Modeling a Nonlinear Biophysical Trend Followed by Long-Memory Equilibrium with Unknown Change Point

TL;DR

This paper introduces a method to identify the change point from a nonlinear trend to a long-memory equilibrium in biological data, improving classification of cell states in ECIS measurements.

Contribution

It proposes a novel approach to simultaneously estimate trend and equilibrium parameters and locate the change point in biological process data.

Findings

Method performs well in simulations.

Provides improved classification of cell states.

Accurately estimates change points and equilibrium features.

Abstract

Measurements of many biological processes are characterized by an initial trend period followed by an equilibrium period. Scientists may wish to quantify features of the two periods, as well as the timing of the change point. Specifically, we are motivated by problems in the study of electrical cell-substrate impedance sensing (ECIS) data. ECIS is a popular new technology which measures cell behavior non-invasively. Previous studies using ECIS data have found that different cell types can be classified by their equilibrium behavior. However, it can be challenging to identify when equilibrium has been reached, and to quantify the relevant features of cells' equilibrium behavior. In this paper, we assume that measurements during the trend period are independent deviations from a smooth nonlinear function of time, and that measurements during the equilibrium period are characterized by a simple long memory model. We propose a method to simultaneously estimate the parameters of the trend and equilibrium processes and locate the change point between the two. We find that this method performs well in simulations and in practice. When applied to ECIS data, it produces estimates of change points and measures of cell equilibrium behavior which offer improved classification of infected and uninfected cells.