Imperial College London
Alternate

DrOliverRatmann

Faculty of Natural Sciences, Department of Mathematics

Reader in Statistics and Machine Learning for Public Good
 
 
 
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Contact

 

oliver.ratmann05 Website

 
 
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Location

 

525Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Ratmann:2007:10.1371/journal.pcbi.0030230,
author = {Ratmann, O and Jorgensen, O and Hinkley, T and Stumpf, M and Richardson, S and Wiuf, C},
doi = {10.1371/journal.pcbi.0030230},
journal = {PLOS Computational Biology},
title = {Using likelihood-free inference to compare evolutionary dynamics of the protein networks of H. pylori and P. falciparum},
url = {http://dx.doi.org/10.1371/journal.pcbi.0030230},
volume = {3},
year = {2007}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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
AU  - Ratmann,O
AU  - Jorgensen,O
AU  - Hinkley,T
AU  - Stumpf,M
AU  - Richardson,S
AU  - Wiuf,C
DO  - 10.1371/journal.pcbi.0030230
PY  - 2007///
SN  - 1553-734X
TI  - Using likelihood-free inference to compare evolutionary dynamics of the protein networks of H. pylori and P. falciparum
T2  - PLOS Computational Biology
UR  - http://dx.doi.org/10.1371/journal.pcbi.0030230
UR  - http://hdl.handle.net/10044/1/33466
VL  - 3
ER  -
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