Dynamical chaos - i.e., exponential sensitivity to small perturbations - is thought to play a key functional role in a diverse range of biological systems , from underpinning flexible information processing in the brain to boosting cellular survival rates through the promotion of heterogeneous gene expression. A method for detecting chaos from empirical measurements should therefore be a key feature of the biologist's toolkit. But, classic chaos-detection tools are highly sensitive to measurement noise, which has made it difficult to distinguish between stochastic, periodic, and chaotic biological processes. To aid in the experimental study of chaotic systems, we here combine several new mathematical tools into a fully automated processing pipeline, which can accurately assess the presence and degree of chaos in a diverse range of measurements, biological or otherwise, even when those measurements are noisy, and even for difficult edge cases.  Our tool is  freely available online.
Dynamical chaos - i.e., exponential sensitivity to small perturbations - is thought to play a key functional role in a diverse range of complex biological processes , from underpinning flexible information processing in the brain to boosting cellular survival rates through the promotion of heterogeneous gene expression. A method for detecting chaos from empirical measurements should therefore be a key feature of the biologist's toolkit. But, classic chaos-detection tools are highly sensitive to measurement noise, which has made it difficult to distinguish between stochastic, periodic, and chaotic biological processes. To aid in the experimental study of chaotic systems, we here combine several new mathematical tools into a fully automated processing pipeline, which can accurately assess the presence and degree of chaos in a diverse range of measurements, biological or otherwise, even when those measurements are noisy, and even for difficult edge cases. As a first-pass application of our method, we provide evidence in favor of the hypothesis that heart rate variability is not chaotic, and instead reflects a nonlinear stochastic process in both health and disease. Our tool is easy-to-use, fully automated, and freely available online.
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biological systems
complex biological processes
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As a first-pass application of our method, we provide evidence in favor of the hypothesis that heart rate variability is not chaotic, and instead reflects a nonlinear stochastic process in both health and disease.
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easy-to-use, fully automated, and