A network perspective on the topological importance of enzymes and their phylogenetic conservation

Abstract

Background: A metabolic network is the sum of all chemical transformations or reactions in the cell, with the metabolites being interconnected by enzyme-catalyzed reactions. Many enzymes exist in numerous species while others occur only in a few. We ask if there are relationships between the phylogenetic profile of an enzyme, or the number of different bacterial species that contain it, and its topological importance in the metabolic network. Our null hypothesis is that phylogenetic profile is independent of topological importance. To test our null hypothesis we constructed an enzyme network from the KEGG (Kyoto Encyclopedia of Genes and Genomes) database. We calculated three network indices of topological importance: the degree or the number of connections of a network node; closeness centrality, which measures how close a node is to others; and betweenness centrality measuring how frequently a node appears on all shortest paths between two other nodes.

TopEVM: Using Co-occurrence and Topology Patterns of Enzymes in Metabolic Networks to Construct Phylogenetic Trees

“…Since the Iof weighting scheme gives lower weights to the enzymes occurring in a large number of organisms, it comes that the lower the Iof value is, the more organisms the enzyme spreads in. This observation also confirms the conclusion of Liu et al [13]. That is, most of the enzymes occur in several organisms they prefer, while only few enzymes occur in most of the studied organisms.…”

“…Also using the Jaccard distance, she determines the evolutionary distance matrix and constructed phylogenetic trees. One drawback of such set-theoretic methods is that they do not usually take into account the edge information, and therefore they do not have enough topological characteristics for the network comparison, especially the topological importance of vertices [9,12,13].…”

Improving Phylogenetic Tree Accuracy through Recommending Missing Metabolic pathways: A Collaborative Filtering Approach

“…This is even harder to measure when the same reactions (or enzymes) in different pathways could be described differently in some cases. Fortunately, some previous studies have shown that structural information in the metabolic network, such as degree and betweenness centrality, correlate with phylogenetic profile (Liu et al, 2007), and phylogenetic distances can be measured through metabolic network-based distances accurately (Mazurie et al, 2008).…”

Trends in Chemical Graph Data Mining

“…Labeled graphs (either topological or geometric) have been a promising abstraction to capture the characteristics of datasets arising in many fields such as the world wide web, social networks, biology, and chemistry ( [9], [13], [30], [49]). The vertices of these graphs correspond to the entities in the objects and the edges correspond to the relations between them.…”

“…For example, in the domain of the world wide web and social networks the entire set of objects and their relations are represented via a single large graph ( [13]). In biology, objects to be mined are represented either as a single large graph (e.g., metabolic and signaling pathways) or via separate graphs (e.g., protein structures) ( [65], [30], [33]). In chemistry, each object to be mined is represented via a separate graph (e.g., molecular graphs) ( [49]).…”