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 .
Chaos, or exponential sensitivity to small perturbations , appears everywhere in nature. Moreover, chaos is predicted to play diverse functional roles in living systems . A method for detecting chaos from empirical measurements should therefore be a key component of the biologist's toolkit. But, classic chaos-detection tools are highly sensitive to measurement noise and break down for common edge cases, making it difficult to detect chaos in domains, like biology, where measurements are noisy. However, newer tools promise to overcome these limitations. Here, we combine several such tools into an automated processing pipeline, and show that our pipeline can detect the presence (or absence) of chaos in noisy recordings, even for difficult edge cases. As a first-pass application of our pipeline, we show that heart rate variability is not chaotic as some have proposed , and instead reflects a stochastic process in both health and disease. Our tool is easy-to-use and freely available .
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Dynamical chaos - i.e.,
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- 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
, appears everywhere in nature. Moreover, chaos is predicted to play diverse functional roles in living systems
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feature
component
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, which has made
and break down for common edge cases, making
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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
detect chaos in domains, like biology, where measurements are noisy. However, newer tools promise to overcome these limitations. Here, we combine several such tools into an
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which can accurately assess the presence and degree
and show that our pipeline can detect the presence (or absence)
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a diverse range of measurements, biological or otherwise, even when those measurements are noisy , and
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method, we provide evidence in favor of the hypothesis
pipeline, we show
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as some have proposed
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nonlinear
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, fully automated,
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online