An improved method for inference of piecewise linear systems by detecting jumps using derivative estimation

Abstract

Inference of dynamical systems using piecewise linear models is a promising active research area. Most of the investigations in this field have been stimulated by the research in functional genomics. In this article we study the inference problem in piecewise linear systems. We propose first identifying the state transitions by detecting the jumps of the derivative estimates, then finding the guard conditions of the state transitions (thresholds) from the values of the state variables at the state transition time and finally using the conventional gene regulatory network inference methods to infer the regulatory relations. This approach does not require a priori information or assumption on the guard conditions and provides robustness to environmental or measurement noise underlined by the used jump detection filter. We discuss the particular problems where the suggested method can improve the efficiency and demonstrate the results on a comparative basis.