Christopher S. Henry

The paper updates the ModelSEED Biochemistry Database with expanded compounds and reactions, integrating multi-source thermodynamic data and grading reaction reliability.

This study shows that filtering genomes based on coherent genotype-phenotype relationships improves cross-dataset prediction of microbial carbon utilisation, outperforming larger but less coherent datasets.

The KBase Research Agent automates genome analysis workflows, producing reproducible results and annotated genomes without human intervention.

The paper maps soil microbiome functions in wetlands to understand methane emissions by analyzing microbial communities and gene expression across spatial and temporal gradients.

The paper integrates thirteen fungal metabolic models into a unified framework, improves their consistency, and uses them to analyze bioenergetic differences across species.

This study identifies and classifies effectors from the fungal pathogen Colletotrichum sublineola, mapping their roles in plant immune responses and pathogenicity.

EC-Bench provides a benchmark for evaluating EC number prediction methods, comparing existing approaches and highlighting task-specific performance variations.

The paper uses a neural-mechanistic hybrid model to simulate iron deficiency effects on plastid metabolism in Poplar and Sorghum, revealing species-specific metabolic responses.

The paper develops an ANN-based reduced-order model to accelerate FBA integration with RTMs, enabling efficient simulation of microbial metabolic switching in Shewanella oneidensis.

This study identifies pathway partitioning as a mechanism for complete denitrification in microbial communities and shows how imbalances in substrate utilization can disrupt cooperativity and lead to nitrous oxide emissions.

ModelSEED v2 improves genome-scale metabolic model reconstruction with enhanced energy metabolism and better pathway curation, leading to more accurate and comprehensive models.

The paper develops a community mutual information model to quantify information flow in microbial communities and demonstrates its application using simulated ecosystems.

This paper develops a machine learning framework using genome annotations to predict bacterial phenotypes, achieving high accuracy for Gram-stain type and respiration type.

The paper develops a molecular communication model integrating metabolic modeling and information theory to analyze how environmental perturbations affect cellular metabolism.

kb_DRAM integrates DRAM tools into KBase for microbial and viral genome annotation and metabolic modeling through a user-friendly interface.

This paper introduces CommPhitting, a dynamic model that predicts microbial community phenotype abundances and growth kinetics using experimental data and optimization.

ORT links genome-scale metabolic models with reactive transport codes to improve ecosystem models using metagenomic data.

TranSyT automates the integration of transport reactions into genome-scale metabolic models by compiling curated transporter data and providing rapid genome annotation.

AGORA2 reconstructs the human microbiome at scale, revealing individual-specific drug metabolism capabilities and correlations with clinical parameters.

The paper introduces the ModelSEED biochemistry database, which integrates metabolic annotations and supports the reconstruction and analysis of metabolic models for plants, fungi, and microbes.

Memote, open-source Python software, runs a community-maintained, partly scored test suite (consistency, biomass, annotations, optional experimental data) on SBML-encoded genome-scale metabolic models, once or through continuous integration.

Open-source KBase platform unites reference genomes and biochemistry, over 70 apps, Jupyter-based Narratives with provenance, and a Docker SDK for reproducible plant and microbial systems biology.