Title of article :
Bridge and brick network motifs: Identifying significant building blocks from complex biological systems
Author/Authors :
Huang، نويسنده , , Chung-Yuan and Cheng، نويسنده , , Chia-Ying and Sun، نويسنده , , Chuen-Tsai، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2007
Pages :
11
From page :
117
To page :
127
Abstract :
SummaryObjective r focus in computational system biology research is defining organizing principles that govern complex biological network formation and evolution. The task is considered a major challenge because network behavior and function prediction requires the identification of functionally and statistically important motifs. Here we propose an algorithm for performing two tasks simultaneously: (a) detecting global statistical features and local connection structures in biological networks, and (b) locating functionally and statistically significant network motifs. s and material ne regulation networks were tested: the bacteria Escherichia coli and the yeast eukaryote Saccharomyces cerevisiae. To understand their structural organizing principles and evolutionary mechanisms, we defined bridge motifs as composed of weak links only or of at least one weak link and multiple strong links, and defined brick motifs as composed of strong links only. s examining functional and topological differences between bridge and brick motifs for predicting biological network behaviors and functions, we found that most genetic network motifs belong to the bridge category. This strongly suggests that the weak-tie links that provide unique paths for signal control significantly impact the signal processing function of transcription networks. sions and brick motif content analysis can provide researchers with global and local views of individual real networks and help them locate functionally and topologically overlapping or isolated motifs for purposes of investigating biological system functions, behaviors, and similarities.
Keywords :
Strong/weak-tie link , Complex biological systems , Small-world properties , local clustering , network motif , Network-oriented approach
Journal title :
Artificial Intelligence In Medicine
Serial Year :
2007
Journal title :
Artificial Intelligence In Medicine
Record number :
1836618
Link To Document :
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