Protein Interaction Network Overview

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About This MicroSim

This MicroSim visualizes a 25-protein interaction network built from well-characterized cancer signaling proteins using a p5.js force-directed layout. Proteins are represented as nodes, with edges showing known physical or functional interactions. The force simulation positions tightly connected proteins near each other, revealing the modular structure of cancer signaling.

Proteins Included

The network spans several major cancer signaling pathways:

TP53 / DNA damage — TP53, MDM2, BRCA1, ATM, CHEK2
Cell cycle — RB1, CDK2, CCND1, E2F1
PI3K/AKT — AKT1, PTEN, PIK3CA, EGFR, ERBB2
MAPK cascade — SRC, RAF1, BRAF, MAP2K1, MAPK1
Transcription — MYC, JUN, FOS
JAK/STAT — STAT3, JAK2
Adaptor — GRB2

Visual Encoding

Node size scales with degree (number of interactions) — larger nodes are more connected
Hub nodes (high degree) are highlighted, indicating proteins central to many interactions
Edges represent known protein-protein interactions
Force-directed layout automatically clusters proteins that share many connections

How to Use

Hover over any node to see its protein name and network metrics
Observe the layout — the force simulation clusters pathway members together
Reset button — Randomize node positions and watch the force simulation re-organize the network
Identify hubs — Find the most connected proteins and consider their biological significance

Suggested Exploration

Find TP53 — it should be one of the largest nodes with 6 connections. In real biology, TP53 is the most frequently mutated gene in human cancers.
Locate GRB2 — this adaptor protein bridges receptor tyrosine kinases to downstream signaling cascades, making it a critical network connector.
Compare pathway clusters — can you visually distinguish the DNA damage pathway from the MAPK cascade based on how the force layout positions them?

Iframe Embed Code

You can add this MicroSim to any web page by adding this to your HTML:

<iframe src="https://dmccreary.github.io/bioinformatics/sims/ppi-network-overview/main.html"
        height="582"
        width="100%"
        scrolling="no"></iframe>

Lesson Plan

Grade Level

College introductory bioinformatics

Duration

15-20 minutes

Prerequisites

Understanding of proteins and their cellular functions
Basic graph theory (nodes, edges, degree)
Familiarity with cancer signaling pathways

Activities

Exploration (5 min): Let the force simulation stabilize. Identify the 3 most connected proteins (largest nodes). What do these hub proteins have in common biologically?
Pathway Identification (5 min): Try to identify visual clusters that correspond to signaling pathways. Do the TP53/DNA-damage proteins cluster together? Do the MAPK proteins form a recognizable group?
Hub Analysis (5 min): If you were designing a cancer drug, which protein(s) would you target based on their network position? Consider: targeting a hub disrupts many pathways, but may cause severe side effects.
Assessment (5 min): Answer the reflection questions below.

Assessment

What is a "hub" protein in a PPI network, and why are hubs often essential genes?
Why does the force-directed layout tend to place proteins from the same signaling pathway near each other?
GRB2 is an adaptor protein with moderate degree but connects receptor tyrosine kinases to downstream signaling. What network metric (other than degree) would capture its importance?
If you removed TP53 from this network, how many connections would be lost? What does this suggest about TP53's role in cancer?