Information Thermodynamics on Causal Networks and its Application to Biochemical Signal Transduction

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Springer, Jul 16, 2016 - Science - 133 pages
In this book the author presents a general formalism of nonequilibrium thermodynamics with complex information flows induced by interactions among multiple fluctuating systems.
The author has generalized stochastic thermodynamics with information by using a graphical theory. Characterizing nonequilibrium dynamics by causal networks, he has obtained a novel generalization of the second law of thermodynamics with information that is applicable to quite a broad class of stochastic dynamics such as information transfer between multiple Brownian particles, an autonomous biochemical reaction, and complex dynamics with a time-delayed feedback control. This study can produce further progress in the study of Maxwell’s demon for special cases.
As an application to these results, information transmission and thermodynamic dissipation in biochemical signal transduction are discussed. The findings presented here can open up a novel biophysical approach to understanding information processing in living systems.

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1 Introduction to Information Thermodynamics on Causal Networks
2 Review of Classical Information Theory
3 Stochastic Thermodynamics for Small System
4 Information Thermodynamics Under Feedback Control
5 Bayesian Networks and Causal Networks
6 Information Thermodynamics on Causal Networks
7 Application to Biochemical Signal Transduction
8 Information Thermodynamics as Stochastic Thermodynamics for Small Subsystem
9 Further Applications of Information Thermodynamics on Causal Networks
10 Conclusions
Appendix Curriculum Vitae

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About the author (2016)

Dr. Sosuke Ito

Department of Physics, The University of Tokyo

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