Microbial Metabolic Engineering and Bioproduction

Genome-scale metabolic modeling reveals SARS-CoV-2-induced metabolic changes and antiviral targets

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.

Metabolic pathway analysis in the presence of biological constraints

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

Prediction of Michaelis constants from structuralfeatures using deep learning

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

Exp2Ipynb: A general machine-learning workflow for the analysis of promoter libraries

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

Architect: a tool for producing high-quality metabolic models through improved enzyme annotation

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

Dynamic metabolic network modeling of a mammalian cell cycle using time-course multi-omics data

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.

An updated genome-scale metabolic network reconstruction of Pseudomonas aeruginosa PA14 to characterize mucin-driven shifts in bacterial metabolism

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: Transcription Regulation Integrated with MEtabolic Regulation

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

Machine learning-guided acyl-ACP reductase engineering for improved in vivo fatty alcohol production

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.

Evolving small-molecule biosensors with improved performance and reprogrammed ligand specificity using OrthoRep

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

A graph neural network model to estimate cell-wise metabolic flux using single cell RNA-seq data

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.

An artificial self-assembling nanocompartment for organising metabolic pathways in yeast

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

metaGEM: reconstruction of genome scale metabolic models directly from metagenomes.

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

De novo biosynthesis of para-nitro-L-phenylalanine in Escherichia coli

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: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design

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

Dissecting the Metabolic Reprogramming of Maize Root under Nitrogen Limiting Stress Condition

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.

Standardization of inducer-activated broad host range expression modules: Debugging and refactoring an alkane-responsive AlkS/PalkB device

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

Transport engineering for improving production and secretion of valuable alkaloids in Escherichia coli

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.

Developing an enzyme selection tool supporting multiple hosts contexts

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

Combinatorial metabolic engineering platform enabling stable overproduction of lycopene from carbon dioxide by cyanobacteria

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

Metabolic Dynamics in Escherichia coli-based Cell-Free Systems

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.

Computational Design and Analysis of Modular Cells for Large Libraries of Exchangeable Product Synthesis Modules

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.

Active and machine learning-based approaches to rapidly enhance microbial chemical production

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

Development of a growth coupled dynamic regulation network balancing malonyl-CoA node to enhance (2S)-naringenin synthesis in E. 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.

A network-based approach to integrate nutrient environment in the prediction of synthetic lethality in cancer metabolism

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.

A structural property for reduction of biochemical networks

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.

Large scale function-based genome prospecting for industrial traits applied to 1,3-propanediol production.

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

Quantification of macromolecular biomass composition for constraint-based metabolic modeling

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

MODEL BASED OPTIMIZATION OF THE TERPENOIDS BIOSYNTHESIS IN E. coli

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.

A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics

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.