Evaluating theoretical models of protein interaction network evolution without seed graphs

  • Authors:

  • Todd A. Gibson;Debra S. Goldberg

  • Affiliations:

  • Department of Computer Science, California State University, Chico;Department of Computer Science, University of Colorado at Boulder

  • Venue:
  • Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

Here we develop an alternate method to evaluate the evolutionary mechanics of theoretical network models which is free of the bias introduced by seed graph selection. We run a model in reverse directly on empirical data, and then run the model forward to generate a network topology to compare against the empirical data. We implement this method on a well-regarded gene duplication and divergence model, and find that it is unable to generate the high clustering found in the empirical data.