How Scale-Free Are Biological Networks
- 1 April 2006
- journal article
- research article
- Published by Mary Ann Liebert Inc in Journal of Computational Biology
- Vol. 13 (3), 810-818
- https://doi.org/10.1089/cmb.2006.13.810
Abstract
The concept of scale-free network has emerged as a powerful unifying paradigm in the study of complex systems in biology and in physical and social studies. Metabolic, protein, and gene interaction networks have been reported to exhibit scale-free behavior based on the analysis of the distribution of the number of connections of the network nodes. Here we study 10 published datasets of various biological interactions and perform goodness-of-fit tests to determine whether the given data is drawn from the power-law distribution. Our analysis did not identify a single interaction network that has a nonzero probability of being drawn from the power-law distribution.Keywords
This publication has 39 references indexed in Scilit:
- Scale-free networks versus evolutionary driftComputational Biology and Chemistry, 2004
- Network biology: understanding the cell's functional organizationNature Reviews Genetics, 2004
- A Map of the Interactome Network of the Metazoan C. elegansScience, 2004
- Back to the biology in systems biology: What can we learn from biomolecular networks?Briefings in Functional Genomics and Proteomics, 2004
- Similarities and Differences in Genome-Wide Expression Data of Six OrganismsPLoS Biology, 2003
- Biological networksCurrent Opinion in Structural Biology, 2003
- Transcriptional Regulatory Networks in Saccharomyces cerevisiaeScience, 2002
- Wrestling with pleiotropy: Genomic and topological analysis of the yeast gene expression networkBioEssays, 2002
- A comprehensive two-hybrid analysis to explore the yeast protein interactomeProceedings of the National Academy of Sciences of the United States of America, 2001
- Stretched exponential distributions in nature and economy: “fat tails” with characteristic scalesZeitschrift für Physik B Condensed Matter, 1998