04773nam 22007815 450 991025462840332120200701193511.0981-10-1664-X10.1007/978-981-10-1664-6(CKB)3710000000754903(DE-He213)978-981-10-1664-6(MiAaPQ)EBC4595466(PPN)194513386(EXLCZ)99371000000075490320160716d2016 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierInformation Thermodynamics on Causal Networks and its Application to Biochemical Signal Transduction[electronic resource] /by Sosuke Ito1st ed. 2016.Singapore :Springer Singapore :Imprint: Springer,2016.1 online resource (XIII, 133 p. 32 illus., 28 illus. in color.) Springer Theses, Recognizing Outstanding Ph.D. Research,2190-5053"Doctoral Thesis accepted by The University of Tokyo, Tokyo, Japan"--Title page.981-10-1662-3 Includes bibliographical references at the end of each chapters.Introduction to Information Thermodynamics on Causal Networks -- Review of Classical Information Theory -- Stochastic Thermodynamics for Small System -- Information Thermodynamics under Feedback Control -- Bayesian Networks and Causal Networks -- Information Thermodynamics on Causal Networks -- Application to Biochemical Signal Transduction -- Information Thermodynamics as Stochastic Thermodynamics for Small Subsystem -- Further Applications of Information Thermodynamics on Causal Networks -- Conclusions.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.Springer Theses, Recognizing Outstanding Ph.D. Research,2190-5053ThermodynamicsStatistical physicsDynamical systemsQuantum computersSpintronicsBiophysicsBiological physicsPhysicsThermodynamicshttps://scigraph.springernature.com/ontologies/product-market-codes/P21050Complex Systemshttps://scigraph.springernature.com/ontologies/product-market-codes/P33000Quantum Information Technology, Spintronicshttps://scigraph.springernature.com/ontologies/product-market-codes/P31070Biological and Medical Physics, Biophysicshttps://scigraph.springernature.com/ontologies/product-market-codes/P27008Numerical and Computational Physics, Simulationhttps://scigraph.springernature.com/ontologies/product-market-codes/P19021Statistical Physics and Dynamical Systemshttps://scigraph.springernature.com/ontologies/product-market-codes/P19090Thermodynamics.Statistical physics.Dynamical systems.Quantum computers.Spintronics.Biophysics.Biological physics.Physics.Thermodynamics.Complex Systems.Quantum Information Technology, Spintronics.Biological and Medical Physics, Biophysics.Numerical and Computational Physics, Simulation.Statistical Physics and Dynamical Systems.536.7Ito Sosukeauthttp://id.loc.gov/vocabulary/relators/aut816191MiAaPQMiAaPQMiAaPQBOOK9910254628403321Information Thermodynamics on Causal Networks and its Application to Biochemical Signal Transduction1821451UNINA