Biological network

About: Biological network is a research topic. Over the lifetime, 5125 publications have been published within this topic receiving 244794 citations.

Assortative mixing in networks.

Abstract: A network is said to show assortative mixing if the nodes in the network that have many connections tend to be connected to other nodes with many connections. Here we measure mixing patterns in a variety of networks and find that social networks are mostly assortatively mixed, but that technological and biological networks tend to be disassortative. We propose a model of an assortatively mixed network, which we study both analytically and numerically. Within this model we find that networks percolate more easily if they are assortative and that they are also more robust to vertex removal.

Functional cartography of complex metabolic networks

Abstract: High-throughput techniques are leading to an explosive growth in the size of biological databases and creating the opportunity to revolutionize our understanding of life and disease Interpretation of these data remains, however, a major scientific challenge Here, we propose a methodology that enables us to extract and display information contained in complex networks1,2,3 Specifically, we demonstrate that we can find functional modules4,5 in complex networks, and classify nodes into universal roles according to their pattern of intra- and inter-module connections The method thus yields a ‘cartographic representation’ of complex networks Metabolic networks6,7,8 are among the most challenging biological networks and, arguably, the ones with most potential for immediate applicability9 We use our method to analyse the metabolic networks of twelve organisms from three different superkingdoms We find that, typically, 80% of the nodes are only connected to other nodes within their respective modules, and that nodes with different roles are affected by different evolutionary constraints and pressures Remarkably, we find that metabolites that participate in only a few reactions but that connect different modules are more conserved than hubs whose links are mostly within a single module

Network motifs in the transcriptional regulation network of Escherichia coli

Abstract: Little is known about the design principles1,2,3,4,5,6,7,8,9,10 of transcriptional regulation networks that control gene expression in cells. Recent advances in data collection and analysis2,11,12, however, are generating unprecedented amounts of information about gene regulation networks. To understand these complex wiring diagrams1,2,3,4,5,6,7,8,9,10,13, we sought to break down such networks into basic building blocks2. We generalize the notion of motifs, widely used for sequence analysis, to the level of networks. We define 'network motifs' as patterns of interconnections that recur in many different parts of a network at frequencies much higher than those found in randomized networks. We applied new algorithms for systematically detecting network motifs to one of the best-characterized regulation networks, that of direct transcriptional interactions in Escherichia coli3,6. We find that much of the network is composed of repeated appearances of three highly significant motifs. Each network motif has a specific function in determining gene expression, such as generating temporal expression programs and governing the responses to fluctuating external signals. The motif structure also allows an easily interpretable view of the entire known transcriptional network of the organism. This approach may help define the basic computational elements of other biological networks.

Network motifs: theory and experimental approaches

Abstract: Transcription regulation networks control the expression of genes. The transcription networks of well-studied microorganisms appear to be made up of a small set of recurring regulation patterns, called network motifs. The same network motifs have recently been found in diverse organisms from bacteria to humans, suggesting that they serve as basic building blocks of transcription networks. Here I review network motifs and their functions, with an emphasis on experimental studies. Network motifs in other biological networks are also mentioned, including signalling and neuronal networks.

Collective Classification in Network Data

Abstract: Many real-world applications produce networked data such as the world-wide web (hypertext documents connected via hyperlinks), social networks (for example, people connected by friendship links), communication networks (computers connected via communication links) and biological networks (for example, protein interaction networks). A recent focus in machine learning research has been to extend traditional machine learning classification techniques to classify nodes in such networks. In this article, we provide a brief introduction to this area of research and how it has progressed during the past decade. We introduce four of the most widely used inference algorithms for classifying networked data and empirically compare them on both synthetic and real-world data.