Bioagents Guide

BioAgent System Architecture:

┌─────────────────────────────────────────────────┐
│                  ORCHESTRATOR                     │
│  (LLM-based planning and reasoning agent)        │
├──────────┬──────────┬──────────┬────────────────┤
│ LITERATURE│ HYPOTHESIS│ EXPERIMENT│   ANALYSIS    │
│  MODULE   │  MODULE   │  MODULE   │   MODULE      │
├──────────┼──────────┼──────────┼────────────────┤
│ PubMed   │ Causal   │ Protocol │ Statistical    │
│ Semantic │ inference│ generator│ analysis       │
│ Scholar  │ Graph    │ Robot    │ Visualization  │
│ BioRxiv  │ reasoning│ interface│ Interpretation │
│ Patents  │ Novelty  │ LIMS     │ Manuscript     │
│          │ scoring  │ integration│ drafting      │
└──────────┴──────────┴──────────┴────────────────┘
         │              │              │
    ┌────┴────┐   ┌────┴────┐   ┌────┴────┐
    │ Knowledge│   │ Wet Lab  │   │ Compute │
    │ Bases    │   │ Equipment│   │ Cluster │
    └─────────┘   └─────────┘   └─────────┘

from dataclasses import dataclass
from typing import List, Optional
import json

@dataclass
class Hypothesis:
    statement: str
    mechanism: str
    evidence_for: List[str]
    evidence_against: List[str]
    novelty_score: float
    testability_score: float
    predicted_outcome: str

def generate_hypotheses(
    research_question: str,
    literature_context: List[dict],
    existing_data: Optional[dict] = None,
    n_hypotheses: int = 5,
) -> List[Hypothesis]:
    """
    Generate ranked hypotheses from literature and data context.

    This is a framework for LLM-driven hypothesis generation.
    In practice, the LLM call would go here.
    """
    prompt = f"""
    Based on the following research question and literature context,
    generate {n_hypotheses} testable hypotheses.

    Research question: {research_question}

    Literature findings:
    {json.dumps(literature_context, indent=2)}

    For each hypothesis, provide:
    1. A clear, falsifiable statement
    2. The proposed mechanism
    3. Supporting evidence from the literature
    4. Contradictory evidence
    5. Novelty score (0-1): How novel relative to existing literature
    6. Testability score (0-1): How feasible to test experimentally
    7. Predicted outcome if the hypothesis is correct
    """

    # In production: response = llm.generate(prompt)
    # Parse and return structured hypotheses
    return []  # Placeholder for LLM output parsing

def rank_hypotheses(hypotheses: List[Hypothesis]) -> List[Hypothesis]:
    """Rank hypotheses by composite score (novelty * testability)."""
    for h in hypotheses:
        h.composite_score = h.novelty_score * h.testability_score
    return sorted(hypotheses, key=lambda h: h.composite_score, reverse=True)

AI-assisted drug discovery workflow:

1. TARGET IDENTIFICATION
   - Literature mining for disease-gene associations
   - Network analysis of protein-protein interactions
   - Druggability assessment (binding site prediction)
   Tools: OpenTargets, STRING, FPocket

2. HIT IDENTIFICATION
   - Virtual screening of compound libraries
   - De novo molecular generation (SMILES, graph-based)
   - Docking and scoring (molecular dynamics)
   Tools: AutoDock-GPU, RDKit, DeepChem

3. LEAD OPTIMIZATION
   - ADMET property prediction (absorption, distribution, metabolism)
   - Toxicity prediction
   - Multi-objective optimization (potency vs. selectivity vs. ADMET)
   Tools: ADMET-AI, ToxCast, Optuna

4. PRECLINICAL VALIDATION
   - In vitro assay design and analysis
   - Animal model selection and protocol design
   - Pharmacokinetic modeling
   Tools: PK-Sim, literature-based dose prediction

# Example: Using ESM-2 embeddings for protein function prediction
# (Practical pattern for bioagent integration)

from transformers import AutoTokenizer, AutoModel
import torch

def get_protein_embeddings(sequences: list, model_name: str = "facebook/esm2_t33_650M_UR50D"):
    """
    Generate protein embeddings using ESM-2 for downstream tasks.
    Applications: function prediction, fitness landscape, design.
    """
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModel.from_pretrained(model_name)
    model.eval()

    embeddings = []
    for seq in sequences:
        inputs = tokenizer(seq, return_tensors="pt", padding=True, truncation=True, max_length=1024)
        with torch.no_grad():
            outputs = model(**inputs)
            # Use mean pooling over sequence length
            embedding = outputs.last_hidden_state.mean(dim=1).squeeze().numpy()
            embeddings.append(embedding)

    return embeddings

# Applications:
# 1. Cluster proteins by function (unsupervised)
# 2. Predict fitness effects of mutations (supervised)
# 3. Guide directed evolution experiments (active learning)
# 4. Design novel sequences (generative, conditional on embedding space)

# Automated RNA-seq analysis pipeline
# (Pattern for agent-orchestrated bioinformatics)

def automated_rnaseq_pipeline(
    fastq_dir: str,
    reference_genome: str,
    sample_sheet: str,
    output_dir: str,
) -> dict:
    """
    End-to-end RNA-seq analysis pipeline that a bioagent can orchestrate.

    Steps:
    1. Quality control (FastQC + MultiQC)
    2. Adapter trimming (Trim Galore)
    3. Alignment (STAR or HISAT2)
    4. Quantification (featureCounts or Salmon)
    5. Differential expression (DESeq2)
    6. Pathway analysis (GSEA, enrichR)
    7. Visualization and report generation
    """
    pipeline_steps = {
        "qc": f"fastqc {fastq_dir}/*.fastq.gz -o {output_dir}/qc/",
        "trim": f"trim_galore --paired {fastq_dir}/*_R1.fastq.gz {fastq_dir}/*_R2.fastq.gz -o {output_dir}/trimmed/",
        "align": f"STAR --genomeDir {reference_genome} --readFilesIn {{trimmed_R1}} {{trimmed_R2}} --outSAMtype BAM SortedByCoordinate",
        "count": f"featureCounts -a {reference_genome}/genes.gtf -o {output_dir}/counts.txt {{bam_files}}",
        "de_analysis": "Rscript run_deseq2.R --counts counts.txt --design sample_sheet.csv",
        "pathway": "Rscript run_gsea.R --de_results de_results.csv --gene_sets msigdb.gmt",
        "report": "Rmarkdown::render('analysis_report.Rmd')",
    }

    return {
        "pipeline": pipeline_steps,
        "expected_outputs": [
            "qc/multiqc_report.html",
            "de_results.csv",
            "pathway_results.csv",
            "analysis_report.html",
            "figures/volcano_plot.pdf",
            "figures/heatmap.pdf",
        ],
    }

Cloud lab integration pattern:

AGENT → API → CLOUD LAB → RESULTS → AGENT

Platforms:
- Emerald Cloud Lab: Programmatic access to wet lab equipment
- Strateos: Automated biology research platform
- Arctoris: AI-integrated drug discovery lab

API pattern:
1. Agent designs experiment protocol (JSON/YAML)
2. Protocol validated against lab capabilities
3. Experiment submitted via API
4. Real-time monitoring of experiment progress
5. Results returned as structured data
6. Agent analyzes results, designs next experiment

Active learning loop:
- Agent proposes most informative experiment (Bayesian optimization)
- Lab executes experiment
- Results update model
- Repeat until convergence or budget exhausted