveal metabolic differences between conditions, outperforming existing methods in capturing experimentally observed features.

This study identifies and disrupts genes in Pseudomonas putida that cause unwanted metabolism of valerolactam, increasing its production titer.

The paper develops an FPGA-based model for Gene-Protein-Reaction Associations (GPRA) using VHDL and Quartus II, demonstrating feasibility for future genome-scale metabolic modeling.

The paper introduces a fast and simple method to identify and analyze extreme points in metabolic phenotypic phase planes, enabling the discovery of hidden metabolic potentials and reaction couplings.

The paper designs a tool for synthetic assembly of E. coli K-12 MG1655 bacteria to improve biofuel production by analyzing metabolic pathways and creating biobricks.

This paper develops NECom, a bi-level optimization framework integrating evolutionary game theory and metabolic models to predict microbial interactions with improved accuracy.

The paper introduces ETFL, a new metabolic model formulation that integrates expression, thermodynamics, and resource allocation constraints, showing improved accuracy over traditional FBA in simulating proteome-limited growth.

The paper introduces three algorithms to improve flux coupling analysis in genome-scale metabolic models, enhancing efficiency and biological interpretability.

The paper presents algorithms for genome design using whole-cell models, discovering smaller minimal genomes for Mycoplasma genitalium than previously known.

This paper introduces canonical metabolic network reductions to decrease network size while preserving metabolic pathways and feasible flux distributions.

The paper analyzes the stability of a metabolic pathway under qORAC control, showing it achieves a unique and stable steady state with maximal flux.