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Biophys. J. BioFAST: First Published June 23, 2006. doi:10.1529/biophysj.105.070797
© 2006 by the Biophysical Society.

A more recent version of this article appeared on September 15, 2006.
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BIOPHYSICAL THEORY AND MODELING

An optimal number of molecules for signal amplification and discrimination in a chemical cascade

Yoshihiro Morishita 1*, Tetsuya J. Kobayashi 2 and Kazuyuki Aihara 3

1 Kyushu University
2 RIKEN CDB
3 Tokyo University

* To whom correspondence should be addressed. E-mail: ymorishi{at}bio-math10.biology.kyushu-u.ac.jp.

Submitted on July 14, 2005
Revised on August 31, 2005
Accepted on 5 June 2006


  Abstract
Understanding the information processing ability of signal transduction pathways is of great importance because of their crucial roles in triggering various cellular responses. Despite continuing theoretical investigation, some important aspects of signal transduction such as a transient response and its connection to stochasticity originating from a small number of molecules have not yet been well understood. It is, however, through these aspects that unexpected and non-trivial properties of the information processing emerge. In this paper, we analyze the transient behavior of a simple signaling cascade by taking into account the stochasticity originating from the small number of molecules. We identify several properties of the signaling cascade that emerge as a result of the interplay between the stochasticity and transient dynamics of the cascade. We specifically demonstrate that each step of the cascade has an optimal number of signaling molecules at which the average signal amplitude becomes maximal. We further investigate the connection between a finite number of molecules and the ability of the cascade to discriminate between true and error signals, which cannot be inferred from deterministic descriptions. The implications of our results are discussed from both biological and mathematical viewpoints.

Key Words: fluctuation, information processing, intracellular dynamics, optimal number, stochasticity, systems biology




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Copyright © 2006 by the Biophysical Society.