Summary

Proceedings of the 2013 International Symposium on Nonlinear Theory and its Applications

2013

Session Number:C1L-B

Session:

Number:348

Information Flow and Processing in Biochemical Reaction Networks

Tetsuya J. Kobayashi,  

pp.348-348

Publication Date:

Online ISSN:2188-5079

DOI:10.15248/proc.2.348

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Transmission and processing of information are crucial to realize robust cellular functions[1]. In cellular decision-making, for example, a cell has to obtain information on the current state of environment by using its stochastic sensing systems such as receptors[2]. Efficient Information flow enables a cell to respond faithfully to the environmental change, which, in turn, leads to increase of fitness advantage[3]. It is easily expected that biochemical networks with specific structures may have higher efficiency in information flow than others.
In this work, I demonstrate that inference theory and information theory can be employed to theoretically predict the network structures and dynamic properties relevant for efficiency of information flow[4, 5]. From this result, I show that efficient information flow in terms of statistics is linked to pure-noise-induced signal amplification that is different from noise-induced signal enhancement in stochastic resonance[6]. The relation between biochemical information processing and information flow will be also discussed.

[1] D. Bray, “Wet ware: A computer in every living cell, ” Yale Univ. Press, (2009).

[2] T. J. Perkins, & P. S. Swain, “Strategies for cellular decision-making,” Mol. Syst. Biol., vol. 5, p. 326, (2009).

[3] T. J. Kobayashi & A. Kamimura, Adv. Exp. Med. Biol., vol. 736, p.275, (2012).

[4] T. J. Kobayashi, Phys. Rev. Lett., vol .104, p. 228104, (2010).

[5] T. J. Kobayashi, & A. Kamimura, Phys. Biol., vol. 8, p. 055007, (2011).

[6] T. J. Kobayashi, Phys. Rev. Lett., vol. 106, p. 228101, (2011).