Metabolic network modularity arising from simple growth processes

Takemoto, Kazuhiro

doi:10.1103/physreve.86.036107

Cited by 16 publications

(25 citation statements)

References 43 publications

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“…For example, neutral models suggest that duplicating parts of a network can increase its modularity (“duplication-differentiation” model [15]) or similarly that mutation, duplication and genetic drift [16] can lead to modularity. Modularity in metabolic networks was suggested to arise from a neutral growth process [17], [18]. On the other hand, other studies suggest that modularity can be selected for, either indirectly or directly.…”

Section: Introductionmentioning

confidence: 99%

Mutation Rules and the Evolution of Sparseness and Modularity in Biological Systems

et al. 2013

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Biological systems exhibit two structural features on many levels of organization: sparseness, in which only a small fraction of possible interactions between components actually occur; and modularity – the near decomposability of the system into modules with distinct functionality. Recent work suggests that modularity can evolve in a variety of circumstances, including goals that vary in time such that they share the same subgoals (modularly varying goals), or when connections are costly. Here, we studied the origin of modularity and sparseness focusing on the nature of the mutation process, rather than on connection cost or variations in the goal. We use simulations of evolution with different mutation rules. We found that commonly used sum-rule mutations, in which interactions are mutated by adding random numbers, do not lead to modularity or sparseness except for in special situations. In contrast, product-rule mutations in which interactions are mutated by multiplying by random numbers – a better model for the effects of biological mutations – led to sparseness naturally. When the goals of evolution are modular, in the sense that specific groups of inputs affect specific groups of outputs, product-rule mutations also lead to modular structure; sum-rule mutations do not. Product-rule mutations generate sparseness and modularity because they tend to reduce interactions, and to keep small interaction terms small.

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Section: Introductionmentioning

confidence: 99%

Mutation Rules and the Evolution of Sparseness and Modularity in Biological Systems

et al. 2013

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“…We constructed metabolic networks–represented as undirected networks in which nodes and edges correspond to metabolites and substrate–product relationships, respectively–and calculated their modularity values ( Q ), as described in previous studies [10], [22], [27] (see Materials and Methods for details). Although metabolic networks are obviously directed, we considered undirected networks for comparison to the previous study [10].…”

Section: Resultsmentioning

confidence: 99%

“…Enzymes require a certain level of structural stability to survive in hot environments; thus, they tend to be easily deactivated (i.e., disappearance of edges) under such conditions, and as a result, the modular structure may collapse. The details of possible mechanisms underlying network modularity based on these growth conditions have been discussed previously [22], [27].…”

Section: Discussionmentioning

confidence: 99%

“…However, they focused on protein interaction networks, not metabolic networks, and only presented qualitative results on the origin of network modularity. Therefore, we proposed an evolving network model without tuning parameters to describe the metabolic networks, and demonstrated quantitatively that metabolic network modularity could arise through simple processes, independent of changes in the evolutionary goal or habitat variability [27].…”

Section: Introductionmentioning

confidence: 99%

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Does Habitat Variability Really Promote Metabolic Network Modularity?

Takemoto

2013

PLoS ONE

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The hypothesis that variability in natural habitats promotes modular organization is widely accepted for cellular networks. However, results of some data analyses and theoretical studies have begun to cast doubt on the impact of habitat variability on modularity in metabolic networks. Therefore, we re-evaluated this hypothesis using statistical data analysis and current metabolic information. We were unable to conclude that an increase in modularity was the result of habitat variability. Although horizontal gene transfer was also considered because it may contribute for survival in a variety of environments, closely related to habitat variability, and is known to be positively correlated with network modularity, such a positive correlation was not concluded in the latest version of metabolic networks. Furthermore, we demonstrated that the previously observed increase in network modularity due to habitat variability and horizontal gene transfer was probably due to a lack of available data on metabolic reactions. Instead, we determined that modularity in metabolic networks is dependent on species growth conditions. These results may not entirely discount the impact of habitat variability and horizontal gene transfer. Rather, they highlight the need for a more suitable definition of habitat variability and a more careful examination of relationships of the network modularity with horizontal gene transfer, habitats, and environments.

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“…Metabolic networks are suggested to be self-dissimilar and scale-rich [19,20] because they are compartmentalized [21] and the degree distributions among different biosynthetic modules are very different, although the definition of modules (boxes) is different between these studies and previous studies (e.g., [11,13]). This inconsistent conclusion emphasizes the need for a more careful examination of network fractality.…”

Section: Introductionmentioning

confidence: 99%

Metabolic networks are almost nonfractal: A comprehensive evaluation

Takemoto

2014

Phys. Rev. E

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Network self-similarity or fractality are widely accepted as an important topological property of metabolic networks; however, recent studies cast doubt on the reality of self-similarity in the networks. Therefore, we perform a comprehensive evaluation of metabolic network fractality using a box-covering method with an earlier version and the latest version of metabolic networks, and demonstrate that the latest metabolic networks are almost self-dissimilar, while the earlier ones are fractal, as reported in a number of previous studies. This result may be because the networks were randomized because of an increase in network density due to database updates, suggesting that the previously observed network fractality was due to a lack of available data on metabolic reactions. This finding may not entirely discount the importance of self-similarity of metabolic networks. Rather, it highlights the need for a more suitable definition of network fractality and a more careful examination of self-similarity of metabolic networks.

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Metabolic network modularity arising from simple growth processes

Cited by 16 publications

References 43 publications

Mutation Rules and the Evolution of Sparseness and Modularity in Biological Systems

Mutation Rules and the Evolution of Sparseness and Modularity in Biological Systems

Does Habitat Variability Really Promote Metabolic Network Modularity?

Metabolic networks are almost nonfractal: A comprehensive evaluation

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