Scientific Hypothesis Generation

Systematically develop testable explanations and experimental designs.

When to Use

• Developing hypotheses from observations or preliminary data
• Filling out .research/project_telos.md aims section
• During the PLANNING phase
• Exploring competing explanations for phenomena
• Designing experiments to test research questions
• Generating predictions for research proposals

Workflow

1. UNDERSTAND    → Clarify the phenomenon/question
2. RESEARCH      → Survey existing literature
3. SYNTHESIZE    → Integrate evidence and identify gaps
4. GENERATE      → Develop 3-5 competing hypotheses
5. EVALUATE      → Assess hypothesis quality
6. DESIGN        → Plan experimental tests
7. PREDICT       → Formulate testable predictions

Step 1: Understand the Phenomenon

Clarifying Questions

Ask these to define the research question:

1. What is the core observation or pattern?

   • What specifically needs to be explained?

2. What is the scope?

   • What are the boundaries of the phenomenon?
   • What is included/excluded?

3. What is already known?

   • Established facts vs. assumptions
   • Previous attempts at explanation

4. What are the constraints?

   • Methodological limitations
   • Time/resource constraints
   • Ethical considerations

Example

## Phenomenon Definition

**Observation**: Treatment X reduces tumor growth in mice, but only in 
animals with intact immune systems.

**Scope**: Focus on solid tumors in murine models. Excludes metastasis 
and blood cancers.

**Known**: Treatment X has no direct cytotoxic effect on cancer cells 
in vitro.

**Question**: How does Treatment X reduce tumor growth in an 
immune-dependent manner?

Step 2: Literature Research

Before generating hypotheses, ground them in existing evidence.

Search Strategy

1. Identify key concepts from the research question
2. List synonyms and related terms
3. Search systematically (use /deep_research if needed)
4. Document findings for later reference

Integration with RA

Use /deep_research to gather literature:

/deep_research Treatment X mechanism of action cancer immunity

This creates documented summaries in .research/literature/

Step 3: Synthesize Existing Evidence

Evidence Summary Template

## Literature Synthesis

### What is Established
1. [Established fact 1 with citation]
2. [Established fact 2 with citation]

### Current Theories
1. [Theory A]: [Brief description] (Supporting: [refs], Contradicting: [refs])
2. [Theory B]: [Brief description]

### Knowledge Gaps
1. [Gap 1]: No studies have examined...
2. [Gap 2]: Conflicting results regarding...

### Relevant Mechanisms from Related Systems
1. [Analogous system 1]: [What can be learned]
2. [Analogous system 2]: [Potential parallel]

Step 4: Generate Competing Hypotheses

Develop 3-5 distinct hypotheses that could explain the phenomenon.

Hypothesis Requirements

Each hypothesis must be:

• Mechanistic: Explains how and why, not just what
• Testable: Can be evaluated empirically
• Distinguishable: Different from other hypotheses in testable ways
• Evidence-based: Grounded in existing knowledge

Strategies for Generation

Example Hypotheses

## Competing Hypotheses

### H1: T-cell Activation Hypothesis
Treatment X enhances T-cell activation through direct binding to 
checkpoint receptors, leading to increased tumor infiltration and 
cytotoxicity.

*Mechanism*: X → checkpoint binding → T-cell activation → tumor killing
*Key prediction*: T-cell depletion would abolish the effect

### H2: Dendritic Cell Priming Hypothesis
Treatment X stimulates dendritic cell maturation and antigen presentation,
leading to enhanced adaptive immune response against tumor antigens.

*Mechanism*: X → DC maturation → antigen presentation → T-cell priming
*Key prediction*: Effect would require intact antigen presentation

### H3: Tumor Microenvironment Remodeling Hypothesis
Treatment X alters the immunosuppressive tumor microenvironment by
depleting regulatory T cells or MDSCs, allowing existing immune 
responses to act.

*Mechanism*: X → Treg/MDSC depletion → reduced immunosuppression
*Key prediction*: Effect correlates with reduction in immunosuppressive cells

### H4: Innate Immune Activation Hypothesis
Treatment X activates innate immune cells (NK cells, macrophages) that
directly kill tumor cells and enhance adaptive immunity.

*Mechanism*: X → innate activation → tumor killing + cytokine release
*Key prediction*: Early innate response precedes adaptive response

Step 5: Evaluate Hypothesis Quality

Quality Criteria

Evaluation Template

## Hypothesis Evaluation

| Hypothesis | Testability | Falsifiability | Parsimony | Explanatory Power | Priority |
|------------|-------------|----------------|-----------|-------------------|----------|
| H1: T-cell | 5 | 5 | 4 | 4 | **High** |
| H2: DC | 4 | 4 | 3 | 4 | Medium |
| H3: TME | 5 | 5 | 4 | 3 | Medium |
| H4: Innate | 4 | 4 | 4 | 3 | Low |

**Strongest hypothesis**: H1 (most testable, clear predictions)
**Alternative to test**: H3 (could explain H1 results)

Step 6: Design Experimental Tests

For Each Hypothesis, Define:

1. Key experiment: The critical test
2. Controls: What comparisons are needed
3. Methods: How would you measure outcomes
4. Expected results: If hypothesis is correct
5. Alternative outcomes: What other results could mean

Experimental Design Template

## Experimental Design: Testing H1

### Critical Experiment
Deplete CD8+ T cells using anti-CD8 antibodies before Treatment X 
administration.

### Experimental Groups
1. Treatment X + isotype control antibody (n=10)
2. Treatment X + anti-CD8 antibody (n=10)
3. Vehicle + isotype control (n=10)
4. Vehicle + anti-CD8 antibody (n=10)

### Primary Outcome
Tumor volume at day 14 post-treatment

### Expected Results (if H1 correct)
- Group 1 shows reduced tumor growth
- Group 2 shows NO reduction (similar to Group 3)
- This would demonstrate T-cell dependence

### Alternative Outcomes
- If Group 2 still shows reduction → Effect is T-cell independent
  → Support for H3 or H4
- If Group 4 shows increased growth → T cells contribute to 
  baseline control → Consider immunocompetent models

Step 7: Formulate Testable Predictions

Prediction Requirements

Good predictions are:

• Specific: Clear, measurable outcomes
• Quantitative (when possible): Expected magnitude or direction
• Conditional: Specify under what conditions
• Distinguishing: Differentiate between hypotheses

Prediction Format

## Predictions

### If H1 is correct:
1. CD8+ T cell infiltration will increase >2-fold after Treatment X
2. T cell depletion will abolish >80% of tumor reduction effect
3. PD-1/PD-L1 blockade will enhance Treatment X efficacy synergistically

### If H2 is correct:
1. Dendritic cell maturation markers (CD80, CD86) will increase
2. Antigen presentation blockade will eliminate the effect
3. The effect will require 7+ days (time for adaptive response)

### Discriminating Predictions:
- H1 predicts rapid effect (days); H2 predicts delayed effect (weeks)
- H1 predicts T cell depletion is sufficient; H2 predicts DC depletion 
  is also required

Integration with RA Workflow

Output to project_telos.md

The generated hypotheses should be added to .research/project_telos.md:

### Aim 1: Determine the mechanism of Treatment X efficacy

- **Hypothesis**: Treatment X enhances T-cell activation through 
  checkpoint receptor binding, leading to increased tumor infiltration 
  and cytotoxicity. (H1 from hypothesis generation)
- **Alternative**: The effect may be mediated by tumor microenvironment 
  remodeling (H3).
- **Approach**: T-cell depletion experiments followed by immune profiling
- **Success Criteria**: Identify the critical immune cell population 
  required for Treatment X efficacy
- **Status**: Not started

Phase Gate Contribution

This skill helps complete the PLANNING phase requirements:

• Project aims are defined ← Hypothesis generation contributes here
• At least one literature search completed
• background.md has at least a rough draft

Hypothesis Generation Checklist

• Phenomenon clearly defined and bounded
• Literature searched and synthesized
• 3-5 competing hypotheses generated
• All hypotheses are mechanistic and testable
• Quality evaluation completed
• Experimental designs outlined
• Predictions formulated and distinguish between hypotheses
• Added to project_telos.md