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 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

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

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

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)

To Do Function implementation Learning 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)

Take node, change the order of the vertices Questions?