skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: LARGE-SCALE TOPOLOGICAL PROPERTIES OF MOLECULAR NETWORKS.

Abstract

Bio-molecular networks lack the top-down design. Instead, selective forces of biological evolution shape them from raw material provided by random events such as gene duplications and single gene mutations. As a result individual connections in these networks are characterized by a large degree of randomness. One may wonder which connectivity patterns are indeed random, while which arose due to the network growth, evolution, and/or its fundamental design principles and limitations? Here we introduce a general method allowing one to construct a random null-model version of a given network while preserving the desired set of its low-level topological features, such as, e.g., the number of neighbors of individual nodes, the average level of modularity, preferential connections between particular groups of nodes, etc. Such a null-model network can then be used to detect and quantify the non-random topological patterns present in large networks. In particular, we measured correlations between degrees of interacting nodes in protein interaction and regulatory networks in yeast. It was found that in both these networks, links between highly connected proteins are systematically suppressed. This effect decreases the likelihood of cross-talk between different functional modules of the cell, and increases the overall robustness of a network by localizing effectsmore » of deleterious perturbations. It also teaches us about the overall computational architecture of such networks and points at the origin of large differences in the number of neighbors of individual nodes.« less

Authors:
Publication Date:
Research Org.:
BROOKHAVEN NATIONAL LABORATORY (US)
Sponsoring Org.:
DOE/OFFICE OF SCIENCE (US)
OSTI Identifier:
15006719
Report Number(s):
BNL-71827-2003-BC
R&D Project: PO-15; KC-02-02-03; TRN: US200411%%408
DOE Contract Number:  
AC02-98CH10886
Resource Type:
Book
Resource Relation:
Other Information: PBD: 17 Nov 2003; Related Information: POWER LAWS, SCALE-FREE NETWORKS AND GENOME BIOLOGY
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY; MOLECULES; TOPOLOGICAL MAPPING; PHYSICAL PROPERTIES

Citation Formats

MASLOV,S.SNEPPEN,K. LARGE-SCALE TOPOLOGICAL PROPERTIES OF MOLECULAR NETWORKS.. United States: N. p., 2003. Web.
MASLOV,S.SNEPPEN,K. LARGE-SCALE TOPOLOGICAL PROPERTIES OF MOLECULAR NETWORKS.. United States.
MASLOV,S.SNEPPEN,K. 2003. "LARGE-SCALE TOPOLOGICAL PROPERTIES OF MOLECULAR NETWORKS.". United States. https://www.osti.gov/servlets/purl/15006719.
@article{osti_15006719,
title = {LARGE-SCALE TOPOLOGICAL PROPERTIES OF MOLECULAR NETWORKS.},
author = {MASLOV,S.SNEPPEN,K.},
abstractNote = {Bio-molecular networks lack the top-down design. Instead, selective forces of biological evolution shape them from raw material provided by random events such as gene duplications and single gene mutations. As a result individual connections in these networks are characterized by a large degree of randomness. One may wonder which connectivity patterns are indeed random, while which arose due to the network growth, evolution, and/or its fundamental design principles and limitations? Here we introduce a general method allowing one to construct a random null-model version of a given network while preserving the desired set of its low-level topological features, such as, e.g., the number of neighbors of individual nodes, the average level of modularity, preferential connections between particular groups of nodes, etc. Such a null-model network can then be used to detect and quantify the non-random topological patterns present in large networks. In particular, we measured correlations between degrees of interacting nodes in protein interaction and regulatory networks in yeast. It was found that in both these networks, links between highly connected proteins are systematically suppressed. This effect decreases the likelihood of cross-talk between different functional modules of the cell, and increases the overall robustness of a network by localizing effects of deleterious perturbations. It also teaches us about the overall computational architecture of such networks and points at the origin of large differences in the number of neighbors of individual nodes.},
doi = {},
url = {https://www.osti.gov/biblio/15006719}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2003},
month = {11}
}

Book:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this book.

Save / Share: