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

TL;DR

This paper applies the optimal causation entropy principle to infer the coupling structure of a synthetic biological system, demonstrating improved accuracy with more data and higher sampling frequency, while reducing false positives.

Contribution

It introduces a novel application of the oCSE principle combined with aggregation and removal algorithms for coupling inference in complex systems.

Findings

Inference accuracy improves with more data.

Higher sampling frequency enhances coupling detection.

Method is robust against false positives.

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.