System Architecture

The Bio-DID-Seq ecosystem integrates multiple components including decentralized identifiers, AI powered processing, knowledge graphs, and secure data management to create a comprehensive platform for research data.

Overview

The system integrates several key components to provide a seamless experience for researchers working with research data:

1. Bio-DID-Seq Core: The foundation providing decentralized identifiers and GDPR compliant data management
2. BioAgents: AI powered agents for research data processing and knowledge extraction
3. Knowledge Graph: Semantic representation of biological entities and relationships
4. IPFS Storage Layer: Decentralized and content-addressed storage
5. UCAN Authorization: Capability based security model

This integration creates a powerful ecosystem that enhances data discoverability, ensures data sovereignty, and enables advanced biological insights.

Core Component Interactions

Integration Architecture

Component Responsibilities

Component	Primary Responsibility	Secondary Functions
Bio-DID-Seq Core	DID Management	Metadata Registry, UCAN Issuance
BioAgents	Knowledge Extraction	Entity Recognition, Relationship Detection
Knowledge Graph	Semantic Representation	SPARQL Queries, Data Discovery
IPFS Storage	Decentralized Data Storage	Content Addressing, Data Integrity
UCAN Service	Authorization & Access Control	Capability Delegation, Permission Management

System Integration Workflows

1. Research Paper Processing Workflow

2. Knowledge Query Workflow

3. UCAN Delegation Workflow

Technical Integration Details

1. API Integration

The components interact through well-defined APIs:

// Example API routes showing system integration
const apiRoutes = {
  // Bio-DID-Seq Core Routes
  "/api/did": "DID document management",
  "/api/auth": "Authentication & UCAN issuance",
  
  // BioAgents Integration Routes
  "/api/bioagents/process": "Process research papers",
  "/api/bioagents/query": "Natural language queries",
  
  // Knowledge Graph Routes
  "/api/kg/sparql": "SPARQL query endpoint",
  "/api/kg/search": "Semantic search interface",
  
  // Data Integration Routes
  "/api/dataverse": "Dataverse integration",
  "/api/ipfs": "IPFS storage operations"
};

2. Service Communication

Services communicate using a combination of:

• REST APIs: For synchronous operations
• Event Bus: For asynchronous workflows
• WebSockets: For real time updates
• gRPC: For high performance internal service communication

3. Data Format Standards

The system uses consistent data formats to ensure interoperability:

• JSON-LD: For semantic data representation
• RDF: For knowledge graph triples
• DIDs: W3C compliant decentralized identifiers
• JWTs: For authentication tokens
• UCANs: For authorization capabilities

Security Integration

Security is integrated across all system components:

1. Authentication Flow

2. UCAN Authorization

UCAN tokens enable capability based security that integrates with the Bio-DID-Seq ecosystem:

// Example of UCAN integration across services
async function validateCapability(ucanToken, requiredCapability, resourceId) {
  // Verify the UCAN token cryptographically
  const valid = await ucanService.verify(ucanToken);
  if (!valid) return false;
  
  // Check if the token has the required capability
  const hasCapability = await ucanService.hasCapability(
    ucanToken, 
    requiredCapability,
    resourceId
  );
  
  return hasCapability;
}

Deployment Architecture

The integrated system is deployed using a microservices architecture:

1. Containerization

All components are containerized using Docker:

# Example docker-compose.yml showing service integration
version: '3'
services:
  bio-did-seq:
    image: bio-did-seq:latest
    ports:
      - "8080:8080"
    depends_on:
      - db
      - ipfs
    environment:
      - BIOAGENTS_URL=http://bioagents:3000
      - KG_URL=http://knowledge-graph:8890
  
  bioagents:
    image: bioagents:latest
    ports:
      - "3000:3000"
    depends_on:
      - db
      - ipfs
    environment:
      - KG_URL=http://knowledge-graph:8890
      - DKG_URL=http://dkg:8900
  
  knowledge-graph:
    image: virtuoso:latest
    ports:
      - "8890:8890"
    volumes:
      - kg-data:/data
  
  ipfs:
    image: ipfs/kubo:latest
    ports:
      - "5001:5001"
    volumes:
      - ipfs-data:/data/ipfs

volumes:
  db-data:
  kg-data:
  ipfs-data:

2. Scalability

The architecture supports horizontal scaling of components:

• API Gateways: Load balanced entry points
• Service Instances: Multiple replicas for high demand services
• Database Sharding: For high volume data storage
• IPFS Cluster: Distributed storage across multiple nodes

Cross-Cutting Concerns

Several aspects integrate across all components:

1. Logging and Monitoring

Unified logging and monitoring provide visibility across the system:

// Example of integrated logging
class SystemLogger {
  static log(component, level, message, metadata) {
    // Add system-wide correlation IDs
    const enhancedMetadata = {
      ...metadata,
      component,
      timestamp: new Date().toISOString(),
      correlationId: getActiveCorrelationId()
    };
    
    // Log to centralized logging system
    logSystem.log(level, message, enhancedMetadata);
  }
}

2. Configuration Management

Centralized configuration ensures consistency across components:

// Example of integrated configuration
class ConfigService {
  static async getConfig(component, key) {
    // Get from distributed configuration store
    return configStore.get(`${component}.${key}`);
  }
  
  static async setConfig(component, key, value) {
    // Update configuration and notify services
    await configStore.set(`${component}.${key}`, value);
    await eventBus.publish('config.updated', { component, key, value });
  }
}

Future Integration Roadmap

The integration architecture supports future enhancements:

1. Federated Knowledge Graphs: Connect with external biological knowledge sources
2. Advanced AI Integration: Specialized biological AI models
3. Blockchain Anchoring: Timestamp proofs for research data provenance
4. Cross Platform DID Resolution: Broader DID method support
5. Multi Modal Data Processing: Support for images, molecular structures, etc.

Integration Benefits

This tightly integrated architecture provides several advantages:

1. Data Sovereignty: Researchers maintain control of their data
2. Enhanced Discoverability: Knowledge graphs make data more findable
3. Simplified Collaboration: UCAN delegation enables secure sharing
4. Reduced Integration Burden: Standardized APIs and formats
5. Future Proof Architecture: Extensible and modular design