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

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.BN/Marileen Dogterom La

Georg Neumarks/ Fürstlichen Sächsischen Weinmarischen Secretarii Poetische Tafeln/ Oder Gründliche Anweisung zur Teutschen Verskunst

GEORG NEUMARKS/ FÜRSTLICHEN SÄCHSISCHEN WEINMARISCHEN SECRETARII POETISCHE TAFELN/ ODER GRÜNDLICHE ANWEISUNG ZUR TEUTSCHEN VERSKUNST Georg Neumarks/ Fürstlichen Sächsischen Weinmarischen Secretarii Poetische Tafeln/ Oder Gründliche Anweisung zur Teutschen Verskunst ([1]r) Kupfertitel ([1]v) Titelseite ([2]r) Hermannus Conringius, ... Apud nos Germanos, fateor, non ea hactenus fuit ... ([2]v) Etlicher Vornehmen Patronen und wehrten Freunde ... ([3]r) Nothwendige Vorrede an den Gewogenen Leser. ([3]r) [15 Tafeln von der ..., Wörter Wohl- und Übelstand, ...] (1) Anmerkungen über Vorhergehende Poetische Tafeln/ Zum Theil nach eigener Anleitung des H. Authoris, ... (1) Fotodokumentation ( -

Acquired CYP19A1 amplification is an early specific mechanism of aromatase inhibitor resistance in ERα metastatic breast cancer

Tumor evolution is shaped by many variables, potentially involving external selective pressures induced by therapies. After surgery, patients with estrogen receptor (ERα)-positive breast cancer are treated with adjuvant endocrine therapy, including selective estrogen receptor modulators (SERMs) and/or aromatase inhibitors (AIs). However, more than 20% of patients relapse within 10 years and eventually progress to incurable metastatic disease. Here we demonstrate that the choice of therapy has a fundamental influence on the genetic landscape of relapsed diseases. We found that 21.5% of AI-treated, relapsed patients had acquired CYP19A1 (encoding aromatase) amplification (CYP19A1(amp)). Relapsed patients also developed numerous mutations targeting key breast cancer-associated genes, including ESR1 and CYP19A1. Notably, CYP19A1(amp) cells also emerged in vitro, but only in AI-resistant models. CYP19A1 amplification caused increased aromatase activity and estrogen-independent ERα binding to target genes, resulting in CYP19A1(amp) cells showing decreased sensitivity to AI treatment. These data suggest that AI treatment itself selects for acquired CYP19A1(amp) and promotes local autocrine estrogen signaling in AI-resistant metastatic patients