TY - JOUR T1 - Statistics and simulation of growth of single bacterial cells: illustrations with <em>B. subtilis</em> and <em>E. coli</em> JF - bioRxiv DO - 10.1101/157545 SP - 157545 AU - Johan H. van Heerden AU - Hermannus Kempe AU - Anne Doerr AU - Timo Maarleveld AU - Niclas Nordholt AU - Frank J. Bruggeman Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/06/29/157545.abstract 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 Escherichia coli and Bacillus subtilis. 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. ER -