Supplementary information including relevant tables and Matlab code for

“Information Flow Analysis of Interactome Networks”

Patrycja V. Missiuro1,2, Kesheng Liu1, Lihua Zou3, Brian C. Ross1, Guoyan Zhao5, Jun S. Liu4, and Hui Ge1*


1Whitehead Institute for Biomedical Research, 9 Cambridge Center, Cambridge, MA 02142

2Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139

3Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115

4Department of Statistics, Harvard University, Cambridge, MA 02138

5Department of Genetics, Washington University, St. Louis, MO 63108

*To whom correspondence should be addressed, email:


Paper as published in PLoS April 2009 -  pdf


All main text figures


Supplementary figures & text:

FigS1.doc : Correlation between degrees and loss-of-function phenotypes.

FigS2.doc : Correlation between information flow scores and loss-of-function phenotypes among proteins of low or medium degrees.

FigS3.eps : Kirchhoffs current law: the basis for calculating information flow scores.

TextS1.doc : Toy networks used to illustrate the differences between information flow and betweenness.

TextS2.doc : Analysis of module size versus GO enrichment to determine appropriate size thresholds for module extraction algorithm.



Links to directories:



Contact: Hui Ge <> or Patrycja Missiuro <> if you have questions.