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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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Structure of the 1,000 bp Ancestral Genome Used To Start All Evolutionary RunsEach gene begins with a start codon (green), followed by type, expression level, and specificity determining regions (red, yellow, pink, respectively), followed by domains encoding protein affinity. The last two reading frames (at 800 bp and 808 bp) are overlapping genes.
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pcbi-0040023-g011: Structure of the 1,000 bp Ancestral Genome Used To Start All Evolutionary RunsEach gene begins with a start codon (green), followed by type, expression level, and specificity determining regions (red, yellow, pink, respectively), followed by domains encoding protein affinity. The last two reading frames (at 800 bp and 808 bp) are overlapping genes.

Mentions: We designed the ancestral genome to have 3 genes on the first 1,000 bp chromosome, with the 2nd chromosome of 1,000 bps filled with poly-‘3′s in order to be as distant as possible to start codons. However, it turned out that the third gene has a start codon (0000) within its specificity domain as well as in the sequence specifying the expression level, both of which give rise to two additional proteins in overlapping reading frames (see Figure 11). Those proteins, because they are useless to the organism, quickly disappear within the first tens of generations. The spaces between the first three genes are filled with random sequence, and the 880 bp genome is padded with 120 poly-‘3′s, to make up the 1,000 bp of the ancestral genome as sketched in Figure 11.


Evolution of complex modular biological networks.

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

Structure of the 1,000 bp Ancestral Genome Used To Start All Evolutionary RunsEach gene begins with a start codon (green), followed by type, expression level, and specificity determining regions (red, yellow, pink, respectively), followed by domains encoding protein affinity. The last two reading frames (at 800 bp and 808 bp) are overlapping genes.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-0040023-g011: Structure of the 1,000 bp Ancestral Genome Used To Start All Evolutionary RunsEach gene begins with a start codon (green), followed by type, expression level, and specificity determining regions (red, yellow, pink, respectively), followed by domains encoding protein affinity. The last two reading frames (at 800 bp and 808 bp) are overlapping genes.
Mentions: We designed the ancestral genome to have 3 genes on the first 1,000 bp chromosome, with the 2nd chromosome of 1,000 bps filled with poly-‘3′s in order to be as distant as possible to start codons. However, it turned out that the third gene has a start codon (0000) within its specificity domain as well as in the sequence specifying the expression level, both of which give rise to two additional proteins in overlapping reading frames (see Figure 11). Those proteins, because they are useless to the organism, quickly disappear within the first tens of generations. The spaces between the first three genes are filled with random sequence, and the 880 bp genome is padded with 120 poly-‘3′s, to make up the 1,000 bp of the ancestral genome as sketched in Figure 11.

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