Generating Biological Network Motifs

Sai Badey

Biological Networks What are they?

Biological vs Regular Networks*

Terminology Motifs Vertices Edges

Overall Scope Focus on efficiency

Large networks Network generation

Project Aim Random Network Generation

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

Potential Results No difference

Compare efficiencies of generation methods Create a standard for network generation

Significant Difference Determine which is more accurate

Steps Amala Ghandi’s work

Looks through existing network Determines motif

Expansion Determine motif across several networks Compare different networks & performance Network Generation

Current Status Class Design

Network 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)

To Do Function implementation

Combine with Amala Ghandi’s work

Learning Vectors Hash tables

Issues Lots of research

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)

Questions?

Take node, change the order of the vertices