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Related Experiment Video

Updated: May 11, 2026

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

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Published on: September 23, 2025

Identifying dynamical systems with bifurcations from noisy partial observation.

Yohei Kondo¹, Kunihiko Kaneko, Shuji Ishihara

¹Graduate School of Arts and Sciences, University of Tokyo, 3-8-1 Komaba, Tokyo 153-8902, Japan. kondo@complex.c.u-tokyo.ac.jp

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|May 18, 2013

Summary

This summary is machine-generated.

We developed a machine-learning method to create simplified models from noisy biological data. This approach robustly identifies key cellular functions, even with incomplete information and experimental errors.

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Area of Science:

Systems Biology
Computational Biology
Machine Learning

Background:

High-dimensional biological systems generate complex, noisy time-series data.
Understanding cellular functions requires robust models despite experimental variability.
Partial observation of biological systems poses challenges for accurate modeling.

Purpose of the Study:

To develop a statistical machine-learning approach for deriving low-dimensional models from noisy, partially observed high-dimensional biological systems.
To characterize biological functions as bifurcation types that are insensitive to system details and experimental errors.
To enable the utilization of quantitative data for understanding cellular phenomena.

Main Methods:

Integrating noisy time-series data from partial observations.
Estimating models using data at different bifurcation parameter values.
Employing a statistical machine-learning framework.

Main Results:

The method successfully derived low-dimensional models from artificial data.
Learned systems robustly inherited bifurcation types from original models.
The approach demonstrated insensitivity to system details and experimental errors.

Conclusions:

The proposed method effectively models complex biological systems using limited, noisy data.
Bifurcation types provide a robust characterization of biological functions.
This approach facilitates quantitative analysis of cellular processes.