| Title |
Inferring average generation via division-linked labeling
|
|---|---|
| Published in |
Journal of Mathematical Biology, January 2016
|
| DOI | 10.1007/s00285-015-0963-3 |
| Pubmed ID | |
| Authors |
Tom S. Weber, Leïla Perié, Ken R. Duffy |
| Abstract |
For proliferating cells subject to both division and death, how can one estimate the average generation number of the living population without continuous observation or a division-diluting dye? In this paper we provide a method for cell systems such that at each division there is an unlikely, heritable one-way label change that has no impact other than to serve as a distinguishing marker. If the probability of label change per cell generation can be determined and the proportion of labeled cells at a given time point can be measured, we establish that the average generation number of living cells can be estimated. Crucially, the estimator does not depend on knowledge of the statistics of cell cycle, death rates or total cell numbers. We explore the estimator's features through comparison with physiologically parameterized stochastic simulations and extrapolations from published data, using it to suggest new experimental designs. |
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 22 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 36% |
| Student > Bachelor | 3 | 14% |
| Professor | 3 | 14% |
| Student > Ph. D. Student | 3 | 14% |
| Student > Master | 1 | 5% |
| Other | 1 | 5% |
| Unknown | 3 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 5 | 23% |
| Immunology and Microbiology | 4 | 18% |
| Agricultural and Biological Sciences | 3 | 14% |
| Mathematics | 2 | 9% |
| Medicine and Dentistry | 2 | 9% |
| Other | 3 | 14% |
| Unknown | 3 | 14% |
Attention Score in Context
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All research outputs
#22,758,309
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Outputs from Journal of Mathematical Biology
#642
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#341,940
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Outputs of similar age from Journal of Mathematical Biology
#10
of 13 outputs
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