1. Generating Biological Network Motifs
Sai Badey

2. Biological Networks What are they? Biological vs Regular Networks*
Terminology
Motifs
Vertices
Edges

4. Overall Scope
Focus on efficiency
Large networks
Network generation

6. Project Aim Random Network Generation Analysis & Comparisons
functions (keep # vertices & # edges constant)
Swap the edges between outer-vertices
Completely random generation
Swap node degrees
Analysis & Comparisons
Run z-testing on the generations for small to extremely large graphs

7. Potential Results No difference Significant Difference
Compare efficiencies of generation methods
Create a standard for network generation
Significant Difference
Determine which is more accurate

8. Steps Amala Ghandi’s work Expansion Looks through existing network
Determines motif
Expansion
Determine motif across several networks
Compare different networks & performance
Network Generation

9. Current Status Class Design Network Class Compare Networks Class
Jung Library
Constructor, Copy constructor
Network generation
Analysis (z-test, motif searching, data collection)
Compare Networks Class
Equals
Comparison (highest degree node, motif comparison)
Analysis (significant difference, etc)

10. To Do Function implementation Learning
Combine with Amala Ghandi’s work
Learning
Vectors
Hash tables

11. Issues
Lots of research

12. Sources Amala Ghandi’s paper
Sahand Khakabimamaghani, Iman Sharafuddin, Norbert Dichter, Ina Koch,  Ali Masoudi-Nejad
QuateXelero: An Accelerated Exact Network Motif Detection Algorithm (Article)
Joseph Blitzstein and Persi Diaconis
A SEQUENTIAL IMPORTANCE SAMPLING ALGORITHM FOR GENERATING RANDOM GRAPHS WITH PRESCRIBED DEGREES (Article)
Bjorn H. Junker & Falk Schreiber
Analysis of Biological Networks (Book)

13. Take node, change the order of the vertices
Questions?