Microbial Metabolic Engineering and Bioproduction

This paper uses Bayesian inference with linear-logarithmic kinetics to efficiently estimate parameter uncertainty in metabolic models from multiomics data.

The paper demonstrates how biochemical resource allocation models can improve ecosystem simulations of microbial communities by capturing physiological acclimation and nutrient co-limitation.

The paper reconstructs and validates a genome-scale metabolic model for Lactobacillus reuteri JCM 1112T, demonstrating its accuracy in predicting metabolic behavior and identifying strategies for metabolic engineering.

The paper constructs a genome-scale metabolic model of Streptococcus mutans and validates it against experimental data, showing high accuracy in predicting nutrient requirements and gene essentiality.

The paper introduces a framework for deconvolving isotopologue profiles using moiety models and provides Python packages for optimization and model selection.

This paper describes a method to evolve and functionalize biosensors in yeast by optimizing ligand specificity and transfer functions through directed evolution.

This study optimizes Y. lipolytica for producing flavonoids and hydroxylated flavonoids by engineering metabolic pathways and enzyme modules.

This study develops a multi-objective metabolic model to identify cancer-associated metabolic vulnerabilities by analyzing Pareto surfaces and validating predictions with experimental data.

This paper introduces a data reconciliation framework to estimate thermodynamically consistent Gibbs free energies from experimental data, improving accuracy over existing methods.

This paper demonstrates the microbial biosynthesis of lactate esters from fermentable sugars using a modular E. coli system, achieving a 4.96-fold increase in ethyl lactate production.

The paper characterizes two glutarate-sensing transcription factors in Pseudomonas putida and develops biosensors to detect glutarate flux for metabolic engineering.

The paper presents rhto-GEM, the first genome-scale metabolic model of Rhodotorula toruloides, which accurately predicts growth and identifies genetic targets for improving lipid production.

The paper develops a genome-scale metabolic model of Rhodopseudomonas palustris CGA009 to analyze interactions between photosynthesis, CO2 fixation, and the quinone pool, finding that the quinone redox state regulates the CBB pathway.

The paper develops a computational tool, gcOpt, to identify growth-coupling strategies that maximize product yield by enforcing strict coupling between product synthesis and microbial growth.

The paper develops a vanillate biosensor for the vanillin pathway in E. coli, identifies improved O-methyltransferases, and demonstrates its utility in metabolic engineering.

The paper constructs a genome-scale metabolic model for Rhodosporidium toruloides IFO0880, integrating functional genomics and phenotypic data to improve predictions of gene essentiality and metabolic capabilities.

This paper introduces iSCHRUNK, a method combining Monte Carlo sampling and machine learning to reduce uncertainty in kinetic models, identifying key parameters for desired metabolic behavior.

The paper identifies strong transcriptional terminators in Synechocystis sp. PCC 6803 that improve inducible control of chromosomally integrated genetic constructs.

The paper engineers inter-species amino acid cross-feeding in bacterial consortia to increase population evenness in synthetic microbial communities.

This paper systematically evaluates genome-scale metabolic reconstruction tools to determine their quality and suitability for different applications.

The paper introduces MEMos, minimum sets of elementary flux modes, to efficiently represent all steady-state behaviors of metabolic networks without enumerating all EFMs.

This paper develops a biosensor-mediated selection strategy using competitive co-cultures in microfluidic droplets to enrich for small molecule producers.

The paper evaluates how different decisions in transcriptomic data integration affect the identification of active reactions in biochemical networks across tissues.

This paper introduces SnoCAP, a method for high-throughput screening of microbial strain libraries by amplifying production phenotypes through syntrophic co-culture, demonstrating its effectiveness in identifying efficient producers of target molecules.

The paper describes an engineered metabolic pathway in E. coli to produce nonnatural straight-chain amino acids via a recursive carbon-chain-extension cycle.

The paper quantifies biosynthetic network robustness in the human oral microbiome using a probabilistic metric to identify metabolic interdependences and uncultivated taxa.

The paper predicts new metabolites and reactions in E. coli using enzyme promiscuity and metabolomics data, enhancing the iML1515 metabolic model.

This paper introduces REMI, a method that integrates gene expression and metabolomic data into metabolic models to improve flux predictions and better reflect physiological states.

The paper introduces MCS^2, a method to compute minimal cut sets in metabolic networks without requiring elementary flux modes, showing improved efficiency over existing methods.

This study uses a genome-scale model to analyze metabolic trade-offs in Rhodopseudomonas palustris under anaerobic photoheterotrophic conditions, revealing light limitation and prioritization of carbon efficiency.