# Nexus Lab

> A PhD student's second brain for computational biology -- 7 papers, 10 experiments, and the connections between them.

---

**You are**: A PhD student in computational biology

**Your situation**: You're working on drug-target prediction and single-cell RNA-Seq analysis. Your vault holds 7 foundational papers, 6 methods, 10 experiments, and 2 active projects. The challenge is tracing how ideas flow from Jumper2021's AlphaFold paper through your methods to last Friday's docking results. You need an AI lab partner that remembers every citation chain.

## Vault Map

```
nexus-lab/
├── literature/
│   ├── Barabasi2004-Network-Biology.md    # Network analysis foundations
│   ├── Haeussler2016-CRISPR-Tools.md      # Guide RNA design
│   ├── Jumper2021-AlphaFold.md            # Protein structure prediction
│   ├── Karplus2002-Molecular-Dynamics.md  # MD simulation foundations
│   ├── Libbrecht2015-ML-Genomics.md       # ML for genomics review
│   ├── Mortazavi2008-RNA-Seq.md           # RPKM normalization
│   └── Tang2009-Single-Cell-RNA-Seq.md    # First single-cell RNA-Seq
├── methods/
│   ├── AMBER Force Field.md               # MD force field (ff19SB)
│   ├── Centrality Measures.md             # Degree, betweenness, eigenvector
│   ├── Random Forests.md                  # ML classifier for drug prediction
│   ├── RPKM Normalization.md              # RNA-Seq normalization
│   ├── Single-Cell Isolation.md           # 10x Genomics, FACS protocols
│   └── Transformer Architecture.md        # AlphaFold's attention mechanism
├── experiments/
│   ├── Experiment-2024-10-28.md   # AlphaFold EGFR prediction (pLDDT 94.2)
│   ├── Experiment-2024-11-01.md   # CTCF binding site CNN (AUC 0.94)
│   ├── Experiment-2024-11-08.md   # GPCR membrane MD equilibration
│   ├── Experiment-2024-11-12.md   # Gene regulatory network (2500 genes)
│   ├── Experiment-2024-11-15.md   # scRNA-Seq mESC (4842 cells, 4 clusters)
│   ├── Experiment-2024-11-16.md   # Marker validation & trajectory
│   ├── Experiment-2024-11-18.md   # PPI network EGFR (324 proteins)
│   ├── Experiment-2024-11-19.md   # CRISPR knockout validation
│   ├── Experiment-2024-11-20.md   # ML drug-target model (AUC 0.89)
│   └── Experiment-2024-11-22.md   # EGFR docking (Compound_472: -11.2 kcal/mol)
├── projects/
│   ├── Drug-Target Prediction.md  # NIH R01, AUC 0.89 model
│   └── Single-Cell RNA-Seq Analysis.md  # NIH R21, 4 cell populations
└── daily/                         # 5 research log entries
```

## Try it now

Ask Claude:

- "How does the AlphaFold paper connect to my docking experiment?"
- "Which experiments used Centrality Measures?"
- "What were the key results from the drug-target prediction project?"
- "Summarize my November experiments"
- "What papers informed the scRNA-Seq analysis?"

## What you'll discover

- Trace citation chains from papers through methods to experiments
- Find which experiments build on each other
- See how data flows across your two active projects

---

## How it works

When you ask Claude questions or request changes, here's the flow:

> Claude's exact tool path varies between runs. These traces show representative sessions.

### Trace a citation chain (enriched search)

```
You: "How does AlphaFold connect to my docking experiment?"

  ● flywheel › search
    query: "AlphaFold docking experiment"
    → literature/Jumper2021-AlphaFold.md
        frontmatter: { type: "literature", year: 2021 }
        outlinks: Transformer Architecture, Structure-Based Drug Design, Protein Folding

        snippet: "...AlphaFold predicts protein structures with atomic accuracy using a novel
                  transformer architecture..."
      experiments/Experiment-2024-10-28.md
        frontmatter: { status: "completed", method: "AlphaFold" }
        outlinks: Jumper2021-AlphaFold, EGFR, Drug-Target Prediction
        snippet: "...pLDDT 94.2, RMSD 0.8Å vs crystal structure PDB 1M17..."
      experiments/Experiment-2024-11-22.md
        frontmatter: { status: "completed", method: "Molecular Docking" }
        outlinks: Experiment-2024-10-28, AMBER Force Field, Drug-Target Prediction
        snippet: "...Compound_472: binding energy -11.2 kcal/mol..."

  ┌─ RESULT ────────────────────────────────────────────┐
  │ Connection path (3 hops):                           │
  │                                                     │
  │ Jumper2021-AlphaFold                                │
  │   → Experiment-2024-10-28 (EGFR structure,          │
  │     pLDDT 94.2, RMSD 0.8Å)                         │
  │   → Experiment-2024-11-22 (docking screen,          │
  │     Compound_472: -11.2 kcal/mol)                   │
  │                                                     │
  │ The predicted structure was validated against        │
  │ crystal structure, then used as receptor for         │
  │ virtual screening of 10 candidates.                  │
  └─────────────────────────────────────────────────────┘

Outlinks in search results trace the chain without
graph traversal tools. Zero file reads needed.
```

### Find experiments using a method (enriched search)

```
You: "Which experiments used Centrality Measures?"

  ● flywheel › search
    query: "Centrality Measures"
    → methods/Centrality Measures.md
        outlinks: Barabasi2004-Network-Biology, Degree Centrality, Betweenness Centrality

      experiments/Experiment-2024-11-18.md
        frontmatter: { status: "completed", title: "PPI Network Analysis" }
        snippet: "...324 proteins, EGFR top hub (degree=47)..."
      experiments/Experiment-2024-11-12.md
        frontmatter: { status: "completed", title: "Gene Regulatory Network" }
        snippet: "...2500 genes, Oct4 top transcription factor (degree=247)..."

  ┌─ RESULT ────────────────────────────────────────────┐
  │ 2 experiments used Centrality Measures:             │
  │                                                     │
  │ 1. Experiment-2024-11-18 (PPI Network Analysis)     │
  │    324 proteins, EGFR top hub (degree=47)           │
  │                                                     │
  │ 2. Experiment-2024-11-12 (Gene Regulatory Network)  │
  │    2,500 genes, Oct4 top TF (degree=247)            │
  │                                                     │
  │ Source paper: Barabasi2004-Network-Biology           │
  └─────────────────────────────────────────────────────┘

Snippets contain the key results. Zero file reads.
```

### Summarize a project (search + read)

```
You: "What are the key results from drug-target prediction?"

  ● flywheel › search
    query: "drug-target prediction project"
    → projects/Drug-Target Prediction.md

  ● read_file
    path: "projects/Drug-Target Prediction.md"
    → AUC 0.89, degree centrality in top-3 features,
    → Compound_472: -11.2 kcal/mol binding energy

  ┌─ RESULT ────────────────────────────────────────────┐
  │ Drug-Target Prediction (NIH R01 CA234567)           │
  │ PI: Dr. Sarah Chen | Status: Active                 │
  │                                                     │
  │ Key results:                                        │
  │ 1. ML model AUC-ROC: 0.89 (Random Forest)          │
  │ 2. Network degree centrality is top-3 feature       │
  │ 3. CRISPR confirms hub proteins essential           │
  │ 4. Top compound: Compound_472 (-11.2 kcal/mol)     │
  │                                                     │
  │ Next: IC50 experimental validation, manuscript      │
  └─────────────────────────────────────────────────────┘
```

---

*32 notes. Just start asking questions.*

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**Token savings:** Enriched search returns outlinks (citation chains), snippets with key metrics, and method metadata — tracing connections without graph traversal tools.
