Bidirectional molecular-phenotypic integration

[cmungall]

@claude please analyze the AI transcript from the last meeting below. Make a plan. Feel free to ask clarifying questions

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What the ideal causal network should include:

Bidirectional molecular-phenotypic integration: Every molecular change (protein or gene dysfunction) should have observable phenotypic features at multiple levels (molecular, cellular, tissue, or whole organism)

Cascading causality: Phenotypic changes should then lead to new molecular changes downstream

Spatiotemporal depth: The network should capture causality going "up and down" through different biological scales

Comprehensive mechanisms: Yellow phenotypic features should be "sprinkled throughout" the network, not just at the end (see FA dismech example)

Key point: The network needs to show how variants/causes → molecular dysfunction → observable phenotypes → subsequent molecular changes → more phenotypes, creating an integrated causal chain rather than jumping from molecular dysfunction directly to disease outcomes.

See FA dismech example: https://dismech.monarchinitiative.org/pages/disorders/Fanconi_Anemia.html

[claude]

Claude finished @cmungall's task —— View job

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Analysis of Bidirectional Molecular-Phenotypic Integration

Based on my analysis of the dismech knowledge base, I can provide insights and recommendations for implementing true bidirectional molecular-phenotypic integration as described in the AI transcript.

Todo List:

• Read and understand the current dismech schema structure
• Examine the Fanconi Anemia example to understand the desired pattern
• Analyze current disorder entries to identify patterns and gaps
• Identify schema limitations for representing cascading causality
• Develop recommendations for bidirectional molecular-phenotypic integration
• Create actionable implementation plan

Current State Analysis

The dismech knowledge base already has some infrastructure for cascading causality:

Existing Strengths:

1. CausalEdge class: Sophisticated modeling with downstream relationships, causal_link_type, and intermediate_mechanisms
2. Widespread adoption: 167 of ~500 disorder files use downstream/consequence relationships
3. Fanconi Anemia exemplar: 22 downstream relationships showing molecular → phenotype → molecular cascades
4. Multi-level representation: Pathophysiology can link to biological processes, cell types, locations, and downstream targets

Current Gaps Identified:

1. Insufficient phenotype-to-molecular feedback: While pathophysiology entries have downstream targeting, phenotypes primarily use sequelae for phenotype-to-phenotype relationships, lacking strong molecular consequences

2. Limited spatiotemporal depth: Most causal chains don't systematically capture effects across biological scales (molecular → cellular → tissue → organism)

3. Phenotypes clustered at endpoints: Many disorders have molecular pathophysiology that jumps directly to end-stage clinical phenotypes, missing intermediate observable changes

4. Weak bidirectional integration: The transcript emphasizes that "phenotypic changes should lead to new molecular changes downstream" - this reverse causality is underrepresented

Fanconi Anemia Pattern Analysis

The FA example demonstrates good cascading patterns:

• DNA Repair Deficiency → Genomic Instability → Hematopoietic Stem Cell Attrition/Clonal Evolution
• Developmental Progenitor Apoptosis → multiple structural anomalies → downstream phenotypes
• Endocrine Gland Dysfunction → Growth Hormone Deficiency/Hypothyroidism → Short Stature

However, it still shows the gap: phenotypes like "Pancytopenia" don't systematically feed back into new molecular changes.

Recommendations for True Bidirectional Integration

1. Enhance Phenotype-to-Molecular Causality

• Add downstream to Phenotype class: Allow phenotypes to target new pathophysiology mechanisms
• Example: Pancytopenia → Compensatory Hematopoiesis → EPO/TPO Upregulation → New Molecular Pathways

2. Implement Spatiotemporal Layering

• Scale-aware categorization: Tag pathophysiology/phenotypes by biological level (molecular, cellular, tissue, organ, organism)
• Temporal sequencing: Add progression stages to show early → intermediate → late manifestations
• Cross-scale bridges: Ensure each level has observable consequences at multiple scales

3. Systematic Phenotype Distribution

• Audit for phenotype gaps: Identify molecular mechanisms without intermediate observable phenotypes
• Add measurable intermediates: Include biomarkers, cellular phenotypes, tissue-level changes between molecular dysfunction and clinical disease

4. Schema Enhancements Needed

# Extend Phenotype class to support downstream molecular consequences
Phenotype:
  slots:
    - downstream  # Add CausalEdge support to phenotypes
    - biological_scale  # molecular, cellular, tissue, organ, organism
    - temporal_stage   # early, intermediate, late
    - measurable_parameters  # lab values, imaging findings, etc.

# New class for cross-scale transitions
ScaleBridge:
  description: Links phenomena across biological scales
  slots:
    - source_scale
    - target_scale  
    - bridging_mechanisms

5. Implementation Action Plan

Phase 1: Schema Evolution (4-6 weeks)

• Extend Phenotype class with downstream field
• Add biological scale and temporal stage annotations
• Create validation rules for bidirectional consistency

Phase 2: Content Enhancement (8-12 weeks)

• Systematic review of high-impact disorders (start with FA, T1D, etc.)
• Add missing intermediate phenotypes and molecular feedback loops
• Implement scale-bridging mechanisms

Phase 3: Quality Assurance (2-4 weeks)

• Validate bidirectional consistency
• Ensure spatiotemporal completeness
• Generate visualization tools for causal networks

Key Success Metrics:

1. Bidirectionality: >80% of phenotypes should have downstream molecular consequences
2. Scale coverage: Each disorder should span ≥3 biological scales
3. Phenotype distribution: Intermediate observable features throughout causal chains
4. Cycle detection: Identify and model positive/negative feedback loops

Questions for Clarification

1. Priority disorders: Should we start with specific disease categories or the most well-curated entries?
2. Validation approach: How should we verify the biological accuracy of new bidirectional relationships?
3. Visualization: Do you want network visualization tools to help curators see gaps in causal chains?
4. Community input: Should this involve collaboration with domain experts for specific disorders?

This plan addresses the core requirement for "variants/causes → molecular dysfunction → observable phenotypes → subsequent molecular changes → more phenotypes" as an integrated causal chain.

[github-actions]

Summary & Analysis

This issue captures a design philosophy from a recent meeting: dismech causal networks should exhibit bidirectional molecular-phenotypic integration, where phenotypes appear as intermediate nodes throughout the causal chain—not just as terminal endpoints.

What the Ideal Network Looks Like

The Fanconi Anemia entry (https://dismech.monarchinitiative.org/pages/disorders/Fanconi_Anemia.html) is the reference exemplar. Its causal structure demonstrates "up and down" traversal across biological scales:

Gene Mutation (FANCA, FANCD2, BRCA2, etc.)
   ↓ [molecular]
Core/ID Complex Dysfunction → FANCD2 monoubiquitination failure
   ↓ [molecular → cellular]
DNA Repair Deficiency → Genomic Instability
   ↓ [cellular phenotype - intermediate]
HSC Attrition (accelerated differentiation via p53/p21/GAS6 axis)
   ↓ [cellular → tissue phenotype - intermediate]
Inflammatory BM Microenvironment (TNF-α, IFN-γ excess)
   ↓ [creates secondary molecular changes]
MYC overexpression → enhanced HSC egress → proteostasis deregulation
   ↓ [tissue → organism phenotype]
Pancytopenia / Aplastic Anemia → Leukemia risk

The yellow "phenotypic" nodes appear at every level: molecular (impaired monoubiquitination), cellular (HSC attrition), tissue (inflammatory BM niche), and organism (pancytopenia). Each feeds back into new molecular changes downstream.

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Current Schema Capabilities

The dismech schema already has the machinery for this:

• CausalEdge + downstream slot: links one mechanism to its downstream consequences
• CausalLinkTypeEnum: DIRECT / INDIRECT_KNOWN_INTERMEDIATES / INDIRECT_UNKNOWN_INTERMEDIATES
• MechanisticHypothesis: groups causal edges into named explanatory models
• consequences slot: free-text downstream effects on Pathophysiology nodes
• phenotypes at Disease level: HP-term-annotated clinical phenotypes

The gap is not schema machinery—it's curation practice and a few missing structural pieces.

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Gap Analysis

Gap	Current State	Ideal State
Biological scale annotation	Not tracked per mechanism	Each mechanism node annotated with biological_level: MOLECULAR / CELLULAR / TISSUE / ORGANISM
Intermediate phenotypes	HP phenotypes only at Disease level (end of chain)	Phenotypic nodes appear within the pathophysiology causal graph, cross-linked from mechanism to mechanism
Phenotype → Mechanism causality	No formal link from a phenotype back to a downstream molecular change	A phenotype node (e.g., "anemia") can be the upstream of a downstream molecular change (e.g., "hypoxia-induced HIF-1α activation")
Scale-crossing edges	Implicit, narrative	Explicit, structured causal edges crossing biological levels

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Plan

Phase 1: Schema Additions

1. Add biological_level enum to Pathophysiology

   BiologicalLevelEnum:
     MOLECULAR: molecular, subcellular
     CELLULAR: cell-autonomous phenotype
     TISSUE: tissue/organ level
     ORGANISM: whole-organism/clinical phenotype

   Relates to: Review and classify events in pathophysiology section #332 (event classification), Use of GO Molecular Function terms in biological_processes field - schema design options #361 (MF vs BP terms)

2. Allow phenotypes as intermediate causal nodes
   Currently phenotypes is a top-level Disease slot. Intermediate phenotypes (cellular or tissue) should also be expressible as Pathophysiology mechanism nodes (with phenotype_descriptors) so they can appear in CausalEdge.target references and themselves have downstream edges.
   Alternative: a new IntermediatePhenotype class or a flag is_intermediate: true on existing phenotype structures.

3. Cross-link phenotype nodes to downstream mechanisms
   Add a secondary_mechanisms or causes slot on PhenotypeDescriptor to allow: "this phenotype causes the following downstream molecular changes"—completing the bidirectional loop.

Phase 2: Curation Practice Guidelines

• Each disorder should have at least one phenotype at each of: MOLECULAR, CELLULAR, TISSUE, and ORGANISM scale
• Intermediate phenotypes (e.g., "HSC attrition", "inflammatory niche") should be explicit mechanism nodes with downstream edges, not only listed as Disease-level HP phenotypes
• When a phenotype triggers a downstream molecular change (feedback loop or secondary cascade), this must be captured as a new Pathophysiology node with the phenotype listed as its upstream cause

Phase 3: Audit & Backfill

• Identify disorders that jump directly from molecular dysfunction to organism-level disease outcome without intermediate phenotypic nodes
• Use FA entry as scoring template: how many biological levels have explicit phenotypic nodes?
• Prioritize high-priority disorders (those with lowest compliance scores per just compliance-weighted)

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Related Issues

Issue	Relationship
#332 - Review/classify pathophysiology events	Directly related: event classification by type and biological level
#361 - GO MF vs BP terms	Related: molecular-level mechanisms benefit from MF terms for enzymatic activities
#364 - Endogenous vs environmental factors	Related: endogenous factors can be intermediate phenotypic states
#309 - Confidence/hypothesis field	Complementary: marking hypothetical feedback loops
#306 - Entry granularity (lumping/splitting)	Complementary: fine-grained entries enable richer intermediate phenotype nodes
#164 (closed) - Split/debundle pathophysiology entries	Predecessor: the atomic causal graph goal this issue builds on
#368 - Submit to NDEx	Downstream consumer: richer bidirectional networks make NDEx queries more powerful

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Clarifying Questions

1. Scope of "phenotype" here: When a mechanism produces an intermediate observable state (e.g., "HSC attrition" in FA), should this live as a Pathophysiology node (with optional HP term annotation), or should it be a distinct PhenotypeNode class in the schema?

2. Feedback loops vs. cascades: Are cycles (phenotype → molecular change → same phenotype amplified) in scope, or only forward-directed cascades? CausalEdge currently implies a DAG structure.

3. Which disorders to pilot first? The FA entry is the exemplar. Candidates for demonstrating the new pattern from scratch might be disorders with well-characterized multi-level cascades: sickle cell disease (vasoocclusion → hypoxia → HIF → sickling amplification), type 2 diabetes (insulin resistance → hyperglycemia → glucotoxicity → β-cell failure), or chronic kidney disease (fibrosis → uremia → systemic inflammation → more fibrosis).

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Recommended Next Steps

1. Resolve the biological_level field design (quick schema PR)
2. Draft curation guidelines for intermediate phenotype nodes
3. Pilot the new pattern on 2-3 disorders beyond FA
4. Update the compliance scoring to reward intermediate phenotypic nodes across biological levels

@dragon-ai-agent please implement Phase 1 schema additions: add a biological_levelenum toPathophysiology nodes (MOLECULAR/CELLULAR/TISSUE/ORGANISM), and document curation guidelines for intermediate phenotype nodes that appear mid-cascade rather than only at terminal Disease-level phenotype lists. Use the Fanconi Anemia entry as the reference exemplar.