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Evolution of complex modular biological networks.

Hintze A, Adami C - PLoS Comput. Biol. (2008)

Bottom Line: These networks evolve in environments that differ in their predictability, and allow us to study modularity from topological, information-theoretic, and gene-epistatic points of view using new tools that do not depend on any preconceived notion of modularity.We find that for our evolved complex networks as well as for the yeast protein-protein interaction network, synthetic lethal gene pairs consist mostly of redundant genes that lie close to each other and therefore within modules, while knockdown suppressor gene pairs are farther apart and often straddle modules, suggesting that knockdown rescue is mediated by alternative pathways or modules.The combination of network modularity tools together with genetic interaction data constitutes a powerful approach to study and dissect the role of modularity in the evolution and function of biological networks.

View Article: PubMed Central - PubMed

Affiliation: Keck Graduate Institute of Applied Life Sciences, Claremont, California, USA.

ABSTRACT
Biological networks have evolved to be highly functional within uncertain environments while remaining extremely adaptable. One of the main contributors to the robustness and evolvability of biological networks is believed to be their modularity of function, with modules defined as sets of genes that are strongly interconnected but whose function is separable from those of other modules. Here, we investigate the in silico evolution of modularity and robustness in complex artificial metabolic networks that encode an increasing amount of information about their environment while acquiring ubiquitous features of biological, social, and engineering networks, such as scale-free edge distribution, small-world property, and fault-tolerance. These networks evolve in environments that differ in their predictability, and allow us to study modularity from topological, information-theoretic, and gene-epistatic points of view using new tools that do not depend on any preconceived notion of modularity. We find that for our evolved complex networks as well as for the yeast protein-protein interaction network, synthetic lethal gene pairs consist mostly of redundant genes that lie close to each other and therefore within modules, while knockdown suppressor gene pairs are farther apart and often straddle modules, suggesting that knockdown rescue is mediated by alternative pathways or modules. The combination of network modularity tools together with genetic interaction data constitutes a powerful approach to study and dissect the role of modularity in the evolution and function of biological networks.

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Average Diameter (Path Length) under Node RemovalAverage network diameter at depth 5,000 under node removal, for the functional network. Light colored dots, path length with removal of hubs; dark colored dots, path length with removal of random nodes; green, static environment; blue, quasi-static; and red, dynamic environment. The breakdown under hub removal comes at about 200 hubs removed.
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pcbi-0040023-g004: Average Diameter (Path Length) under Node RemovalAverage network diameter at depth 5,000 under node removal, for the functional network. Light colored dots, path length with removal of hubs; dark colored dots, path length with removal of random nodes; green, static environment; blue, quasi-static; and red, dynamic environment. The breakdown under hub removal comes at about 200 hubs removed.

Mentions: Similar to what was observed in yeast protein–protein interaction networks [21], the path length in our networks increases dramatically up to a break point when nodes that are characterized as hubs are removed from the network (see Figure 4), but increases smoothly until the network almost collapses if random nodes are removed instead. In Figure 5, we show a network evolved in a dynamic environment, with 534 genes producing 435 molecules, with nodes representing molecules and proteins (functional network annotation, rendered with Pajek [39]).


Evolution of complex modular biological networks.

Hintze A, Adami C - PLoS Comput. Biol. (2008)

Average Diameter (Path Length) under Node RemovalAverage network diameter at depth 5,000 under node removal, for the functional network. Light colored dots, path length with removal of hubs; dark colored dots, path length with removal of random nodes; green, static environment; blue, quasi-static; and red, dynamic environment. The breakdown under hub removal comes at about 200 hubs removed.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2233666&req=5

pcbi-0040023-g004: Average Diameter (Path Length) under Node RemovalAverage network diameter at depth 5,000 under node removal, for the functional network. Light colored dots, path length with removal of hubs; dark colored dots, path length with removal of random nodes; green, static environment; blue, quasi-static; and red, dynamic environment. The breakdown under hub removal comes at about 200 hubs removed.
Mentions: Similar to what was observed in yeast protein–protein interaction networks [21], the path length in our networks increases dramatically up to a break point when nodes that are characterized as hubs are removed from the network (see Figure 4), but increases smoothly until the network almost collapses if random nodes are removed instead. In Figure 5, we show a network evolved in a dynamic environment, with 534 genes producing 435 molecules, with nodes representing molecules and proteins (functional network annotation, rendered with Pajek [39]).

Bottom Line: These networks evolve in environments that differ in their predictability, and allow us to study modularity from topological, information-theoretic, and gene-epistatic points of view using new tools that do not depend on any preconceived notion of modularity.We find that for our evolved complex networks as well as for the yeast protein-protein interaction network, synthetic lethal gene pairs consist mostly of redundant genes that lie close to each other and therefore within modules, while knockdown suppressor gene pairs are farther apart and often straddle modules, suggesting that knockdown rescue is mediated by alternative pathways or modules.The combination of network modularity tools together with genetic interaction data constitutes a powerful approach to study and dissect the role of modularity in the evolution and function of biological networks.

View Article: PubMed Central - PubMed

Affiliation: Keck Graduate Institute of Applied Life Sciences, Claremont, California, USA.

ABSTRACT
Biological networks have evolved to be highly functional within uncertain environments while remaining extremely adaptable. One of the main contributors to the robustness and evolvability of biological networks is believed to be their modularity of function, with modules defined as sets of genes that are strongly interconnected but whose function is separable from those of other modules. Here, we investigate the in silico evolution of modularity and robustness in complex artificial metabolic networks that encode an increasing amount of information about their environment while acquiring ubiquitous features of biological, social, and engineering networks, such as scale-free edge distribution, small-world property, and fault-tolerance. These networks evolve in environments that differ in their predictability, and allow us to study modularity from topological, information-theoretic, and gene-epistatic points of view using new tools that do not depend on any preconceived notion of modularity. We find that for our evolved complex networks as well as for the yeast protein-protein interaction network, synthetic lethal gene pairs consist mostly of redundant genes that lie close to each other and therefore within modules, while knockdown suppressor gene pairs are farther apart and often straddle modules, suggesting that knockdown rescue is mediated by alternative pathways or modules. The combination of network modularity tools together with genetic interaction data constitutes a powerful approach to study and dissect the role of modularity in the evolution and function of biological networks.

Show MeSH