One approach to studying the URLanization of biochemistry is to use statistical graph theory. Even in such a heavily simplified method, which disregards most of the dynamic aspects of biochemistry, one is faced with fundamental questions, such as how the chemical reaction systems should be reduced to a graph so that as much information as possible about the original network is retained. Vertices may represent either chemical substances, or reactions , or both . A common practice in the analysis of metabolic networks is to preprocess the network by deleting so called currency metabolites -- abundant molecular species that react with a large number of metabolites. One of several reasons to do this is that they do not put any constraint on the reactions, and thus do not give much information of the higher order functionality of the system. In this paper we evaluate different network representations of metabolism and describe their structure with respect to both modularity and currency metabolites . We find that a "substance network," where all metabolites participating in a reaction are connected, is a good trade-off between high functional overlap (between network modules and observed functions ) and a reasonable number of currency metabolites.
One approach to studying the URLanization of biochemistry is to use statistical graph theory. Even in such a heavily simplified method, which disregards most of the dynamic aspects of biochemistry, one is faced with fundamental questions, such as how the chemical reaction systems should be reduced to a graph retaining as much functional information as possible from the original reaction system. In such graph representations, should the edges go between substrates and products, or substrates and substrates , or both ? Should vertices represent substances or reactions? Different definitions encode different information about the reaction system. In this paper we evaluate four different graph representations of metabolism , applied to data from URLanisms and databases. The graph representations are evaluated by comparing the overlap between clusters (network modules) and annotated functions, and also by comparing the set of identified currency metabolites with those that other authors have identified using qualitative biological arguments . We find that a "substance network," where all metabolites participating in a reaction are connected, is relatively better than others, evaluated both with respect to the functional overlap between modules and functions and to the number and identity of identified currency metabolites.
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so that as much
retaining as much functional
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about the original network is retained. Vertices may represent either chemical substances, or reactions
from the original reaction system. In such graph representations, should the edges go between substrates and products, or substrates and substrates
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. A common practice in the analysis of metabolic networks is to preprocess the network by deleting so called currency metabolites -- abundant molecular species that react with a large number of metabolites. One of several reasons to do this is that they do not put any constraint on the reactions, and thus do not give much information of the higher order functionality of the
? Should vertices represent substances or reactions? Different definitions encode different information about the reaction
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different network
four different graph
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and describe their structure with respect to both modularity and currency metabolites
, applied to data from URLanisms and databases. The graph representations are evaluated by comparing the overlap between clusters (network modules) and annotated functions, and also by comparing the set of identified currency metabolites with those that other authors have identified using qualitative biological arguments
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a good trade-off between high functional overlap (between network modules and observed functions ) and a reasonable number of
relatively better than others, evaluated both with respect to the functional overlap between modules and functions and to the number and identity of identified