ArticleiScience2024
From biological data to oscillator models using SINDy.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- From observable fermentation data to hidden cell states: A modeling study of a mixotrophic Clostridium coculture under perfusion mode.PLoS computational biology · 2026Article
- Enhancing generalizability of model discovery across parameter space with multi-experiment equation learning for biological systems.PLoS computational biology · 2026Article
- Data-driven identification of biological systems using multi-scale analysis.PLoS computational biology · 2025Article
- Modeling Moose-Wolf interactions in Isle Royale National Park using sparseidentification of nonlinear dynamics.Scientific reports · 2025Article
- Implicit Runge-Kutta based sparse identification of governing equations in biologically motivated systems.Scientific reports · 2025Article
- Weak-form inference for hybrid dynamical systems in ecology.Journal of the Royal Society, Interface · 2024Article
- From biological data to oscillator models using SINDy.iScience · 2024Article
- Data-Driven Identification of Rational Nonlinear Dynamics in Biochemical Networks via an Implicit Singular Value Decomposition Based Framework.IET systems biologyArticle
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Authors and funding
2 authors.
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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 SINDy's 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.