Network motif

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Network motif refers to recurring, significant patterns of interconnections occurring in complex networks at numbers that are significantly higher than those in randomized networks. These motifs are considered the building blocks of complex networks, including biological networks, social networks, and technological networks.

Overview

Network motifs are small subgraphs, typically consisting of 3 to 8 nodes, that appear more frequently in a given network than would be expected in a random network of the same size and degree distribution. The study of network motifs helps in understanding the structural and functional properties of complex networks.

Types of Network Motifs

Network motifs can be classified based on the type of network they are found in:

• Feed-forward loop: A common motif in gene regulatory networks where a gene regulates a second gene, and both jointly regulate a third gene.
• Bi-fan motif: Often found in protein-protein interaction networks, where two proteins interact with two other proteins.
• Triadic closure: Common in social networks, where if two people have a common friend, they are more likely to become friends themselves.

Applications

Network motifs have applications in various fields:

• Systems biology: Understanding the design principles of biological networks such as metabolic networks and signal transduction pathways.
• Neuroscience: Analyzing the connectivity patterns in neural networks.
• Sociology: Studying the structure of social networks to understand social dynamics and influence.
• Engineering: Designing robust and efficient communication networks and transportation networks.

Identification of Network Motifs

The identification of network motifs involves the following steps:

1. Network Construction: Building the network from empirical data. 2. Subgraph Enumeration: Counting all possible subgraphs of a given size. 3. Random Network Generation: Creating randomized versions of the original network to serve as a null model. 4. Statistical Analysis: Comparing the frequency of subgraphs in the original network to those in the randomized networks to identify significant motifs.

Significance

The presence of network motifs indicates non-random structural features that can provide insights into the underlying principles governing the network's formation and function. For example, the prevalence of feed-forward loops in gene regulatory networks suggests a mechanism for filtering out noise and ensuring robust gene expression.

See Also

• Complex network
• Graph theory
• Systems biology
• Social network analysis
• Neural network