期刊
PROGRESS IN BIOPHYSICS & MOLECULAR BIOLOGY
卷 100, 期 1-3, 页码 57-66出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.pbiomolbio.2009.06.003
关键词
Gene expression noise; Stochastic modeling; Transcription factor; Population distribution
资金
- International Graduate School (DFG) [GRK1305, BMBF FRISYS 0313921]
- Excellence Initiative of the German Federal and State Governments
Noise in gene expression, either due to inherent stochasticity or to varying inter- and intracellular environment, can generate significant cell-to-cell variability of protein levels in clonal populations. To quantify the different sources of gene expression noise, several theoretical studies have been performed using either a quasi-stationary approximation for the emerging master equation or employing a time-dependent description, when cell division is taken explicitly into account Here, we give an overview of the different origins of gene expression noise which were found experimentally and introduce the basic stochastic modeling approaches. We extend, and apply a time-dependent description of gene expression noise to experimental data. The analysis shows that the induction level of the transcription factor can be employed to discriminate the noise profiles and their characteristic signatures. On the basis of experimentally measured cell distributions, our simulations suggest that transcription factor binding and promoter activation can be modeled independently of each other with sufficient accuracy. (C) 2009 Elsevier Ltd. All rights reserved.
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