Print Email Facebook Twitter Statistics and simulation of growth of single bacterial cells Title Statistics and simulation of growth of single bacterial cells: Illustrations with B. subtilis and E. coli Author Van Heerden, Johan H. (Vrije Universiteit Amsterdam) Kempe, Hermannus (Swammerdam Institute for Life Sciences) Doerr, A. (TU Delft BN/Marileen Dogterom Lab; Vrije Universiteit Amsterdam) Maarleveld, Timo (Vrije Universiteit Amsterdam; Central Risk Management) Nordholt, Niclas (Vrije Universiteit Amsterdam; Federal Institute for Materials Research and Testing Berlin) Bruggeman, Frank J. (Vrije Universiteit Amsterdam) Date 2017 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. To reference this document use: http://resolver.tudelft.nl/uuid:bb365148-bf86-4896-894b-bdb8068f2189 DOI https://doi.org/10.1038/s41598-017-15895-4 ISSN 2045-2322 Source Scientific Reports, 7 (1) Part of collection Institutional Repository Document type journal article Rights © 2017 Johan H. Van Heerden, Hermannus Kempe, A. Doerr, Timo Maarleveld, Niclas Nordholt, Frank J. Bruggeman /islandora/object/uuid:bb365148-bf86-4896-894b-bdb8068f2189/datastream/OBJ/view