# Cross Member Metabolic Dependency Resolution

> Use when when you have consensus metabolic reconstructions for multiple community members (e.

- Skill: `holobiomicslab/cross-member-metabolic-dependency-resolution` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/cross-member-metabolic-dependency-resolution`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/cross-member-metabolic-dependency-resolution/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: CC-BY-4.0
- Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/holobiomicslab/cross-member-metabolic-dependency-resolution

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# cross-member-metabolic-dependency-resolution

## Summary

Identify and resolve metabolic gaps in individual community members by leveraging cross-member dependencies and consensus constraints within a multi-organism metabolic model. This skill applies community-level constraints to gap-fill individual reconstructions, ensuring metabolic feasibility and interdependency consistency across all community members.

## When to use

When you have consensus metabolic reconstructions for multiple community members (e.g., from Arabidopsis thaliana microbial communities or plant-associated microbiota) and need to fill metabolic gaps by exploiting the fact that members may compensate for each other's missing reactions through exchange of metabolites or shared biosynthetic pathways.

## When NOT to use

- When metabolic reconstructions are already validated and complete with no documented gaps — gap-filling is unnecessary
- When individual member reconstructions must remain independent and isolated (gap-filling may create artificial inter-dependencies that violate experimental or modeling assumptions)
- When community composition is unknown or highly dynamic; stable, defined community membership is required to leverage cross-member constraints

## Inputs

- consensus metabolic reconstructions (SBML or JSON format) for all community members
- community composition/membership definition
- biomass equation definitions per member

## Outputs

- gap-filled community metabolic model (SBML or JSON)
- individual gap-filled member reconstructions
- gap-filling report documenting resolved reactions and justification

## How to apply

Load the consensus metabolic reconstructions for all community members in standard format (SBML or JSON). Apply COMMIT's community-dependent gap-filling algorithm, which processes each member reconstruction while enforcing community-level constraints that capture cross-member metabolic dependencies. The algorithm identifies reactions missing from individual members that can be inferred from the collective community capacity or mutual metabolite exchange requirements. Validate the resulting gap-filled models by checking reaction mass balance, confirming biomass production feasibility for each member under community growth conditions, and verifying that resolved gaps are biochemically justified by community-level constraints rather than arbitrary additions.

## Related tools

- **COMMIT** (executes community-dependent gap-filling algorithm to identify and fill metabolic gaps leveraging community-level constraints and cross-member dependencies) — https://zenodo.org/badge/latestdoi/363932874

## Evaluation signals

- All member reconstructions produce non-zero, feasible biomass after gap-filling when simulated under community growth conditions
- Gap-filled reactions pass mass balance validation (atom and charge conservation) and conform to SBML schema or JSON model specification
- Cross-member dependencies are traceable: resolved gaps correspond to metabolites that other community members can supply or biosynthetic functions that complementary members provide
- Comparison of gap-filled model metabolic scope to original consensus reconstruction shows expansion of compound/reaction coverage that is biochemically justified by community structure
- No orphan metabolites remain after gap-filling (all consumed or produced compounds have source or sink reactions)

## Limitations

- Effectiveness depends on the quality and completeness of the initial consensus reconstructions; severe gaps in all members cannot be resolved by cross-member compensation alone
- Algorithm assumes community members can exchange metabolites; results may be misleading if physical or ecological barriers prevent inter-member metabolite transfer
- Gap-filled models represent a consensus community state; may not capture temporal dynamics or member-specific growth conditions
- Validation is computational (biomass feasibility, stoichiometric consistency); biological validation via growth assays or metabolomics is recommended before relying on predictions

## Evidence

- [other] Apply COMMIT's community-dependent gap-filling algorithm to identify and fill metabolic gaps in each member reconstruction by leveraging community-level constraints and cross-member dependencies: "Apply COMMIT's community-dependent gap-filling algorithm to identify and fill metabolic gaps in each member reconstruction by leveraging community-level constraints and cross-member dependencies"
- [other] Validate the gap-filled models for consistency (reaction balancing, biomass production feasibility) and export the complete gap-filled community model in standard format (SBML or JSON): "Validate the gap-filled models for consistency (reaction balancing, biomass production feasibility) and export the complete gap-filled community model in standard format (SBML or JSON)"
- [other] COMMIT implements community-dependent gap-filling for communities sampled from Arabidopsis thaliana by processing consensus metabolic reconstructions: "COMMIT implements community-dependent gap-filling for communities sampled from Arabidopsis thaliana by processing consensus metabolic reconstructions"