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Inferring time derivatives including cell growth rates using Gaussian processes.
Peter S Swain1, Keiran Stevenson1, Allen Leary2
1SynthSys-Synthetic and Systems Biology, School of Biological Sciences, University of Edinburgh, Mayfield Road, Edinburgh EH9 3BF, UK.
Nature Communications
|December 13, 2016
Summary
This study introduces a new Gaussian process method to accurately calculate time derivatives from data. The approach estimates errors and allows for interpolation, proving useful across various scientific fields.
Area of Science:
- Computational Biology
- Biophysics
- Data Science
Background:
- Time derivatives of variables are crucial in dynamic systems.
- Existing methods for inferring derivatives from time-series data have limitations.
Purpose of the Study:
- To develop a novel non-parametric method for inferring first and second time derivatives from time-series data.
- To provide accurate error estimation for both derivative inference and derived statistics.
Main Methods:
- Utilized Gaussian processes for non-parametric inference of time derivatives.
- Applied the method to time-series data to estimate rates and accelerations.
Main Results:
- The method accurately infers time derivatives, comparable to existing techniques.
- Demonstrated advantages in error estimation for inference and summary statistics like lag times.
- Enabled interpolation with associated error estimation.
Conclusions:
- The Gaussian process-based method offers a robust and versatile tool for analyzing dynamic processes.
- Broad applicability demonstrated in microbial cell growth, amyloid fibril assembly, and cell division dynamics.
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