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dc.contributor.author
Rybiński, Mikołaj
dc.contributor.author
Möller, Simon
dc.contributor.author
Sunnåker, Mikael
dc.contributor.author
Lormeau, Claude
dc.contributor.author
Stelling, Jörg
dc.date.accessioned
2020-02-07T13:22:27Z
dc.date.available
2020-02-06T04:10:07Z
dc.date.available
2020-02-07T13:22:27Z
dc.date.issued
2020
dc.identifier.issn
1471-2105
dc.identifier.other
10.1186/s12859-020-3343-y
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/397276
dc.identifier.doi
10.3929/ethz-b-000397276
dc.description.abstract
Background To develop mechanistic dynamic models in systems biology, one often needs to identify all (or minimal) representations of the biological processes that are consistent with experimental data, out of a potentially large set of hypothetical mechanisms. However, a simple enumeration of all alternatives becomes quickly intractable when the number of model parameters grows. Selecting appropriate dynamic models out of a large ensemble of models, taking the uncertainty in our biological knowledge and in the experimental data into account, is therefore a key current problem in systems biology. Results The TopoFilter package addresses this problem in a heuristic and automated fashion by implementing the previously described topological filtering method for Bayesian model selection. It includes a core heuristic for searching the space of submodels of a parametrized model, coupled with a sampling-based exploration of the parameter space. Recent developments of the method allow to balance exhaustiveness and speed of the model space search, to efficiently re-sample parameters, to parallelize the search, and to use custom scoring functions. We use a theoretical example to motivate these features and then demonstrate TopoFilter’s applicability for a yeast signaling network with more than 250’000 possible model structures. Conclusions TopoFilter is a flexible software framework that makes Bayesian model selection and reduction efficient and scalable to network models of a complexity that represents contemporary problems in, for example, cell signaling. TopoFilter is open-source, available under the GPL-3.0 license at https://gitlab.com/csb.ethz/TopoFilter. It includes installation instructions, a quickstart guide, a description of all package options, and multiple examples.
en_US
dc.format
application/pdf
en_US
dc.publisher
Biomed Central
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Ensemble modeling
en_US
dc.subject
Bayesian model selection
en_US
dc.subject
topological filtering
en_US
dc.subject
Signal transduction
en_US
dc.title
Topofilter: A matlab package for mechanistic model identification in systems biology
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2020-01-29
ethz.journal.title
BMC Bioinformatics
ethz.journal.volume
21
en_US
ethz.pages.start
34
en_US
ethz.size
12 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02060 - Dep. Biosysteme / Dep. of Biosystems Science and Eng.::03699 - Stelling, Jörg / Stelling, Jörg
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02060 - Dep. Biosysteme / Dep. of Biosystems Science and Eng.::03699 - Stelling, Jörg / Stelling, Jörg
ethz.date.deposited
2020-02-06T04:10:30Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-02-07T13:22:38Z
ethz.rosetta.lastUpdated
2021-02-15T07:51:42Z
ethz.rosetta.versionExported
true
ethz.COinS
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