Microbial Metabolic Engineering and Bioproduction

This study uses whole-body metabolic models to show that isoleucine availability limits SARS-CoV-2 virus shedding and identifies potential therapeutic strategies.

This study shows that yeast can adapt rapidly to non-native substrates with minimal metabolic engineering by integrating non-native genes with a native regulon system.

This paper predicts gene essentiality using machine learning on wild-type flux distributions, achieving near state-of-the-art accuracy for Escherichia coli.

This study identifies alternative glycolytic pathways in E. coli that bypass canonical EMP glycolysis, showing that the serine shunt is selected for in adaptive evolution.

The paper presents a framework for engineering microbial biosynthesis of butyryl-CoA-derived esters with controllable selectivity, achieving significant production increases in target compounds.

This paper uses multiobjective optimization to analyze trade-offs in biosensor design for dynamic pathway engineering, showing improved production in E. coli.

This study uses isotopically nonstationary 13C metabolic flux analysis to measure carbon fluxes in resting and thrombin-activated human platelets, revealing metabolic shifts during activation.

The paper addresses false positive enrichment in biosensor-guided biocatalyst engineering by desensitizing biosensors and using orthogonal pre-screens to reduce cheater cells during FACS.

The paper investigates the non-random organization of flux control mechanisms in yeast metabolic pathways, identifying distinct regulatory strategies across different pathways.

The paper reports CRISPR-Cas9 editing of industrial Saccharomyces cerevisiae strains for xylitol production from sugarcane bagasse hydrolysate, showing improved performance over a lab strain.

This paper demonstrates a hybrid cellular-heterogeneous catalytic strategy to produce olefins from glucose using engineered microbial strains and catalysts.

The paper reconfirms the degradation of 2-ketobutyrate to propionyl-CoA, 1-propanol, and propionate in E. coli and re-introduces endogenous pathways for producing odd-chain compounds.

The paper resequences and analyzes transcriptomic changes in an engineered C. glutamicum strain under bioreactor conditions with and without ionic liquid stress.

The paper introduces a modular cloning system for genetic engineering in Komagataeibacter bacteria to enable multigene construct assembly and programmed protein secretion.

COBREXA.jl is a Julia package for scalable constraint-based metabolic modeling, designed for high-performance computing environments and multi-organism community models.

DeltaFBA predicts metabolic flux changes between conditions using genome-scale models and gene expression data without requiring a specified cellular objective.

GPRuler automatically reconstructs gene-protein-reaction rules for metabolic networks using biological databases, achieving high accuracy in both small-scale and genome-scale models.

PccGEO designs functional genetic elements using a knowledge-constrained deep learning model, achieving high success rates in promoter design for E. coli.

This paper describes a platform for de novo synthesis of nepetalactone in yeast, achieving 3.10 mg/L/OD600 from simple sugars by optimizing pathway genes and deleting competing enzymes.

The paper constructs a synthetic RNA polymerase operon in E. coli, showing that adaptive evolution can restore growth after deleting native genes.

The paper introduces a dynamic community FBA model that simulates microbial community dynamics without requiring cooperation for a global objective, and applies it to E. coli mutants and a gut microbiome.

This paper introduces a dynamic flux balance model to simulate extracellular enzyme secretion and activity in microbial communities, finding optimal secretion rates that maximize growth or biomass yield based on environmental conditions.

This paper demonstrates that dynamic deregulation of central metabolism in E. coli improves process robustness and scalability for industrial chemical production.

The paper introduces a community-level gap-filling algorithm to resolve metabolic gaps and predict interactions in microbial communities.

This paper introduces elementary growth modes and vectors for next-generation metabolic models, providing an unbiased understanding of cellular growth and resource allocation without relying on fixed biomass composition.

This paper introduces SSA-FBA, a computational framework for simulating single-cell metabolism using genome-scale models and gene expression data, demonstrating its application to Mycoplasma pneumoniae.

The paper introduces a method to estimate in-vivo metabolic kinetic constants and metabolite concentrations from omics data by solving a convex optimization problem.

This paper quantifies how uncertainty in biomass coefficients and non-steady-state conditions affect flux balance analysis results.

The paper uses genome-scale metabolic modeling to identify SARS-CoV-2-induced metabolic changes and predict antiviral targets validated through drug and genetic screens.