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Entropy 2014, 16(6), 3416-3433; doi:10.3390/e16063416

Identifying the Coupling Structure in Complex Systems through the Optimal Causation Entropy Principle

Department of Mathematics, Clarkson University, 8 Clarkson Ave, Potsdam, NY 13699-5815, USA
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Received: 28 April 2014 / Revised: 14 May 2014 / Accepted: 9 June 2014 / Published: 20 June 2014
(This article belongs to the Special Issue Information in Dynamical Systems and Complex Systems)
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Abstract

Inferring the coupling structure of complex systems from time series data in general by means of statistical and information-theoretic techniques is a challenging problem in applied science. The reliability of statistical inferences requires the construction of suitable information-theoretic measures that take into account both direct and indirect influences, manifest in the form of information flows, between the components within the system. In this work, we present an application of the optimal causation entropy (oCSE) principle to identify the coupling structure of a synthetic biological system, the repressilator. Specifically, when the system reaches an equilibrium state, we use a stochastic perturbation approach to extract time series data that approximate a linear stochastic process. Then, we present and jointly apply the aggregative discovery and progressive removal algorithms based on the oCSE principle to infer the coupling structure of the system from the measured data. Finally, we show that the success rate of our coupling inferences not only improves with the amount of available data, but it also increases with a higher frequency of sampling and is especially immune to false positives. View Full-Text
Keywords: optimal causation entropy; coupling structure; complex and dynamical systems optimal causation entropy; coupling structure; complex and dynamical systems
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Sun, J.; Cafaro, C.; Bollt, E.M. Identifying the Coupling Structure in Complex Systems through the Optimal Causation Entropy Principle. Entropy 2014, 16, 3416-3433.

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