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dc.contributor.author
Kuzmanovska, Irena
dc.contributor.author
Milias-Argeitis, Andreas
dc.contributor.author
Mikelson, Jan
dc.contributor.author
Zechner, Christoph
dc.contributor.author
Khammash, Mustafa
dc.date.accessioned
2017-10-26T07:12:55Z
dc.date.available
2017-10-06T02:53:04Z
dc.date.available
2017-10-18T15:48:44Z
dc.date.available
2017-10-18T15:57:24Z
dc.date.available
2017-10-26T07:12:55Z
dc.date.issued
2017-04-26
dc.identifier.issn
1752-0509
dc.identifier.other
10.1186/s12918-017-0425-1
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/191159
dc.identifier.doi
10.3929/ethz-b-000191159
dc.description.abstract
Background: With the advance of experimental techniques such as time-lapse fluorescence microscopy, the availability of single-cell trajectory data has vastly increased, and so has the demand for computational methods suitable for parameter inference with this type of data. Most of currently available methods treat single-cell trajectories independently, ignoring the mother-daughter relationships and the information provided by the population structure. However, this information is essential if a process of interest happens at cell division, or if it evolves slowly compared to the duration of the cell cycle. Results: In this work, we propose a Bayesian framework for parameter inference on single-cell time-lapse data from lineage trees. Our method relies on a combination of Sequential Monte Carlo for approximating the parameter likelihood function and Markov Chain Monte Carlo for parameter exploration. We demonstrate our inference framework on two simple examples in which the lineage tree information is crucial: one in which the cell phenotype can only switch at cell division and another where the cell state fluctuates slowly over timescales that extend well beyond the cell-cycle duration. Conclusion: There exist several examples of biological processes, such as stem cell fate decisions or epigenetically controlled phase variation in bacteria, where the cell ancestry is expected to contain important information about the underlying system dynamics. Parameter inference methods that discard this information are expected to perform poorly for such type of processes. Our method provides a simple and computationally efficient way to take into account single-cell lineage tree data for the purpose of parameter inference and serves as a starting point for the development of more sophisticated and powerful approaches in the future.
en_US
dc.language.iso
en
en_US
dc.publisher
BioMed Central
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Stochastic systems
en_US
dc.subject
Single cell
en_US
dc.subject
Monte Carlo methods
en_US
dc.subject
Parameter inference
en_US
dc.subject
Cell lineages
en_US
dc.title
Parameter inference for stochastic single-cell dynamics from lineage tree data
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
BMC Systems Biology
ethz.journal.volume
11
en_US
ethz.journal.abbreviated
BMC syst. biol.
ethz.pages.start
52
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.grant
Efficient Computational Methods and Software Tools for the Inference of Stochastic Models of Biochemical Reaction Networks
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.::03921 - Khammash, Mustafa / Khammash, Mustafa
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02060 - Dep. Biosysteme / Dep. of Biosystems Science and Eng.::03921 - Khammash, Mustafa / Khammash, Mustafa
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02060 - Dep. Biosysteme / Dep. of Biosystems Science and Eng.::03921 - Khammash, Mustafa / Khammash, Mustafa
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02060 - Dep. Biosysteme / Dep. of Biosystems Science and Eng.::03921 - Khammash, Mustafa / Khammash, Mustafa
ethz.grant.agreementno
157129
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
ethz.date.deposited
2017-10-06T02:53:09Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-10-18T15:48:46Z
ethz.rosetta.lastUpdated
2018-11-05T23:22:59Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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