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Originally published as Biophys J. BioFAST on April 4, 2008.
doi:10.1529/biophysj.107.124677
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Biophysical Journal 95:1063-1074 (2008)
© 2008 The Biophysical Society

Estimation of the Source-by-Source Effect of Autorepression on Genetic Noise

Hiroyuki Okano *, Tetsuya J. Kobayashi {dagger} {ddagger}, Hirokazu Tozaki § and Hidenori Kimura *

* Bio-Mimetic Control Research Center, The Institute of Physical and Chemical Research, RIKEN, Nagoya, Japan; {dagger} Research Fellow of the Japan Society for the Promotion of Science, {ddagger} Center for Developmental Biology, The Institute of Physical and Chemical Research, RIKEN, Kobe, Japan; and § ERATO Aihara Complexity Modeling Project, JST, Tokyo, Japan

Correspondence: Address reprint requests to H. Okano, Tel.: 81-52-736-5861; E-mail: okano{at}bmc.riken.jp.

Transcriptional autorepression has been thought to be one of the simplest control circuits to attenuate fluctuations in gene expression. Here, we explored the effect of autorepression on fluctuations from different noise sources. We theoretically represent the fluctuations in the copy number of proteins as the sum of several terms, each of which is related to a specific noise source and expressed as the product of the source-specific fluctuations under no autorepression (path gain) and the effect of autorepression on them (loop gain). Inspection of each term demonstrates the source-independent noise-attenuating effect of autorepression as well as its source-dependent efficiency. Our experiments using a synthetic autorepression module revealed that autorepression attenuates fluctuations of various noise compositions. These findings indicate that the noise-attenuating effect of autorepression is robust against variation in noise compositions. We also experimentally estimated the loop gain for mRNA noise, demonstrating that loop gains are measurable parameters. Decomposition of fluctuations followed by experimental estimation of path and loop gains would help us to understand the noise-related feature of design principles underlying loop-containing biological networks.







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Copyright © 2008 by the Biophysical Society.