Show simple item record

dc.contributor.author
Ratmann, Oliver
dc.contributor.author
Jørgensen, Ole
dc.contributor.author
Hinkley, Trevor
dc.contributor.author
Stumpf, Michael
dc.contributor.author
Richardson, Sylvia
dc.contributor.author
Wiuf, Carsten
dc.date.accessioned
2019-01-18T13:11:34Z
dc.date.available
2017-06-08T17:52:58Z
dc.date.available
2019-01-18T13:11:34Z
dc.date.issued
2007-11-30
dc.identifier.issn
1553-734X
dc.identifier.issn
1553-7358
dc.identifier.other
10.1371/journal.pcbi.0030230
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/7713
dc.identifier.doi
10.3929/ethz-b-000007713
dc.description.abstract
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 immediate divergence alone may explain more than 60% of biological network data in both domains.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Public Library of Science
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/3.0/
dc.title
Using Likelihood-Free Inference to Compare Evolutionary Dynamics of the Protein Networks of H. pylori and P. falciparum
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 3.0 Unported
ethz.journal.title
PLoS Computational Biology
ethz.journal.volume
3
en_US
ethz.journal.issue
11
en_US
ethz.journal.abbreviated
PLOS comput. biol.
ethz.pages.start
e230
en_US
ethz.size
13 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.nebis
005410277
ethz.publication.place
San Francisco, CA, USA
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02350 - Dep. Umweltsystemwissenschaften / Dep. of Environmental Systems Science::02720 - Institut für Integrative Biologie / Institute of Integrative Biology::03584 - Bonhoeffer, Sebastian / Bonhoeffer, Sebastian
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02350 - Dep. Umweltsystemwissenschaften / Dep. of Environmental Systems Science::02720 - Institut für Integrative Biologie / Institute of Integrative Biology::03584 - Bonhoeffer, Sebastian / Bonhoeffer, Sebastian
ethz.date.deposited
2017-06-08T17:53:06Z
ethz.source
ECIT
ethz.identifier.importid
imp59364bb891b1e11509
ethz.ecitpid
pub:18309
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-20T17:09:56Z
ethz.rosetta.lastUpdated
2020-02-15T16:42:34Z
ethz.rosetta.versionExported
true
ethz.COinS
ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.atitle=Using%20Likelihood-Free%20Inference%20to%20Compare%20Evolutionary%20Dynamics%20of%20the%20Protein%20Networks%20of%20H.%20pylori%20and%20P.%20falciparum&rft.jtitle=PLoS%20Computational%20Biology&rft.date=2007-11-30&rft.volume=3&rft.issue=11&rft.spage=e230&rft.issn=1553-734X&1553-7358&rft.au=Ratmann,%20Oliver&J%C3%B8rgensen,%20Ole&Hinkley,%20Trevor&Stumpf,%20Michael&Richardson,%20Sylvia&rft.genre=article&
 Search via swisscovery

Files in this item

Thumbnail

Publication type

Show simple item record