ArticleProceedings of the National Academy of Sciences of the United States of America2007
Reconstruction of ancestral protein interaction networks for the bZIP transcription factors.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Maximum likelihood reconstruction of ancestral networks by integer linear programming.Bioinformatics (Oxford, England) · 2021Article
- Inferring Temporal Information from a Snapshot of a Dynamic Network.Scientific reports · 2019Article
- Nach Is a Novel Subgroup at an Early Evolutionary Stage of the CNC-bZIP Subfamily Transcription Factors from the Marine Bacteria to Humans.International journal of molecular sciences · 2018Article
- Modelling the evolution of transcription factor binding preferences in complex eukaryotes.Scientific reports · 2017Article
- Bayesian inference for duplication-mutation with complementarity network models.Journal of computational biology : a journal of computational molecular cell biology · 2015Article
- Evolution after whole-genome duplication: a network perspective.G3 (Bethesda, Md.) · 2013Article
- Predicting protein interactions via parsimonious network history inference.Bioinformatics (Oxford, England) · 2013Article
- The evolutionary dynamics of protein-protein interaction networks inferred from the reconstruction of ancient networks.PloS one · 2013Article
- GraphAlignment: Bayesian pairwise alignment of biological networks.BMC systems biology · 2012Article
- Parsimonious reconstruction of network evolution.Algorithms for molecular biology : AMB · 2012Article
- Evolution of gene neighborhoods within reconciled phylogenies.Bioinformatics (Oxford, England) · 2012Article
- Evolution of domain architectures and catalytic functions of enzymes in metabolic systems.Genome biology and evolution · 2012Article
- Evolution of biological interaction networks: from models to real data.Genome biology · 2011Review
- Unexpected complexity of the reef-building coral Acropora millepora transcription factor network.BMC systems biology · 2011Article
- Network archaeology: uncovering ancient networks from present-day interactions.PLoS computational biology · 2011Article
- The evolution of the phage shock protein response system: interplay between protein function, genomic organization, and system function.Molecular biology and evolution · 2011Article
- Phylogeny-guided interaction mapping in seven eukaryotes.BMC bioinformatics · 2009Article
- From evidence to inference: probing the evolution of protein interaction networks.HFSP journal · 2009Article
- Making the right connections: biological networks in the light of evolution.BioEssays : news and reviews in molecular, cellular and developmental biology · 2009Article
- Model criticism based on likelihood-free inference, with an application to protein network evolution.Proceedings of the National Academy of Sciences of the United States of America · 2009Article
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4 authors.
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Abstract
As whole-genome protein-protein interaction datasets become available for a wide range of species, evolutionary biologists have the opportunity to address some of the unanswered questions surrounding the evolution of these complex systems. Protein interaction networks from divergent organisms may be compared to investigate how gene duplication, deletion, and rewiring processes have shaped the evolution of their contemporary structures. However, current approaches for comparing observed networks from multiple species lack the phylogenetic context necessary to reconstruct the evolutionary history of a network. Here we show how probabilistic modeling can provide a platform for the quantitative analysis of multiple protein interaction networks. We apply this technique to the reconstruction of ancestral networks for the bZIP family of transcription factors and find that excellent agreement is obtained with an alternative sequence-based method for the prediction of leucine zipper interactions. Further analysis shows our probabilistic method to be significantly more robust to the presence of noise in the observed network data than a simple parsimony-based approach. In addition, the integration of evidence over multiple species means that the same method may be used to improve the quality of noisy interaction data for extant species. The ancestral states of a protein interaction network have been reconstructed here by using an explicit probabilistic model of network evolution. We anticipate that this model will form the basis of more general methods for probing the evolutionary history of biochemical networks.
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