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In this paper we measure the relation between network modularity and several aspects of robustness directly in a model system of metabolism.
Further, we identified a strong association between network modularity and the microbe's temperature and oxygen requirements.
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Díaz-Mejía et al. [ 7] investigated the relation of network modularity and distance between reactions with the retention of gene duplicates in various species and databases.
When we look closer at quantities designed to characterize the modular functionality, we see higher network modularity and more currency metabolites in metabolic networks than atmospheric networks.
InfoMod is an information-theoretic approach to quantifying network modularity and revealing functional modules in large networks.
For each random network, we calculate the network modularity, and calculate the average and standard deviation for the entire set of random networks.
Netcarto was used by Tamames et al. [ 33] to explore the relationship between reduction in genome size and network modularity.
Here, we attempt to test these findings in a natural biological system, by asking whether there is a correlation between metabolic network's modularity and the variability of the environment in which it evolved.
As recommended in [19], we extended the JUNG implementation to sequentially remove all edges, and recalculate the network modularity after each edge removal, and automatically identify the number of edges which results in the optimal / maximum network modularity.
For each random network, the network modularity score was recalculated, and the observed modularity score was then converted to a z-score or a scaled modularity score [30].
In terms of metabolic networks, this means that there will be many connections between the modules and thus that the network modularity will be comparatively low.
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