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bioRxiv · 10.1101/2023.08.25.554817

Data-driven discovery of oscillator models using SINDy: Towards the application on experimental data in biology

Prokop, B.·Gelens, L.
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Abstract

Periodic changes in the concentration or activity of different molecules regulate vital cellular processes such as cell division and circadian rhythms. Developing mathematical models is essential to better understand the mechanisms underlying these oscillations. Recent data-driven methods like SINDy have fundamentally changed model identification, yet their application to experimental biological data remains limited. This study investigates SINDys constraints by directly applying it to biological oscillatory data. We identify insufficient resolution, noise, dimensionality, and limited prior knowledge as primary limitations. Using various generic oscillator models of different complexity and/or dimensionality, we systematically analyze these factors. We then propose a comprehensive guide for inferring models from biological data, addressing these challenges step by step. Our approach is validated using glycolytic oscillation data from yeast.

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Prokop, B., Gelens, L.. 2023-08-27. Data-driven discovery of oscillator models using SINDy: Towards the application on experimental data in biology. https://doi.org/10.1101/2023.08.25.554817

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