COBRAxy: spatial and single-cell metabolic modelling

[kiaradamiani]

Background

Galaxy is an open-source platform designed to make advanced bioinformatics analyses accessible and reproducible. Among its many applications, constraint-based metabolic modeling (CBM) plays a pivotal role in exploring cellular metabolism through predictive simulations of flux distributions in metabolic networks.

Current tools in the Galaxy ecosystem, such as MaREA4Galaxy, provide robust capabilities for analyzing metabolic networks based on gene expression data. However, these tools are primarily focused on bulk RNA-seq data and do not yet fully support single-cell RNA-seq (scRNA-seq) or spatial transcriptomics data. High-resolution single-cell and spatial data offer unprecedented opportunities to study metabolic heterogeneity and spatially localized metabolic activities but require significant adaptations to workflows and computational tools.

Existing Python libraries, such as COBRApy, implement key CBM techniques, including flux balance analysis (FBA) and flux variability analysis (FVA). Efforts like cobraxy have already ported some of these functionalities into Galaxy, but gaps remain—particularly in the support for single-cell FBA (scFBA) and the integration of spatial transcriptomics workflows. Moreover, there is a need to improve the computational efficiency of sampling algorithms by interfacing directly with solvers such as Gurobi or Gulp.

This project aims to address these gaps by extending Galaxy’s CBM capabilities to support single-cell and spatial data integration, along with optimizations to statistical testing and computational efficiency.

Goal

This project builds on the foundation of MaREA4Galaxy, a Galaxy tool designed for metabolic reaction enrichment analysis, expanding its scope to:

1. Support single-cell metabolic analysis: Implement models like scFBA, which integrate transcriptomics data into population-based flux models to capture metabolic heterogeneity at single-cell resolution.
2. Integrate spatial transcriptomics workflows: Enable mapping of metabolic activities onto physical tissue architectures and co-localization analyses.
3. Improve computational efficiency: Optimize the sampling algorithm by directly interfacing with solvers like Gurobi or Gulp, bypassing intermediate steps in COBRApy to reduce runtime.
4. Enhance statistical testing: Introduce advanced methods for pathway enrichment analyses, including p-value adjustments (e.g., Bonferroni, FDR) and Bayesian approaches to improve result reliability.
5. Develop visualization tools: Enable spatial overlays and interactive visualizations for flux distributions and pathway activities.

Difficulty Level: Medium

This project is categorized as medium difficulty because the integration of existing Python tools into Galaxy workflows is straightforward but requires careful adaptation to handle single-cell and spatial data effectively.

Size and Length of Project

Define the project commitment as either “small: 90 hours", "medium: 175 hours" or "large: 350 hours" and the timeline between 10 and 22 weeks, for example:

• medium: 175 hours
• 12 weeks

Note that the project length for small projects should be 10-12 weeks.

Skills

Essential skills:

• Python programming
• Constraint-based metabolic modeling (e.g., FBA, scFBA)

Nice to have skills:

• Galaxy tool development
• Experience with single-cell scRNA-seq

Public Repository

The existing COBRAxy tools can be found in the following repository:
COBRAxy on Galaxy ToolShed

Potential Mentors

Chiara Damiani, [email protected]
Bruno Galuzzi , [email protected]