Quantifying Pathway Identifiability Under Partial Metabolomics for Measurement Prioritization

Why It Matters

By explicitly measuring and reducing pathway uncertainty, the approach guides targeted metabolite assays, cutting costs and accelerating biological insight in metabolomics‑driven research.

Key Takeaways

• JL-FGW operator aligns condition-specific pathway graphs.
• Composite functional quantifies pathway ambiguity.
• Optimization selects next-best metabolite to reduce uncertainty.
• Framework outperforms heuristic baselines in synthetic tests.
• Runtime remains tractable for curated and genome-scale pathways.

Pulse Analysis

Partial metabolomics has become a routine constraint in systems biology, where only a fraction of the metabolome is reliably quantified. This limited coverage creates a combinatorial explosion of plausible pathway configurations, leaving researchers with ambiguous interpretations that can misguide downstream experiments. Traditional approaches—such as imputation, enrichment scoring, or generic pathway ranking—address the symptom rather than the root cause, offering little guidance on which additional metabolites would most effectively resolve uncertainty. Quantifying identifiability, therefore, is a prerequisite for rational experimental design and for extracting actionable insights from sparse data.

The authors propose a unified operator‑based framework that directly tackles identifiability under partial coverage. By employing a Johnson‑Lindenstrauss stabilized fused Gromov‑Wasserstein (JL‑FGW) operator, condition‑specific pathway graphs are aligned while preserving both topological structure and metabolite feature similarity despite heterogeneous observation patterns. A composite underdetermination functional—combining transport entropy, alignment instability, and structural risk—provides a scalar measure of pathway ambiguity. Measurement prioritization is then cast as an optimization over the sensitivity of this functional, delivering a computable estimator for the next‑best metabolite without enumerating all latent pathway completions.

Empirical evaluation demonstrates low regret compared with mechanistic and heuristic baselines on synthetic masking experiments, and the method identifies a compact, tractable set of pathways in real‑world cohorts where identifiability benchmarking is feasible. Sensitivity analyses reveal stable ranking under moderate perturbations of the composite weights, while runtime profiling confirms scalability to curated pathways and moderate genome‑scale models. By pinpointing the most informative metabolites, the framework enables targeted assay development, reduces experimental costs, and accelerates hypothesis generation in metabolic engineering, drug discovery, and precision nutrition. Open‑source code promises rapid community adoption and further methodological extensions.