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.
COBRAxy is an evolving project conceived as a unifying framework for constraint-based metabolic analysis within the Galaxy ecosystem. Its long-term goal is to act as a collector and integrative hub for tools implementing COBRA-based methods, with a particular focus on the integration of omics data into genome-scale metabolic models. By leveraging Galaxy workflows, COBRAxy aims to make advanced metabolic modeling approaches accessible, reproducible, and usable by researchers without extensive programming expertise.
Beyond its current functionalities, COBRAxy is designed to be extensible and to progressively incorporate alternative constraint-based integration strategies proposed in the literature, fostering a comprehensive and modular ecosystem for COBRA analysis in Galaxy.
At present, the COBRAxy tool suite enables the comparison of metabolic flux distributions across conditions or populations, based on transcriptomic information. The tool also supports the integration of medium composition information to define nutrient availability and exchange constraints based on common commercial growth media.
Additionally, COBRAxy supports visualization of flux differences on user-defined metabolic vectorial maps.
However, transcriptomics data integration strategies are currently limited to flux capacity constraint approaches, coupled with sampling-based methods, including corner-based sampling and hit-and-run sampling.
Moreover, the 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.
Finally, there is a need to improve the computational efficiency of sampling algorithms.
This project aims to address these gaps by extending COBRAxy capabilities to support single-cell and spatial data integration, alternative integration strategies, and optimizations to statistical testing and computational efficiency.
Goal
This project builds on the foundation of COBRAxy, a Galaxy tool suite designed for metabolic network modeling, expanding its scope to:
- 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.
- Integrate spatial transcriptomics workflows: Enable mapping of metabolic activities onto physical tissue architectures and co-localization analyses.
- Improve computational efficiency: Optimize the sampling algorithm by directly interfacing with solvers like Gurobi or Glpk, bypassing intermediate steps in COBRApy to reduce runtime.
- Enhance statistical testing: Introduce advanced methods for pathway enrichment analyses, including mixed linear effect models.
- Develop visualization tools: Enable spatial overlays and interactive visualizations for flux distributions and pathway activities.
- Include alternative data integration strategies proposed in the literature
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
- 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 constraint-based metabolic modeling and/or scRNA-seq data
Usage of AI tooling
Applicants may use AI tools to support proposal writing and project development, provided their use is clearly disclosed and critically reviewed. AI tools must not replace the applicant’s own understanding: contributors are expected to fully understand, explain, and take responsibility for all submitted text and code. Proposals that rely primarily on unedited AI-generated content will be penalized or rejected.
Public Repository
The existing COBRAxy tools can be found in the following repository:
COBRAxy on Galaxy ToolShed
Potential Mentors
Chiara Damiani, chiara.damiani@unimib.it
Bruno Galuzzi, brunogiovanni.galuzzi@cnr.it
Fransco Lapi, f.lapi@campus.unimib.it
Getting started:
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.
COBRAxy is an evolving project conceived as a unifying framework for constraint-based metabolic analysis within the Galaxy ecosystem. Its long-term goal is to act as a collector and integrative hub for tools implementing COBRA-based methods, with a particular focus on the integration of omics data into genome-scale metabolic models. By leveraging Galaxy workflows, COBRAxy aims to make advanced metabolic modeling approaches accessible, reproducible, and usable by researchers without extensive programming expertise.
Beyond its current functionalities, COBRAxy is designed to be extensible and to progressively incorporate alternative constraint-based integration strategies proposed in the literature, fostering a comprehensive and modular ecosystem for COBRA analysis in Galaxy.
At present, the COBRAxy tool suite enables the comparison of metabolic flux distributions across conditions or populations, based on transcriptomic information. The tool also supports the integration of medium composition information to define nutrient availability and exchange constraints based on common commercial growth media.
Additionally, COBRAxy supports visualization of flux differences on user-defined metabolic vectorial maps.
However, transcriptomics data integration strategies are currently limited to flux capacity constraint approaches, coupled with sampling-based methods, including corner-based sampling and hit-and-run sampling.
Moreover, the 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.
Finally, there is a need to improve the computational efficiency of sampling algorithms.
This project aims to address these gaps by extending COBRAxy capabilities to support single-cell and spatial data integration, alternative integration strategies, and optimizations to statistical testing and computational efficiency.
Goal
This project builds on the foundation of COBRAxy, a Galaxy tool suite designed for metabolic network modeling, expanding its scope to:
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
Note that the project length for small projects should be 10-12 weeks.
Skills
Essential skills:
Nice to have skills:
Usage of AI tooling
Applicants may use AI tools to support proposal writing and project development, provided their use is clearly disclosed and critically reviewed. AI tools must not replace the applicant’s own understanding: contributors are expected to fully understand, explain, and take responsibility for all submitted text and code. Proposals that rely primarily on unedited AI-generated content will be penalized or rejected.
Public Repository
The existing COBRAxy tools can be found in the following repository:
COBRAxy on Galaxy ToolShed
Potential Mentors
Chiara Damiani, chiara.damiani@unimib.it
Bruno Galuzzi, brunogiovanni.galuzzi@cnr.it
Fransco Lapi, f.lapi@campus.unimib.it
Getting started: