Metabolic Flux Signatures for Pediatric Cancers Under Hypoxic Gradients

Subasree Sridhar's dataset contains results from genome-scale metabolic modeling of ten pediatric cancer cell lines under normoxic, hypoxic, and extreme hypoxic conditions. The data was generated using constraint-based modeling and machine learning classifiers to identify discriminative metabolic features and reaction-level flux patterns. The dataset was last updated on 2026-04-30 and is shared under a CC-BY-4.0 license.

Use Cases

• Training classifiers to distinguish hypoxic gradients based on metabolic pathway features mentioned in the description
• Analyzing reaction-level flux patterns for reactive oxygen and sulfur species detoxification pathways
• Identifying potential synthetic lethality or drug targets from predicted discriminative metabolic features
• Comparing metabolic rewiring across ten pediatric cancer cell lines under oxygen constraints

Strengths

• Modeling results for ten distinct pediatric cancer cell lines
• Analysis performed across three defined oxygen conditions (normoxic, hypoxic, extreme hypoxic)
• Dataset size is 1.7 MB, indicating a focused and potentially manageable analysis

Limitations

• Column-level documentation is absent; field semantics must be inferred after download
• Row count is unknown, which may limit suitability assessment
• The primary data format is PDF, which may require extraction for computational analysis

Provenance

Source

Subasree Sridhar via figshare

Collection Method

Generated using reactive-species-integrated genome-scale metabolic models and constraint-based modeling simulations.

Freshness

Last updated 2026-04-30 05:41:40