Microbial Metabolic Engineering and Bioproduction

The paper analyzes metabolic pathways under biological constraints and shows how to efficiently compute elementary fluxes with thermodynamic and kinetic constraints.

The paper develops a deep learning model to predict Michaelis constants for enzyme-substrate combinations using structural features.

The paper introduces a machine-learning workflow for analyzing promoter libraries to identify sequence design principles that predict gene expression activity.

Architect improves metabolic model quality by combining enzyme annotations from multiple tools and gap-filling based on likelihood scores.

The paper uses dynamic metabolic modeling to analyze time-course multi-omics data from murine pro-B cells, revealing key metabolic changes during the cell cycle.

The paper updates a genome-scale metabolic network for Pseudomonas aeruginosa PA14 and integrates it with transcriptomic data to reveal metabolic shifts in response to mucin exposure.

TRIMER integrates transcriptional and metabolic regulation to predict metabolic fluxes and gene states, outperforming existing methods on simulated and experimental data.

This study uses machine learning to engineer acyl-ACP reductases for improved in vivo fatty alcohol production, achieving over twofold higher titers than natural enzymes.

This study uses OrthoRep to evolve biosensors with improved dynamic range and reprogrammed ligand specificity for metabolic pathway engineering.

This paper introduces scFEA, a graph neural network model to estimate cell-wise metabolic flux from scRNA-seq data, validated with matched metabolomics data and applied to multiple datasets.

The paper demonstrates an artificial nanocompartment in yeast that enhances metabolic pathway efficiency by stabilizing enzymes and reducing toxicity.

metaGEM reconstructs genome-scale metabolic models directly from metagenomes, demonstrating comparable metabolic representation to reference-based models and capturing intraspecies diversity in gut microbiomes.

This paper designs a de novo biosynthetic pathway in E. coli to produce para-nitro-L-phenylalanine (pN-Phe) from glucose, achieving a maximum titer of 819 µM.

dGPredictor improves thermodynamic analysis of metabolic reactions by incorporating stereochemistry and enables accurate prediction of free energy changes for novel reactions and pathway design.

This study develops a genome-scale metabolic model for maize root to analyze metabolic reprogramming under nitrogen stress, identifying key metabolites involved in biomass growth and stress tolerance.

The paper standardizes an inducible expression system for bacterial hosts by refactoring the AlkS/PalkB module to improve predictability and interoperability.

This study uses transport engineering to enhance reticuline production and secretion in E. coli by introducing the AtDTX1 transporter, achieving an 11-fold increase in production.

The paper updates Selenzyme, an enzyme selection tool, to support multiple host contexts for better affinity comparison and optimal host selection.

The paper demonstrates a combinatorial metabolic engineering approach in cyanobacteria that achieves stable overproduction of lycopene from CO2 without iterative optimization.

This study investigates metabolic dynamics in Escherichia coli-based cell-free systems using metabolomics, finding that lysate preparation significantly affects protein yield and metabolic profiles, while endogenous metabolism is resilient to enzymatic perturbations.

This study introduces ModCell-HPC, a computational method for designing modular cells capable of producing large libraries of chemicals, demonstrating its effectiveness in generating E. coli strains for 161 endogenous modules.

This work introduces ActiveOpt, a machine learning approach that reduces the number of experiments needed to optimize microbial chemical production in Escherichia coli.

The paper develops a dynamic regulation network in E. coli to balance malonyl-CoA for enhanced (2S)-naringenin synthesis, achieving an 8.4-fold production improvement with balanced growth.

This paper introduces a network-based method to predict synthetic lethality in cancer metabolism by integrating genetic interactions and nutrient environments, identifying potential vulnerabilities in tumor cells.

This paper introduces a model reduction approach for biochemical networks that preserves steady-state fluxes and reduces metabolite counts by up to 99% in Escherichia coli models.

The paper identifies 187 new candidate species for 1,3-propanediol production using a function-based genome mining approach.

The paper develops a pipeline for experimental biomass quantification in E. coli, improving coverage and resolution, and evaluates its impact on metabolic model predictions.

The paper optimizes terpenoids biosynthesis in E. coli by modifying enzyme kcat values and combining them with other strategies to achieve a 21.22 fold increase in biosynthetic flux.

This paper develops yETFL, an extended ME-model for Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics, demonstrating its predictive ability for growth rate, essential genes, and overflow metabolism.

The paper presents a stochastic gene expression model of cell growth that explains the growth advantage of metabolic exchange and auxotrophy in microbial communities.