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Statistics and simulation of growth of single bacterial cells : Illustrations with B. subtilis and E. coli. / Van Heerden, Johan H.; Kempe, Hermannus; Doerr, Anne; Maarleveld, Timo; Nordholt, Niclas; Bruggeman, Frank J.

In: Scientific Reports, Vol. 7, No. 1, 16094, 2017.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Van Heerden, JH, Kempe, H, Doerr, A, Maarleveld, T, Nordholt, N & Bruggeman, FJ 2017, 'Statistics and simulation of growth of single bacterial cells: Illustrations with B. subtilis and E. coli' Scientific Reports, vol. 7, no. 1, 16094. https://doi.org/10.1038/s41598-017-15895-4

APA

Van Heerden, J. H., Kempe, H., Doerr, A., Maarleveld, T., Nordholt, N., & Bruggeman, F. J. (2017). Statistics and simulation of growth of single bacterial cells: Illustrations with B. subtilis and E. coli. Scientific Reports, 7(1), [16094]. https://doi.org/10.1038/s41598-017-15895-4

Vancouver

Van Heerden JH, Kempe H, Doerr A, Maarleveld T, Nordholt N, Bruggeman FJ. Statistics and simulation of growth of single bacterial cells: Illustrations with B. subtilis and E. coli. Scientific Reports. 2017;7(1). 16094. https://doi.org/10.1038/s41598-017-15895-4

Author

Van Heerden, Johan H. ; Kempe, Hermannus ; Doerr, Anne ; Maarleveld, Timo ; Nordholt, Niclas ; Bruggeman, Frank J. / Statistics and simulation of growth of single bacterial cells : Illustrations with B. subtilis and E. coli. In: Scientific Reports. 2017 ; Vol. 7, No. 1.

BibTeX

@article{bb365148bf864896894bbdb8068f2189,
title = "Statistics and simulation of growth of single bacterial cells: Illustrations with B. subtilis and E. coli",
abstract = "The inherent stochasticity of molecular reactions prevents us from predicting the exact state of single-cells in a population. However, when a population grows at steady-state, the probability to observe a cell with particular combinations of properties is fixed. Here we validate and exploit existing theory on the statistics of single-cell growth in order to predict the probability of phenotypic characteristics such as cell-cycle times, volumes, accuracy of division and cell-age distributions, using real-time imaging data for Bacillus subtilis and Escherichia coli. Our results show that single-cell growth-statistics can accurately be predicted from a few basic measurements. These equations relate different phenotypic characteristics, and can therefore be used in consistency tests of experimental single-cell growth data and prediction of single-cell statistics. We also exploit these statistical relations in the development of a fast stochastic-simulation algorithm of single-cell growth and protein expression. This algorithm greatly reduces computational burden, by recovering the statistics of growing cell-populations from the simulation of only one of its lineages. Our approach is validated by comparison of simulations and experimental data. This work illustrates a methodology for the prediction, analysis and tests of consistency of single-cell growth and protein expression data from a few basic statistical principles.",
author = "{Van Heerden}, {Johan H.} and Hermannus Kempe and Anne Doerr and Timo Maarleveld and Niclas Nordholt and Bruggeman, {Frank J.}",
year = "2017",
doi = "10.1038/s41598-017-15895-4",
language = "English",
volume = "7",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",
number = "1",

}

RIS

TY - JOUR

T1 - Statistics and simulation of growth of single bacterial cells

T2 - Scientific Reports

AU - Van Heerden, Johan H.

AU - Kempe, Hermannus

AU - Doerr, Anne

AU - Maarleveld, Timo

AU - Nordholt, Niclas

AU - Bruggeman, Frank J.

PY - 2017

Y1 - 2017

N2 - The inherent stochasticity of molecular reactions prevents us from predicting the exact state of single-cells in a population. However, when a population grows at steady-state, the probability to observe a cell with particular combinations of properties is fixed. Here we validate and exploit existing theory on the statistics of single-cell growth in order to predict the probability of phenotypic characteristics such as cell-cycle times, volumes, accuracy of division and cell-age distributions, using real-time imaging data for Bacillus subtilis and Escherichia coli. Our results show that single-cell growth-statistics can accurately be predicted from a few basic measurements. These equations relate different phenotypic characteristics, and can therefore be used in consistency tests of experimental single-cell growth data and prediction of single-cell statistics. We also exploit these statistical relations in the development of a fast stochastic-simulation algorithm of single-cell growth and protein expression. This algorithm greatly reduces computational burden, by recovering the statistics of growing cell-populations from the simulation of only one of its lineages. Our approach is validated by comparison of simulations and experimental data. This work illustrates a methodology for the prediction, analysis and tests of consistency of single-cell growth and protein expression data from a few basic statistical principles.

AB - The inherent stochasticity of molecular reactions prevents us from predicting the exact state of single-cells in a population. However, when a population grows at steady-state, the probability to observe a cell with particular combinations of properties is fixed. Here we validate and exploit existing theory on the statistics of single-cell growth in order to predict the probability of phenotypic characteristics such as cell-cycle times, volumes, accuracy of division and cell-age distributions, using real-time imaging data for Bacillus subtilis and Escherichia coli. Our results show that single-cell growth-statistics can accurately be predicted from a few basic measurements. These equations relate different phenotypic characteristics, and can therefore be used in consistency tests of experimental single-cell growth data and prediction of single-cell statistics. We also exploit these statistical relations in the development of a fast stochastic-simulation algorithm of single-cell growth and protein expression. This algorithm greatly reduces computational burden, by recovering the statistics of growing cell-populations from the simulation of only one of its lineages. Our approach is validated by comparison of simulations and experimental data. This work illustrates a methodology for the prediction, analysis and tests of consistency of single-cell growth and protein expression data from a few basic statistical principles.

UR - http://resolver.tudelft.nl/uuid:bb365148-bf86-4896-894b-bdb8068f2189

UR - http://www.scopus.com/inward/record.url?scp=85034836604&partnerID=8YFLogxK

U2 - 10.1038/s41598-017-15895-4

DO - 10.1038/s41598-017-15895-4

M3 - Article

VL - 7

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

IS - 1

M1 - 16094

ER -

ID: 34217676