Inferring causality in biological oscillators

Author:

Tyler Jonathan12,Forger Daniel13,Kim Jae Kyoung45ORCID

Affiliation:

1. Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA

2. Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA

3. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA

4. Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea

5. Biomedical Mathematics Group, Institute for Basic Science, Daejeon 34126, Republic of Korea

Abstract

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 and implementation 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 https://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.

Funder

National Institutes of Health Training Grant

Institute for Basic Science

Samsung Science and Technology Foundation

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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