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Open AccessArticle

Information Thermodynamics for Time Series of Signal-Response Models

1
Theoretische Biophysik, Humboldt-Universität zu Berlin, Invalidenstraße 42, D-10115 Berlin, Germany
2
Dipartimento di Fisica, Sapienza Università di Roma, 00185 Rome, Italy
3
INFN, Sezione di Roma 1, 00185 Rome, Italy
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(2), 177; https://doi.org/10.3390/e21020177
Received: 17 December 2018 / Revised: 27 January 2019 / Accepted: 11 February 2019 / Published: 14 February 2019
The entropy production in stochastic dynamical systems is linked to the structure of their causal representation in terms of Bayesian networks. Such a connection was formalized for bipartite (or multipartite) systems with an integral fluctuation theorem in [Phys. Rev. Lett. 111, 180603 (2013)]. Here we introduce the information thermodynamics for time series, that are non-bipartite in general, and we show that the link between irreversibility and information can only result from an incomplete causal representation. In particular, we consider a backward transfer entropy lower bound to the conditional time series irreversibility that is induced by the absence of feedback in signal-response models. We study such a relation in a linear signal-response model providing analytical solutions, and in a nonlinear biological model of receptor-ligand systems where the time series irreversibility measures the signaling efficiency. View Full-Text
Keywords: irreversibility; fluctuation theorems; time series; transfer entropy; causal influence irreversibility; fluctuation theorems; time series; transfer entropy; causal influence
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Auconi, A.; Giansanti, A.; Klipp, E. Information Thermodynamics for Time Series of Signal-Response Models. Entropy 2019, 21, 177.

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