Inferring causality in biological oscillators

A fundamental goal of biological study is to identify regulatory interactions among components. The recent surge in time-series data collection in biology provides a unique opportunity to infer regulatory networks computationally. However, when the components oscillate, model-free inference methods, while easily implemented, struggle to distinguish periodic synchrony and causality. Alternatively, model-based methods test whether time series are reproducible with a specific model but require inefficient simulations and have limited applicability. Here, 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 of various oscillatory networks, including the repressilator and a network of cofactors of pS2 promoter, outperforming popular inference methods. We also provide a computational package, ION (Inferring Oscillatory Networks), that users can easily apply to noisy, oscillatory time series to decipher the mechanisms by which diverse systems generate oscillations.