↓ Skip to main content

Parameter inference for stochastic single-cell dynamics from lineage tree data

Overview of attention for article published in BMC Systems Biology, April 2017
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
39 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Parameter inference for stochastic single-cell dynamics from lineage tree data
Published in
BMC Systems Biology, April 2017
DOI 10.1186/s12918-017-0425-1
Pubmed ID
Authors

Irena Kuzmanovska, Andreas Milias-Argeitis, Jan Mikelson, Christoph Zechner, Mustafa Khammash

Abstract

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. 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. 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.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 39 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 23%
Researcher 8 21%
Student > Master 3 8%
Professor 3 8%
Other 2 5%
Other 3 8%
Unknown 11 28%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 21%
Agricultural and Biological Sciences 7 18%
Physics and Astronomy 3 8%
Computer Science 2 5%
Mathematics 2 5%
Other 6 15%
Unknown 11 28%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 January 2018.
All research outputs
#7,166,530
of 12,416,981 outputs
Outputs from BMC Systems Biology
#461
of 1,051 outputs
Outputs of similar age
#126,747
of 262,600 outputs
Outputs of similar age from BMC Systems Biology
#11
of 14 outputs
Altmetric has tracked 12,416,981 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,051 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 50% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 262,600 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.