PPI Knowledge Graph Schema

Run the PPI Knowledge Graph Schema MicroSim Fullscreen

About This MicroSim

This MicroSim displays the schema (data model) for a protein-protein interaction knowledge graph. Unlike a data-level graph that shows specific proteins, this schema shows the types of nodes and the types of relationships between them — it is a blueprint for how PPI data should be organized.

Node Types

Protein — Individual protein entities with properties (name, sequence, molecular weight)
Gene — Genes that encode proteins
Disease — Medical conditions linked to proteins
Drug — Pharmaceutical compounds that target proteins
Pathway — Biological pathways that proteins participate in
Organism — Species from which proteins originate

Relationship Types

Protein interacts_with Protein
Gene encodes Protein
Protein associated_with Disease
Drug targets Protein
Protein participates_in Pathway
Protein belongs_to Organism

How to Use

Click any node type to see its properties and example values
Read edge labels — Each relationship type is labeled with its semantic meaning
Trace paths — Follow multi-hop paths through the schema to understand what queries the graph can answer

Iframe Embed Code

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        height="560"
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Lesson Plan

Grade Level

College introductory bioinformatics

Duration

15-20 minutes

Prerequisites

Understanding of knowledge graphs and schemas
Familiarity with biological entities (proteins, genes, diseases)
Concept of data modeling

Activities

Exploration (5 min): Identify all node types and relationship types. How many node types are there? How many relationship types?
Query Design (5 min): Using this schema, write natural-language queries that the KG could answer. For example: "Which drugs target proteins associated with breast cancer?" Identify the path: Drug → targets → Protein → associated_with → Disease.
Schema Extension (5 min): What node types or relationship types are missing? Consider adding: clinical trials, publications, cellular locations. How would you connect them?
Assessment (3 min): Answer the reflection questions below.

Assessment

What is the difference between a schema-level graph and a data-level graph?
How does the "encodes" relationship between Gene and Protein reflect the central dogma?
Design a 3-hop query using this schema that could help identify drug repurposing candidates.
Why is having a well-defined schema important before populating a knowledge graph with data?