Identifying dynamical systems with bifurcations from noisy partial observation

TL;DR

This paper introduces a machine learning method to identify low-dimensional dynamical systems with bifurcations from noisy, partial observations, effectively capturing bifurcation structures in biological models.

Contribution

It presents a novel statistical machine-learning approach for modeling bifurcations from noisy, partial data, applicable to biological systems.

Findings

Successfully inferred bifurcation structures from noisy data

Robustly captured bifurcation behavior in cell-cycle models

Demonstrated effectiveness on artificial data simulating biological systems

Abstract

Dynamical systems are used to model a variety of phenomena in which the bifurcation structure is a fundamental characteristic. Here we propose a statistical machine-learning approach to derive lowdimensional models that automatically integrate information in noisy time-series data from partial observations. The method is tested using artificial data generated from two cell-cycle control system models that exhibit different bifurcations, and the learned systems are shown to robustly inherit the bifurcation structure.