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Graph Data Model Design Process

Run the Graph Data Model Design Process MicroSim Fullscreen

About This MicroSim

This MicroSim shows the iterative design process for building a graph data model, visualized as a cyclic flowchart. Each step in the process is a node, with a feedback loop from the final validation step back to the beginning, emphasizing that graph data model design is never a one-pass process.

Design Steps

  1. Identify Entities — Determine what real-world objects become nodes (genes, proteins, diseases, drugs)
  2. Define Relationships — Determine what connections exist between entities and what they mean (interacts_with, treats, encodes)
  3. Assign Properties — Add key-value attributes to nodes and edges (gene name, molecular weight, confidence score)
  4. Validate Against Queries — Test the model against the questions you need to answer (Can the model answer "which drugs treat diseases linked to gene X"?)
  5. Iterate — Refine the model based on validation results, cycling back to add missing entities or relationships

How to Use

  1. Click each step to see its detailed description and examples from bioinformatics
  2. Follow the cycle — Notice that the process loops back from Iterate to Identify Entities
  3. Consider the examples — Each step includes concrete biomedical examples

Iframe Embed Code

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<iframe src="https://dmccreary.github.io/bioinformatics/sims/graph-data-model-design/main.html"
        height="520"
        width="100%"
        scrolling="no"></iframe>

Lesson Plan

Grade Level

College introductory bioinformatics

Duration

15-20 minutes

Prerequisites

  • Basic understanding of graph databases (nodes, edges, properties)
  • Familiarity with biological entities (genes, proteins, diseases)
  • Concept of data modeling

Activities

  1. Exploration (5 min): Click each step in the design cycle. What is the key question answered at each step?
  2. Hands-On Design (5 min): Imagine you need a graph to answer "Which proteins interact with BRCA1, and what diseases are they associated with?" Walk through each design step and sketch your model on paper.
  3. Discussion (5 min): Why is graph data model design iterative rather than linear? What might cause you to go back and redesign your model?
  4. Assessment (3 min): Answer the reflection questions below.

Assessment

  1. What are the five steps in the graph data model design process?
  2. Why is "Validate Against Queries" an important step before deploying a graph database?
  3. Give an example of when you would need to iterate on your graph model after initial design.
  4. How does a graph data model differ from a relational (tabular) data model for biological data?

References

  1. Graph database — Wikipedia
  2. Data modeling — Wikipedia
  3. Knowledge graph — Wikipedia