Almut Heinken

The paper integrates SIRT1 transcriptional regulation into a genome-scale metabolic model to simulate host-microbiome interactions and metabolic responses to diet.

The paper introduces the MicroMap, a curated metabolic network visualization resource for microbiome metabolism, integrating with existing tools and providing educational and comparative capabilities.

APOLLO provides 247,092 genome-scale metabolic models of human microbes, enabling strain-level taxonomic prediction and disease-associated metabolite analysis.

This study evaluates COBRA-based tools for microbial consortia modeling using FAIR criteria and experimental data, finding that newer, better-documented tools generally outperform older ones in quality and performance.

This study uses longitudinal flux balance analysis of a Crohn's disease patient's microbiome to identify time-correlated metabolic alterations and their systemic effects.

This paper develops a method to infer causal relationships between microbiome and metabolome in observational data using in silico in vivo association pattern analyses and validates it on a multi-omics dataset.

This study integrates constraint-based community modeling with fecal metabolomics to show Fusobacteria negatively impact butyrate production in gut microbiomes.

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

The paper identifies differences in bile acid pathways for cholesterol clearance in Alzheimer’s disease by analyzing transcriptomes and metabolomics data from brain samples.

The paper develops a computational approach to analyze metabolic potential in gut microbiomes of IBD patients with dysbiosis, finding distinct metabolite profiles compared to healthy controls and non-dysbiotic patients.

The paper introduces a toolbox for modeling microbial interactions and communities using metagenomic data and extends the COBRA Toolbox.

The paper introduces the Virtual Metabolic Human database, which integrates human and gut microbiome metabolism with nutrition and disease data to support biomedical research.

The paper presents two gender-specific whole-body metabolism models, Harvey and Harvetta, that integrate physiological and omics data to predict metabolic processes and interactions across organs.

The paper uses personalized microbial community models to analyze bile acid metabolism in healthy and IBD individuals, revealing distinct metabolic potentials and strain contributions.