GALLANT: A Scalable Computational Platform for Microbial Consortium Interaction and Population Heterogeneity Analysis
Metabolic modeling enables the prediction of functional capabilities in organisms and microbial communities from genomic data. However, current workflows for genome-scale metabolic model (GEM) reconstruction and contextualization remain time-consuming and technically demanding, particularly when integrating multi-omics data or deriving community-level models from taxonomic profiles. Although recent advances have improved automation and omics integration, challenges persist in incorporating heterogeneous datasets such as single-cell RNA sequencing and in interpreting microbiomes from 16S rRNA data. We present a computational tool for rapid, automated GEM generation with integrated support for contextualization using transcriptomic and single-cell omics data. The platform also enables the construction of core consortium metabolic models from 16S rRNA profiles, facilitating systems-level interpretation of both single-organism and community-scale datasets. This streamlined pipeline offers a scalable solution for microbiome research, including population heterogeneity analysis and metabolic engineering. Its utility has been demonstrated by exploring phenotypic heterogeneity within Bacillus subtilis populations and identifying metabolic interactions among members of a cyanobacteria-enriched microbial consortium. SummaryMetabolic modeling has become a cornerstone in systems biology, enabling researchers to simulate and analyze the metabolic capabilities of individual organisms and complex microbial communities. Genome-scale metabolic models (GEMs) have been widely used to explore metabolic phenotypes, guide metabolic engineering, and interpret omics data. Despite the increasing availability of genomic and metagenomic data, the generation and contextualization of metabolic models remains a time-consuming and technically demanding process, often requiring manual curation and domain-specific expertise. At the community level, modeling microbial consortia is gaining momentum, particularly with the rise of microbiome research. Tools like MICOM (Diener et al., 2020) and CarveMe (Machado et al., 2018) have advanced the ability to create metabolic models for entire communities, yet there remains a significant gap when it comes to systematically deriving community-level models from marker-gene data such as 16S rRNA sequencing. While 16S data provides a cost-effective snapshot of microbial composition, translating this into functional, mechanistic models of community metabolism is still an emerging area, typically involving multiple steps of taxonomic inference, genome mapping, and model aggregation. Recent advances in automation and algorithmic approaches have facilitated the reconstruction of draft models (Tarzi et al., 2024), yet the field continues to face challenges in the accurate contextualization of these models with experimental data. Multi-omics integration, including transcriptomics, proteomics, metabolomics, and especially single-cell RNA sequencing, has the potential to significantly improve the predictive power of metabolic models by providing condition-specific and cell-type-specific constraints. However, integrating these heterogeneous data sources remains a complex task, often limited by compatibility issues, lack of standardized pipelines, and computational overhead.