Skip to content

60ad85f700000058 Atf2 mouse

Let me now analyze this model for consistency with GO-CAM best practices and biological accuracy. I'll check several aspects of the model.

Overview of GO-CAM Model (gomodel:60ad85f700000058)

This is a model titled "Atf2 mouse" with the taxon ID NCBITaxon:9606 (human). I notice that the title mentions mouse but the taxon ID is for human, which is an inconsistency that should be addressed.

1. Model Structure and Connectivity

The model contains 63 activities with ATF2 (MGI:MGI:109349) as the central protein, along with its interactions with various gene products including: - JDP2 (MGI:MGI:1932093) - JUN (MGI:MGI:96646) - ATF7 (MGI:MGI:2443472) - CREB5 (MGI:MGI:2443973) - And many others

2. Causal Connectivity Issues

While there are some causal relationships defined using the RO:0002418 (activated by) predicate, most of the activities in the model appear disconnected from each other. This limits the model's ability to represent pathway flow, which is a key aspect of GO-CAM models.

The model has only a few causal associations, specifically: - JDP2 protein homodimerization causing JDP2 cAMP response element binding - ATF2 and JDP2 interactions through leucine zipper domain binding and heterodimerization - No clearly defined pathways showing how upstream activities lead to downstream effects

3. Function Annotations

Many activities are annotated with GO:0003674 (molecular function), which is a root term and not specific. This indicates potential incomplete annotations that could be refined with more specific functions where evidence is available.

4. Evidence and References

The model uses a variety of evidence codes and PMIDs, which is good practice. However: - Some activities lack evidence annotations altogether - The same references are often used across multiple activities without clear distinction of what specific aspects they support

5. Biological Process Context

The model represents ATF2 involvement in multiple biological processes including: - Gene expression (GO:0010467) - Cellular response to oxidative stress (GO:0034599) - Heart development (GO:0007507) - Positive regulation of transcription (GO:0045944) - And others

However, these processes are not connected into coherent pathways, making it difficult to understand the overall biological narrative.

Recommendations for Improvement

  1. Taxonomic Inconsistency: Resolve the discrepancy between the title "Atf2 mouse" and the human taxon ID (NCBITaxon:9606). The model should use mouse taxon ID if it's describing mouse proteins.

  2. Causal Connectivity: Add more causal connections between activities to create coherent pathways showing how upstream activities lead to downstream effects. Use appropriate predicates like RO:0002413 (provides input for), RO:0002629 (directly positively regulates), or RO:0002630 (directly negatively regulates).

  3. Function Specificity: Replace generic GO:0003674 (molecular function) annotations with more specific molecular functions where evidence supports it.

  4. Model Organization: Consider organizing the model into distinct sub-models representing different pathways or contexts in which ATF2 functions (e.g., separate the transcriptional regulation pathway from the stress response pathway).

  5. Activity Grouping: Group related activities to improve model readability. For example, all activities involved in transcription regulation could be visually grouped together.

  6. Evidence Documentation: Ensure all activities have appropriate evidence annotations, and when using the same reference for multiple activities, clearly document which aspects of the reference support each specific activity.

  7. Complex Representation: The model shows protein-protein interactions via protein binding annotations, but these could be better represented using complex formation activities according to the GO-CAM How to annotate complexes in GO-CAM guidelines.

Conclusion

This GO-CAM model provides a collection of ATF2-related activities and interactions, but lacks the pathway connectivity that would make it a truly valuable causal activity model. The primary recommendation is to enhance causal connections between activities to better represent biological pathway flow. Additionally, addressing the taxonomic inconsistency and improving function specificity would enhance the model's accuracy and usefulness.

Would you like me to elaborate on any specific aspect of this review? For example, I could check the documentation on specific best practices for representing transcription factors in GO-CAMs, which would be relevant for ATF2.