Biology subjects

Aminian-Dehkordi, J.

Publications and source records attributed to Aminian-Dehkordi, J..

4 recordsLinked to original sources

bioRxiv2026

Identifiability of metabolic resilience from sparse longitudinal metabolomics

Sparse, irregular longitudinal metabolomic sampling fundamentally constrains which dynamical properties of gut metabolism can be robustly inferred from observational data. We develop an effective landscape inference framework to characterize these identifiability limits while quantifying aspects of metabolic resilience that remain recoverable under realistic sampling regimes. Using stochastic simulations with known ground truth, we first characterize the identifiability limits of multistability detection under sparse sampling, showing that bistable dynamics can appear monostable at sampling densities typical of existing human cohorts. Guided by these identifiability limits, we illustrate the framework using a small subset (N = 4) of longitudinal stool metabolomic trajectories meeting stringent quality-control criteria. Within this sparse-sampling regime, landscape curvature, a bootstrap-quantified measure of local recovery strength, remains identifiable and provides preliminary evidence of inter-individual variability in inferred recovery dynamics. An autoregressive extension prioritizes bile acids and fermentation intermediates as candidate modulators of butyrate return dynamics.

microbiology↗

bioRxiv2025

KODA: Agentic Framework for Microbiome Drug Target Discovery

The gut microbiome plays a crucial role in human health and disease, influencing diverse biological processes such as immune regulation and nutrient metabolism. However, the complexity of micro-bial interactions and their metabolic cross-feeding dynamics remains poorly understood. This study proposes KODA, an agentic framework that integrates large language models (LLMs) and knowledge graphs (KGs) to facilitate the discovery of targets in antimicrobial drugs in the gut microbiome. Our approach employs a multi-agent system to interpret natural language queries and translate them into precise graph database queries, enabling intuitive interactions with complex microbiome data. Focusing on KEGG orthologies related to essential microbial genes, KODA identifies potential antimicrobial drug targets by analyzing microbial metabolic pathways. The system employs a Neo4j-based microbiome KG, which integrates microbial interaction data, metabolic models, and KEGG annotations. A dedicated evaluation framework, which incorporates LLM-based reviewers, assesses the quality of generated queries and analytical reports. Our results demonstrate the efficacy of KODA in providing actionable insights for antimicrobial research, particularly in identifying conserved essential genes as potential drug targets. This framework holds the potential to democratize microbiome research by lowering technical barriers and accelerating hypothesis generation in drug discovery.

bioinformatics↗

bioRxiv2025

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

Understanding gut microbiome dynamics gut requires deciphering complex, metabolically driven interactions beyond taxonomic profiles. We present SIMBA, a novel framework that integrates mechanistic metabolic simulations with a graph neural network (GNN) to predict microbial abundances and uncover cross-feeding relationships. By simulating pairwise interactions among gut microbes using metabolic networks, we generate biologically grounded graphs that capture metabolite cross-feeding and functional relationships. Our custom GNN, enhanced with edge-aware attention, is trained through a multi-stage pipeline combining self-supervised learning, simulation-based pretraining, and fine-tuning on real microbial abundance data. SIMBA achieves state-of-the-art performance (Spearman {rho} = 0.85) and enables interpretable insights into keystone taxa and metabolic bottlenecks. This work demonstrates the power of combining metabolic networks with deep learning for precision microbiome analysis.

systems biology↗

bioRxiv2024

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

Mucosal microbial communities (MMCs) are complex ecosystems near the mucosal layers of the gut, essential for maintaining health and modulating disease states. Despite advances in high-throughput omics technologies, current methodologies struggle to capture the dynamic metabolic interactions and spatiotemporal variations within MMCs. In this work, we present MetaBiome, a multiscale model integrating agent-based modeling (ABM), finite volume methods, and constraint-based models to explore the metabolic interactions within these communities. Integrating ABM allows for the detailed representation of individual microbial agents, each governed by rules that dictate cell growth, division, and interactions with their surroundings. Through a layered approach--encompassing environmental conditions, agent information, and metabolic pathways--we simulated different communities to showcase the potential of the model. Using our in-silico platform, we explored the dynamics and spatiotemporal patterns of MMCs in the proximal small intestine and the cecum, simulating the physiological conditions of the two gut regions. Our findings revealed how specific microbes adapt their metabolic processes based on substrate availability and local environmental conditions, shedding light on spatial metabolite regulation and informing targeted therapies for localized gut diseases. MetaBiome provides a detailed representation of microbial agents and their interactions, surpassing the limitations of traditional grid-based systems. This work marks a significant advancement in microbial ecology as it offers new insights into predicting and analyzing microbial communities. ImportanceOur study presents a novel multiscale model that combines agent-based modeling, finite volume methods, and genome-scale metabolic models to simulate the complex dynamics of mucosal microbial communities in the gut. This integrated approach allows us to capture spatial and temporal variations in microbial interactions and metabolism that are difficult to study experimentally. Key findings from our model include: O_LIPrediction of metabolic cross-feeding and spatial organization in multi-species communities C_LIO_LIInsights into how oxygen gradients and nutrient availability shape community composition in different gut regions C_LIO_LIIdentification of spatially-regulated metabolic pathways and enzymes in E. coli C_LI We believe this work represents a significant advance in computational modeling of microbial communities and provides new insights into the spatial regulation of gut microbiome metabolism. The multiscale modeling approach we have developed could be broadly applicable for studying other complex microbial ecosystems.

microbiology↗