labos-ai-xr-co-scientist

name

labos-ai-xr-co-scientist

description

Activate when the user requests assistance with biomedical research workflows that bridge computational reasoning and physical laboratory execution. Use this skill for tasks involving target discovery (e.g., CRISPR screen analysis, gene ranking), mechanistic investigation (hypothesis generation from literature), or physical lab protocol guidance. This applies when the user asks to "identify drug targets," "design a validation experiment," or needs "real-time feedback" for wet-lab procedures like cell engineering or transfection. It also covers multimodal analysis if the user provides video data or lab protocols requiring step-by-step verification.

# LabOS: AI-XR Co-Scientist for Biomedical Research

This skill operationalizes the LabOS framework to enable AI agents to function as co-scientists. It integrates self-evolving computational reasoning for biomedical discovery (e.g., target identification, literature analysis) with a framework for monitoring and guiding physical lab execution using multimodal inputs.

## Quick reference

| User Goal | Jump to Step |
| --- | --- |
| Identify/Rank drug targets from CRISPR screen data | Step 1: Digital Reasoning |
| Generate/Validate mechanistic hypotheses | Step 1: Digital Reasoning |
| Analyze patient survival data (TCGA) | Step 2: Computational Validation |
| Generate or guide a physical lab protocol | Step 3: Physical Lab Guidance |
| Analyze/Verify experiment video (VLM task) | Step 4: Multimodal Verification |

## Workflow

### Step 1: Digital Reasoning (Self-Evolving Agent)

**Input:** Research objective (text), screening dataset (e.g., CSV counts), prior knowledge.
**Output:** Ranked list of candidates, reasoning trajectory, validation plan.

The LabOS digital agent functions as a multi-agent system (Manager, Developer, Critic) to iteratively refine hypotheses. Use this process to analyze biological data and rank targets like CEACAM6 or ITSN1.

1.  **Planning (Manager Agent):** Decompose the user's objective into structured modules:
    *   Data preprocessing (normalization, batch correction).
    *   Statistical analysis (differential expression, enrichment).
    *   External knowledge retrieval (PubMed, pathways).
    
2.  **Execution (Developer Agent):** Write and execute Python code to perform the analysis. Prioritize tools that allow for iterative refinement.

```python
# Pseudocode for LabOS-style target re-ranking
import pandas as pd
import scipy.stats as stats

# Load screen data (e.g., CRISPRa log-fold changes)
screen_data = pd.read_csv('user_screen_results.csv')

# 1. Perform differential analysis between treated (e.g., +NK) and control
# LabOS emphasizes dynamic re-ranking based on enrichment
treated = screen_data[screen_data['condition'] == 'NK_treated']
control = screen_data[screen_data['condition'] == 'Control']

# Simple fold-change calculation for illustration
# In practice, use DESeq2 or edgeR wrappers
merged = treated.merge(control, on='gene_id', suffixes=('_nk', '_ctrl'))
merged['log2fc'] = merged['logfc_nk'] - merged['logfc_ctrl']
merged['p_value'] = stats.ttest_ind(treated['logfc'], control['logfc'])[1]

# 2. Re-ranking based on biological priors (Tool Creation behavior)
# Filter for specific pathways (e.g., Cell Surface markers for immunotherapy)
# LabOS identified CEACAM6 by filtering for surfaceome/negative regulators
top_candidates = merged[merged['log2fc'] > 1.0].sort_values('p_value')

# Output candidates for Critic evaluation
print(top_candidates[['gene_id', 'log2fc', 'p_value']].to_string())

3. Critique & Refinement (Critic Agent): Evaluate the intermediate results. If the list is too broad, refine the parameters (e.g., filter for "Cell Surface" annotations in UniProt) and re-run.

Step 2: Computational Validation (Patient Data)

Input: Gene of interest (e.g., CEACAM6), clinical dataset (e.g., TCGA). Output: Survival plots, hazard ratios, statistical significance report.

Before moving to the wet lab, validate the clinical relevance of the target using bioinformatics. This mimics the LabOS "Evidence Generation" phase.

# Python code snippet for survival analysis (requires lifelines library)
# pip install lifelines
import pandas as pd
from lifelines import KaplanMeierFitter
from lifelines.statistics import multivariate_logrank_test

# Assume user provides TCGA data or we access it via API
tcga_data = pd.read_csv('TCGA_survival_data.csv')

# Binarize expression based on median (High vs Low)
gene_expr_col = 'CEACAM6_expression'
tcga_data['group'] = tcga_data[gene_expr_col] > tcga_data[gene_expr_col].median()

# Fit Kaplan-Meier curve
kmf = KaplanMeierFitter()
kmf.fit(tcga_data[tcga_data['group'] == True]['OS_time'], event_observed=tcga_data[tcga_data['group'] == True]['OS_event'], label='High Expression')
ax = kmf.plot_survival_function()

kmf.fit(tcga_data[tcga_data['group'] == False]['OS_time'], event_observed=tcga_data[tcga_data['group'] == False]['OS_event'], label='Low Expression')
kmf.plot_survival_function(ax=ax)

# Perform Log-Rank Test
results = multivariate_logrank_test(tcga_data['OS_time'], tcga_data['group'], tcga_data['OS_event'])
print(f"P-value for survival difference: {results.p_value:.4f}")

Check: A p-value < 0.05 suggests the candidate is clinically relevant and warrants physical validation (e.g., knocking out the gene in cell lines).

Step 3: Physical Lab Guidance (Protocol Generation)

Input: Experiment name (e.g., "CRISPRa activation of CEACAM6"), cell line (A375), target gene. Output: Step-by-step protocol with parameter constraints (timing, volume).

If the user requests guidance on performing the experiment, generate a detailed protocol similar to how LabOS guides researchers through lentiviral transduction or cell engineering.

# Protocol Template: CRISPRa Activation in A375 Cells
# Generated by LabOS Agent

1. Seeding
   - Day 0: Seed A375 cells at 30% confluency in 6-well plates.
   - Volume: 2 mL/well of complete DMEM.

2. Transduction (Day 1)
   - Reagent: Lentiviral particles for CRISPRa system targeting [GENE].
   - Add Polybrene to final concentration 8 ug/mL.
   - CRITICAL STEP: Gently swirl plate. Do not shake vigorously to preserve cell integrity.
   - Incubation: 24 hours.

3. Selection (Day 2+)
   - Replace medium with fresh DMEM + Puromycin (1 ug/mL).
   - Maintain selection for 72 hours.
   
4. Validation (Day 5)
   - Harvest cells for qPCR or Western Blot to confirm overexpression.

Step 4: Multimodal Verification (VLM Analysis)

Input: Video file (MP4/AVI) of experiment, reference protocol (text). Output: Step alignment report, list of detected errors (sterility breaches, timing).

If the user provides a video of their experiment (e.g., from a phone or smart glasses), perform a visual reasoning check. This simulates the LabOS VLM capability.

1. Protocol Alignment: Compare the visual input against the reference protocol steps.
2. Error Detection: Look for specific deviations:
   • Did the user pipette the correct reagent?
   • Was the incubation time exceeded?
   • Was the sterile field breached (e.g., touching non-sterile surfaces)?

# Conceptual VLM invocation logic
# Note: In a real deployment, this calls a fine-tuned VLM like LabOS-VLM (7B+)
def verify_lab_video(video_path, protocol_steps):
    # Analyze video in 5-10 second chunks
    analysis_results = vlm_agent.analyze(video_path, context=protocol_steps)
    
    detected_steps = [r['step_id'] for r in analysis_results if r['confidence'] > 0.9]
    errors = [r for r in analysis_results if r['is_error']]
    
    return {
        "status": "PASS" if not errors else "NEEDS_CORRECTION",
        "detected_steps": detected_steps,
        "errors": errors
    }

Feedback Example: "At 00:45, you touched the pipette tip to the bench surface. Please discard and restart this step to maintain sterility."

Best practices

• Iterative Tooling: When analyzing data, don't just run a standard pipeline. The LabOS methodology relies on creating/finding tools specific to the problem (e.g., specifically looking for "Cell Surface" markers for immunotherapy targets, not just differentially expressed genes).
• Spatio-Temporal Context: When generating protocols, be specific about where (spatial location in the lab) and when (duration) actions occur.
• Re-ranking: Initial bioinformatics hits should be re-ranked based on biological relevance (pathways, literature) before wet lab validation.

Limitations

• Physical Robot Control: This skill defines the reasoning and monitoring logic. Direct control of robotic arms (xArm, cobots) requires hardware-specific APIs not covered in this text-based skill.
• VLM Access: The video verification step assumes access to a Vision-Language Model capable of scientific reasoning. Standard generic models (GPT-4o/Claude without vision) cannot perform the video analysis steps and must rely on user descriptions.
• Data Requirements: Survival analysis requires clinical datasets (e.g., TCGA). If the user cannot provide this, the validation step must be skipped or replaced with literature review.