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Sci Rep. 2017 Nov 23;7(1):16094. doi: 10.1038/s41598-017-15895-4.

Statistics and simulation of growth of single bacterial cells: illustrations with B. subtilis and E. coli.

Author information

1
Systems Bioinformatics, VU University, De Boelelaan 1087, 1081 HV, Amsterdam, The Netherlands.
2
Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, The Netherlands.
3
Department of Bionanoscience, Kavli Institute of Nanoscience, TU Delft, Delft, The Netherlands.
4
Central Risk Management, ABN AMRO NV, Amsterdam, The Netherlands.
5
Federal Institute for Materials Research and Testing (Department of Materials and Environment, Division Biodeterioration and Reference Organisms), D-12205, Berlin, Germany.
6
Systems Bioinformatics, VU University, De Boelelaan 1087, 1081 HV, Amsterdam, The Netherlands. f.j.bruggeman@vu.nl.

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.

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