Send to

Choose Destination
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

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


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.

Supplemental Content

Full text links

Icon for Nature Publishing Group Icon for PubMed Central
Loading ...
Support Center