Inferring causality in biological oscillators.
Ontology highlight
ABSTRACT: Motivation
Fundamental to biological study is identifying regulatory interactions. The recent surge in time-series data collection in biology provides a unique opportunity to infer regulations computationally. However, when components oscillate, model-free inference methods, while easily implemented, struggle to distinguish periodic synchrony and causality. Alternatively, model-based methods test the reproducibility of time series given a specific model but require inefficient simulations and have limited applicability.Results
We develop an inference method based on a general model of molecular, neuronal, and ecological oscillatory systems that merges the advantages of both model-based and model-free methods, namely accuracy, broad applicability, and usability. Our method successfully infers the positive and negative regulations within various oscillatory networks, e.g., the repressilator and a network of cofactors at the pS2 promoter, outperforming popular inference methods.Availability
We provide a computational package, ION (Inferring Oscillatory Networks), that users can easily apply to noisy, oscillatory time series to uncover the mechanisms by which diverse systems generate oscillations. Accompanying MATLAB code under a BSD-style license and examples are available at ttps://github.com/Mathbiomed/ION. Additionally, the code is available under a CC-BY 4.0 License at https://doi.org/10.6084/m9.figshare.16431408.v1.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Tyler J
PROVIDER: S-EPMC8696107 | biostudies-literature |
REPOSITORIES: biostudies-literature
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