Publications
93 results found
Ratmann O, Wiuf C, Pinney JW, 2009, From evidence to inference: probing the evolution of protein interaction networks, HFSP Journal, Vol: 3, Pages: 290-306, ISSN: 1955-2068
The evolutionary mechanisms by which protein interaction networks grow and change are beginning to be appreciated as a major factor shaping their present-day structures and properties. Starting with a consideration of the biases and errors inherent in our current views of these networks, we discuss the dangers of constructing evolutionary arguments from naïve analyses of network topology. We argue that progress in understanding the processes of network evolution is only possible when hypotheses are formulated as plausible evolutionary models and compared against the observed data within the framework of probabilistic modeling. The value of such models is expected to be greatly enhanced as they incorporate more of the details of the biophysical properties of interacting proteins, gene phylogeny, and measurement error and as more advanced methodologies emerge for model comparison and the inference of ancestral network states.
Surmeli D, Ratmann O, Mewes H-W, et al., 2008, FunCat functional inference with belief propagation and feature integration, Computational Biology and Chemistry, Vol: 32, Pages: 375-377, ISSN: 1476-9271
Pairwise comparison of sequence data is intensively used for automated functional protein annotation, while graphical models emerge as promising candidates for an integration of various heterogeneous features. We designed a model, termed hRMN that integrates different genomic features and implemented a variant of belief propagation for functional annotation transfer. hRMN allows the assignment of multiple functional categories while avoiding common problems in annotation transfer from heterogeneous datasets, such as an independency of the investigated datasets. We benchmarked this system with large-scale annotation transfer (based on the MIPS FunCat ontology) to proteins of the prokaryotes Bacillus subtilis, Helicobacter pylori, Listeria monocytogenes, and Listeria innocua. hRMN consistently outperformed two competitors in annotation of four bacterial genomes. The developed code is available for download at http://mips.gsf.de/proj/bfab/hRMN.html.
Ratmann O, Jorgensen O, Hinkley T, et al., 2007, Using likelihood-free inference to compare evolutionary dynamics of the protein networks of H. pylori and P. falciparum, PLOS Computational Biology, Vol: 3, ISSN: 1553-734X
Gene duplication with subsequent interaction divergence is one of the primary driving forces in the evolution of genetic systems. Yet little is known about the precise mechanisms and the role of duplication divergence in the evolution of protein networks from the prokaryote and eukaryote domains. We developed a novel, model-based approach for Bayesian inference on biological network data that centres on approximate Bayesian computation, or likelihood-free inference. Instead of computing the intractable likelihood of the protein network topology, our method summarizes key features of the network and, based on these, uses a MCMC algorithm to approximate the posterior distribution of the model parameters. This allowed us to reliably fit a flexible mixture model that captures hallmarks of evolution by gene duplication and subfunctionalization to protein interaction network data of Helicobacter pylori and Plasmodium falciparum. The 80% credible intervals for the duplication–divergence component are [0.64, 0.98] for H. pylori and [0.87, 0.99] for P. falciparum. The remaining parameter estimates are not inconsistent with sequence data. An extensive sensitivity analysis showed that incompleteness of PIN data does not largely affect the analysis of models of protein network evolution, and that the degree sequence alone barely captures the evolutionary footprints of protein networks relative to other statistics. Our likelihood-free inference approach enables a fully Bayesian analysis of a complex and highly stochastic system that is otherwise intractable at present. Modelling the evolutionary history of PIN data, it transpires that only the simultaneous analysis of several global aspects of protein networks enables credible and consistent inference to be made from available datasets. Our results indicate that gene duplication has played a larger part in the network evolution of the eukaryote than in the prokaryote, and suggests that single gene duplications with immedia
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