Does Habitat Variability Really Promote Metabolic Network Modularity?

Takemoto, Kazuhiro

doi:10.1371/journal.pone.0061348

Cited by 22 publications

(44 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is in contrast to recent studies in which network features that were found to correlate with environmental variability (e.g. modularity) disappeared when tested on newer versions of the data 30,43 . These findings suggest that alterations in the motif signature provide a robust indicator of adaptability and evolvability in bacterial metabolic networks.…”

Section: Discussioncontrasting

confidence: 99%

“…The metabolic data in this study is the same as used by Takemoto 30 , and was derived from the KEGG database 31 on May 20th, 2011. In total, we studied upto 383 bacterial species (see Tables 1 and 2 for an overview of some basic network properties), each being characterised by a number of shared biological features (e.g.…”

Section: Metabolic Networkmentioning

confidence: 99%

“…This avoids, for example, issues that arise when constructing randomised networks through the rewiring of metabolic networks consisting of a number of disconnected components. For further details of the network construction the interested reader is referred to the papers of Takemoto and colleagues 11,30,35 . It is worth noting that, in addition to the modelling issues touched upon above, there are general limitations to any complex network study of metabolism due to noisy and incomplete metabolic maps (e.g.…”

Section: Metabolic Networkmentioning

confidence: 99%

See 2 more Smart Citations

Network motif frequency vectors reveal evolving metabolic network organisation

2015

View full text Add to dashboard Cite

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.

show abstract

Section: Discussioncontrasting

confidence: 99%

Section: Metabolic Networkmentioning

confidence: 99%

Section: Metabolic Networkmentioning

confidence: 99%

See 1 more Smart Citation

Network motif frequency vectors reveal evolving metabolic network organisation

2015

View full text Add to dashboard Cite

show abstract

“…The procedure for metabolic network construction is generally the same as that reported previously (Takemoto, 2013). XML files (version 0.7.1) containing the metabolic network data were downloaded from the KEGG database (Kanehisa et al, 2014) (ftp://ftp.genome.jp/pub/kegg/xml/kgml/metabolic/organisms/) on August 11, 2015, and two types of metabolic networks were constructed: compound networks and enzymatic networks.…”

Section: Construction Of Metabolic Networkmentioning

confidence: 99%

“…In particular, network modularity may be overestimated or underestimated due to isolated components (Parter et al, 2007;Takemoto & Borjigin, 2011;Takemoto, 2013); thus, the use of entire networks was avoided in the present study. However, similar conclusions were also obtained using entire networks.…”

Section: Construction Of Metabolic Networkmentioning

confidence: 99%

Habitat variability does not generally promote metabolic network modularity in flies and mammals

Takemoto

2016

Biosystems

Self Cite

View full text Add to dashboard Cite

The evolution of species habitat range is an important topic over a wide range of research fields. In higher organisms, habitat range evolution is generally associated with genetic events such as gene duplication. However, the specific factors that determine habitat variability remain unclear at higher levels of biological organization (e.g., biochemical networks). One widely accepted hypothesis developed from both theoretical and empirical analyses is that habitat variability promotes network modularity; however, this relationship has not yet been directly tested in higher organisms. Therefore, I investigated the relationship between habitat variability and metabolic network modularity using compound and enzymatic networks in flies and mammals. Contrary to expectation, there was no clear positive correlation between habitat variability and network modularity. As an exception, the network modularity increased with habitat variability in the enzymatic networks of flies. However, the observed association was likely an artifact, and the frequency of gene duplication appears to be the main factor contributing to network modularity. These findings raise the question of whether or not there is a general mechanism for habitat range expansion at a higher level (i.e., above the gene scale). This study suggests that the currently widely accepted hypothesis for habitat variability should be reconsidered.

show abstract

Modular organization of cancer signaling networks is associated with patient survivability

Takemoto

Kihara

2013

Biosystems

Self Cite

View full text Add to dashboard Cite

Molecular signaling networks are believed to determine cancer robustness. Although cancer patient survivability was reported to correlate with the heterogeneous connectivity of the signaling networks inspired by theoretical studies on the increase of network robustness due to the heterogeneous connectivity, other theoretical and data analytic studies suggest an alternative explanation: the impact of modular organization of networks on biological robustness or adaptation to changing environments. In this study, thus, we evaluate whether the modularity-robustness hypothesis is applicable to cancer using network analysis. We focus on 14 specific cancer types whose molecular signaling networks are available in databases, and show that modular organization of cancer signaling networks is associated with the patient survival rate. In particular, the cancers with less modular signaling networks are more curable. This result is consistent with a prediction from the modularity-robustness hypothesis. Furthermore, we show that the network modularity is a better descriptor of the patient survival rate than the heterogeneous connectivity. However, these results do not contradict the importance of the heterogeneous connectivity. Rather, they provide new and different insights into the relationship between cellular networks and cancer behaviors. Despite several limitations of data analysis, these findings enhance our understanding of adaptive and evolutionary mechanisms of cancer cells.

show abstract

Does Habitat Variability Really Promote Metabolic Network Modularity?

Cited by 22 publications

References 61 publications

Network motif frequency vectors reveal evolving metabolic network organisation

Network motif frequency vectors reveal evolving metabolic network organisation

Habitat variability does not generally promote metabolic network modularity in flies and mammals

Modular organization of cancer signaling networks is associated with patient survivability

Contact Info

Product

Resources

About