Biology subjects

Aminian-Dehkordi, J.

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

6 recordsLinked to original sources

bioRxiv2026

Decision-relevant measurement for precision microbiome interventions

Human gut microbiome studies often rely on stool, despite increasing evidence that it incompletely represents the spatially heterogeneous intestinal ecosystem. Whether this loss of spatial detail matters for downstream intervention decisions is largely unresolved. We treat compositional reconstruction and decision preservation as separate objectives. Using 16 matched recovery events from 11 participants, each containing stool and four intended regional capsule measurements, we aggregated amplicon sequence variants into 11 prespecified taxonomic and functional proxy guilds. We evaluated models that reconstruct stool from regional measurements by nested leave one participant out validation under complementary compositional geometries. We then assessed decision preservation through three spatial proxy states and measurement induced decision loss (MIDL) across normalized binary intervention thresholds. Multi region CLR ridge and Aitchison models combining regional measurements with a population reference had the lowest point estimate Aitchison errors, but evidence that either outperformed the population Aitchison center was not retained after multiplicity correction. Rankings changed under total variation and after guild amalgamation; at the coarser resolution, the primary ordering showed a small, uncertain reversal that was sensitive to zero handling. The model with the lowest compositional error did not have the lowest MIDL curve calibration error. Population centers closely reproduced the nearly uninformative MIDL curves of observed stool while retaining little individualized regional information. These findings show that reconstruction, decision preservation, and calibration to observed stool decision loss answer different questions. Evaluation of microbiome measurement strategies should therefore treat compositional geometry, biological representation, and the downstream decision task as parts of the estimand.

systems biology↗

bioRxiv2026

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

Microbiome measurements are commonly judged by how faithfully they reconstruct an underlying biological profile, using taxonomic similarity, prediction accuracy, or compositional distance. Yet faithfulness and usefulness are not the same: a distorted measurement may still support the correct intervention, while a small error can be decisive when it falls near the boundary between two treatment choices. We therefore evaluate measurement quality by whether information loss changes the decisions and outcomes that remain attainable. We treat the intervention-relevant state of the gut community as a hidden quantity seen only through an imperfect, information losing measurement, mapped by a decision rule to a graded intervention. Each decision rule is evaluated by net host benefit and ecological margin, generating a feasible set of attainable outcome pairs. We define the measurement-induced decision loss (MIDL) as the maximum net host benefit lost under imperfect measurement relative to perfect observation. For scalar affine information loss, increasing degradation is a Blackwell garbling; under post-processing closure, more informative measurement weakly expands the feasible set and cannot reduce optimal host benefit. In the binary-state model, MIDL increases linearly while an observation-dependent decision rule remains optimal and plateaus once the optimal policy becomes signal-independent. We extend the framework to finite states, state-specific loss, multi-segment stool aggregation, and ecological-margin constraints. The framework distinguishes measurement faithfulness from decision usefulness and provides a principled basis for comparing stool and richer measurements by the interventions and trade-offs they support.

systems biology↗

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↗