YeastPathways PWY3O 4158 superpathway of NAD biosynthesis imported from Saccharomyces Genome Database
Now let me review this GO-CAM model. The model represents the superpathway of NAD biosynthesis in yeast (Saccharomyces cerevisiae). I'll assess various aspects of the model according to GO-CAM best practices and biological accuracy.
Model Overview¶
Title: "superpathway of NAD biosynthesis - imported from: Saccharomyces Genome Database"
ID: gomodel:YeastPathways_PWY3O-4158
Taxon: NCBITaxon:559292 (Saccharomyces cerevisiae S288C)
Status: production
Source: Imported from Saccharomyces Genome Database (SGD)
Quality Control Review¶
1. Completeness and Biological Accuracy¶
- The model captures the NAD biosynthesis pathway from multiple precursors including the de novo pathway (from tryptophan) and salvage pathways (from nicotinamide, nicotinic acid, and nicotinamide riboside).
- All activities are properly associated with the general biological process GO:0009435 (NAD biosynthetic process).
- All activities are correctly located in GO:0005829 (cytosol).
- Each gene product is associated with appropriate molecular functions that match their known biochemical roles.
- Input and output molecules are properly specified for each reaction with appropriate CHEBI identifiers.
2. Compliance with GO-CAM Best Practices¶
- Gene Product Representation: Each activity is enabled by a specific gene product from SGD.
- Molecular Functions: Appropriate molecular function terms are used (e.g., GO:0004516 for nicotinate phosphoribosyltransferase activity).
- Evidence: All associations have evidence codes (ECO:0000313 - imported information used in automatic assertion) with appropriate references to the source pathway.
- Provenance: Attribution is provided with contributor information (GOC:sgd_curators).
3. Model Structure and Connectivity¶
- There are several causal associations that indicate reaction order in the pathway, using RO:0002411 (causally upstream of) predicate.
- However, many expected causal connections between activities are missing. The model would benefit from additional causal links to better represent the flow of the pathway.
- Some activities appear to be duplicated (e.g., gomodel:2.7.7.1-RXN appears twice with different gene products), which may be confusing.
4. Issues Identified¶
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Missing Causal Connections: Many expected causal connections between sequential enzymatic steps are not specified.
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Duplicated Activity IDs: The same activity ID (e.g., gomodel:2.7.7.1-RXN, gomodel:ARYLFORMAMIDASE-RXN, gomodel:NICONUCADENYLYLTRAN-RXN) is used for different gene products, which is potentially confusing.
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Duplicate Output Molecules: Some activities list the same molecule twice in their outputs (e.g., CHEBI:32544 appears twice as output of gomodel:NICOTINAMID-RXN).
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Self-referential Causal Association: There is a case where an activity is causally upstream of itself (gomodel:NAD-SYNTH-GLN-RXN), which appears erroneous.
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Reaction Identifiers: The model uses reaction identifiers (e.g., RXN-8441) as activity IDs rather than more descriptive IDs, which makes the model harder to interpret without additional context.
Recommendations¶
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Add Missing Causal Connections: Complete the causal connections between sequential reactions to better represent the pathway flow.
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Fix Duplicate Activity IDs: Use unique IDs for each activity instance, even if they represent similar reactions.
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Remove Duplicate Molecules: Ensure each molecule appears only once in input/output lists.
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Fix Self-referential Causality: Remove or correct the self-referential causal association in gomodel:NAD-SYNTH-GLN-RXN.
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Better Document Reaction Steps: Consider adding comments to clarify the different branches of the NAD biosynthesis pathway.
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Check for Completeness: Verify that all known enzymatic steps in the NAD biosynthesis pathway in yeast are represented.
Conclusion¶
The GO-CAM model for the superpathway of NAD biosynthesis from SGD provides a comprehensive representation of the biochemical reactions involved in NAD biosynthesis in yeast. While the model captures the essential components (gene products, molecular functions, cellular locations, and chemical entities), it could be improved by better representing the causal flow of the pathway and addressing the issues with duplicate identifiers and molecule listings.
Overall, the model is biologically accurate and follows most GO-CAM best practices, but the connectivity and organizational issues should be addressed to improve its clarity and usefulness.