Condition-dependent cell volume and concentration of Escherichia coli to facilitate data conversion for systems biology modeling
| LEADER | naa a22 4500 | ||
|---|---|---|---|
| 001 | 52878210X | ||
| 005 | 20180924065513.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 180924e20110729xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.3929/ethz-b-000038570 |2 doi |
| 024 | 7 | 0 | |a 10.1371/journal.pone.0023126 |2 doi |
| 035 | |a (ETHRESEARCH)oai:www.research-collecti.ethz.ch:20.500.11850/276902 | ||
| 100 | 1 | |a Volkmer |D Benjamin | |
| 245 | 1 | 0 | |a Condition-dependent cell volume and concentration of Escherichia coli to facilitate data conversion for systems biology modeling |h [Elektronische Daten] |c [Benjamin Volkmer, Matthias Heinemann] |
| 246 | 0 | |a PLoS ONE | |
| 506 | |a Open access |2 ethresearch | ||
| 520 | 3 | |a Systems biology modeling typically requires quantitative experimental data such as intracellular concentrations or copy numbers per cell. In order to convert population-averaging omics measurement data to intracellular concentrations or cellular copy numbers, the total cell volume and number of cells in a sample need to be known. Unfortunately, even for the often studied model bacterium Escherichia coli this information is hardly available and furthermore, certain measures (e.g. cell volume) are also dependent on the growth condition. In this work, we have determined these basic data for E. coli cells when grown in 22 different conditions so that respective data conversions can be done correctly. First, we determine growth-rate dependent cell volumes. Second, we show that in a 1 ml E. coli sample at an optical density (600 nm) of 1 the total cell volume is around 3.6 µl for all conditions tested. Third, we demonstrate that the cell number in a sample can be determined on the basis of the sample's optical density and the cells' growth rate. The data presented will allow for conversion of E. coli measurement data normalized to optical density into volumetric cellular concentrations and copy numbers per cell - two important parameters for systems biology model development. | |
| 540 | |a Creative Commons Attribution 3.0 Unported |u http://creativecommons.org/licenses/by/3.0 |2 ethresearch | ||
| 700 | 1 | |a Heinemann |D Matthias |e joint author | |
| 773 | 0 | |t PLoS ONE |d Lawrence, KS : Public Library of Science |g 6 (7), p. e23126 |x 1932-6203 | |
| 856 | 4 | 0 | |u http://hdl.handle.net/20.500.11850/276902 |q text/html |z WWW-Backlink auf das Repository (Open access) |
| 908 | |D 1 |a Journal Article |2 ethresearch | ||
| 950 | |B ETHRESEARCH |P 856 |E 40 |u http://hdl.handle.net/20.500.11850/276902 |q text/html |z WWW-Backlink auf das Repository (Open access) | ||
| 950 | |B ETHRESEARCH |P 100 |E 1- |a Volkmer |D Benjamin | ||
| 950 | |B ETHRESEARCH |P 700 |E 1- |a Heinemann |D Matthias |e joint author | ||
| 950 | |B ETHRESEARCH |P 773 |E 0- |t PLoS ONE |d Lawrence, KS : Public Library of Science |g 6 (7), p. e23126 |x 1932-6203 | ||
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 949 | |B ETHRESEARCH |F ETHRESEARCH |b ETHRESEARCH |j Journal Article |c Open access | ||