Javad Aminian-Dehkordi

Identifiability of metabolic resilience from sparse longitudinal metabolomics

This study investigates the identifiability of metabolic resilience from sparse longitudinal metabolomics data using simulations and stool metabolomics from the Integrative Human Microbiome Project.

Decision-relevant measurement for precision microbiome interventions

The paper evaluates how well microbiome measurements from different intestinal regions predict intervention decisions compared to stool samples.

Measurement-Induced Decision Loss: A feasible-set theory of microbiome intervention under observation loss

Measurement-induced decision loss (MIDL), host benefit forgone versus perfect observation, grows linearly with microbiome information loss in a binary model, then plateaus once the optimal policy becomes signal-independent.

KODA: Agentic Framework for Microbiome Drug Target Discovery

KODA uses LLMs and knowledge graphs to identify antimicrobial drug targets in the gut microbiome by analyzing metabolic pathways and microbial interactions.

SIMBA-GNN: Simulation-augmented Microbiome Abundance Graph Neural Network

SIMBA-GNN predicts microbial abundances and cross-feeding relationships by integrating metabolic simulations with a graph neural network, achieving high performance.

MetaBiome: A Multiscale Model Integrating Agent-Based Modeling and Metabolic Networks Reveals Spatial Regulation in Mucosal Microbial Communities

MetaBiome integrates agent-based modeling and metabolic networks to simulate spatial regulation in gut microbial communities, revealing how microbes adapt to environmental conditions.