Functional cartography of complex metabolic networks

By Roger Guimerà, Luís A. Nunes Amaral. Nature, 2005

3,777 citations recorded by the source corpus.

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 networks. Specifically, we demonstrate that we can find functional modules 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 networks are among the most challenging biological networks and, arguably, the ones with most potential for immediate applicability. 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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f43fd1b3ff9d6e6d36d43428460e531c7dabe322

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Citations in the corpus

Global patterns of mainland and insular pollination networks (2016, methodology): “…v.3.1.0 (R Development Core Team, 2014), the software nodf v.2.0 (Almeida-Neto & Ulrich, 2011; http://www.keib.umk.pl/ nodf/) for the calculation of weighted nestedness based on overlap and decreasing fill (WNODF) and the software NetCarto (Guimerà & Amaral, 2005) for the computation of modularity.”
ggClusterNet: An R package for microbiome network analysis and modularity‐based multiple network layouts (2022, background): “…by biologists, mathematicians, social scientists, and computational scientists to explore interactions between entities, be they individuals in a school [3], species in a food web [4], nodes on a computer network [5], proteins in metabolic pathways [6], and group comparisons in Venn network [7].”
Conservation of species interaction networks (2010, methodology): “Among these generalists, highly connected species within a compartment are referred to as ‘‘module hubs” and species that link two or more compartments together are called ‘‘connectors” (Guimerà and Amaral, 2005; Olesen et al., 2007).”
Focal Brain Lesions to Critical Locations Cause Widespread Disruption of the Modular Organization of the Brain (2012, positive): “This gives a relative (z-scored) measure of how well connected any node is to other nodes within its own module (Guimera & Nunes Amaral, 2005).”
Multilayer Analysis and Visualization of Networks (2014, background): “Network representations focus on connectivity, and they have now become a paradigmatic way to investigate the organization and functionality of cells [20, 22, 36, 40, 42, 43, 55, 63, 66, 71], synaptic connectivity [6, 16, 17, 54, 59, 60, 64, 68, 69, 72], and more.”
Robustness of community structure in networks. (2007, methodology): “Perhaps the most widely used technique, however, is the maximization of the benefit function known as modularity [5, 15, 16, 17, 18, 19, 20, 21], which is (to within a multiplicative constant) the difference between the number of edges within communities and the expected number of such edges under a”
Finding Statistically Significant Communities in Networks (2010, neutral): “subject [11], compartments in food webs [12,13], functional modules in protein interaction networks [14], biochemical pathways in metabolic networks [15,16], etc.”
Spatio‐temporal variation in the structure of pollination networks (2009, methodology): “We used a method developed by Guimerà and Amaral (2005) to calculate and test M. Modularity: M XNM S 1 Is I ks 2I 2 ; M 0; 1 1 NM where NM is number of modules in the network, I is number of interactions in the network, Is is number of interactions between species in module s (‘within-module…”