Network motif frequency vectors reveal evolving metabolic network organisation

Abstract

At the systems level many organisms of interest may be described by their patterns of interaction, and as such, are perhaps best characterised via network or graph models. Metabolic networks, in particular, are fundamental to the proper functioning of many important biological processes, and thus, have been widely studied over the past decade or so. Such investigations have revealed a number of shared topological features, such as a short characteristic path-length, large clustering coefficient and hierarchical modular structure. However, the extent to which evolutionary and functional properties of metabolism manifest via this underlying network architecture remains unclear. In this paper, we employ a novel graph embedding technique, based upon low-order network motifs, to compare metabolic network structure for 383 bacterial species categorised according to a number of biological features. In particular, we introduce a new global significance score which enables us to quantify important evolutionary relationships that exist between organisms and their physical environments. Using this new approach, we demonstrate a number of significant correlations between environmental factors, such as growth conditions and habitat variability, and network motif structure, providing evidence that organism adaptability leads to increased complexities in the resultant metabolic networks.

Graphlet-based Characterization of Directed Networks

“…Recently, Pearcy et al . 21 related the similarities between the metabolic networks of 383 bacterial species, as measured by the similarity of their motif spectra, with their phenotypic variability (e.g., aquatic or terrestrial species, aerobic or anaerobic environment), which suggests that adaptation to environment during evolution may have shaped the topology of metabolic networks. Finally, motifs and their spectra were used to relate the positioning of enzymes and metabolites in metabolic networks and their biological functions.…”

Identification and Analysis of Bacterial Genomic Metabolic Signatures

“…For instance, free-living and host-associated organisms differed with respect to frequency of observed carbohydrate and energy metabolism pathways; motile and non-motile organisms differed with respect to xenobiotic degradation pathways. More recently, Pearcy et al [6] introduced a method that produced vectors for an organism whose elements described individual network motifs. They analyzed 3 and 4 node motifs that are abstractions of specific compound and reaction connections and identified network motifs that were enriched for organisms with different habitats/lifestyles, such as aerobic/facultative vs. anaerobic.…”

“…With thousands of annotated genome sequences of microbial organisms available, it is now possible to analyze not only the metabolic properties of individual organisms, but also the patterns that are seen in metabolic networks across organisms. This includes analyses of the evolution of specific metabolic pathways [e.g., 1,2], analyses based on network topology and properties [e.g., [3][4][5][6], analyses of simulated metabolic networks [e.g., 7,8], and combinations of flux balance analysis based modeling of metabolic networks within the context of phylogenies [9][10][11]. Such analyses can lead to a deeper understanding of the metabolic landscape represented by microbial diversity.…”

Complexity and robustness in hypernetwork models of metabolism

“…The next interesting observation is that the percolation thresholds for the specialised and aquatic classes are almost identical. This is perhaps not too surprising, however, since these two classes are often considered to be equivalent in terms of their environmental variability (Parter et al, 2007;Crofts and Estrada, 2014;Pearcy et al, 2015). Therefore, the bacteria from these two classes are likely to have a similar tolerance towards random errors, despite these bacteria being exposed to quite different conditions.…”