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

  • Sosuke Ito

Part of the Springer Theses book series (Springer Theses)

Table of contents

About this book

Introduction

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.

Keywords

Bayesian network Causal Networks Information thermodynamics Maxwell’s demon Transfer entropy information processing in living systems

Authors and affiliations

  • Sosuke Ito
    • 1
  1. 1.Department of PhysicsThe University of TokyoTokyoJapan

Bibliographic information

  • DOI https://doi.org/10.1007/978-981-10-1664-6
  • Copyright Information Springer Science+Business Media Singapore 2016
  • Publisher Name Springer, Singapore
  • eBook Packages Physics and Astronomy
  • Print ISBN 978-981-10-1662-2
  • Online ISBN 978-981-10-1664-6
  • Series Print ISSN 2190-5053
  • Series Online ISSN 2190-5061
  • About this book
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