Information theory and network physiology are rapidly converging fields that offer powerful frameworks for unraveling the complexities of living systems. At the heart of network physiology lies the understanding that physiological functions emerge from dynamic and coordinated interactions among distributed organ systems, tissues, and cells. However, the intricate, nonlinear nature of these interactions often eludes traditional analytical approaches. Recent advances in information theory—encompassing measures such as entropy, mutual information, transfer entropy, and complexity—provide novel tools for quantifying interdependence, directionality, and redundancy in multiscale physiological signals. Despite these promising developments, significant questions remain about the validity, interpretability, and practical implementation of information-theoretic measures when applied to physiological networks. Ongoing debates continue around how to account for nonstationarity, noise, and the high dimensionality inherent to biological data, as well as how to link quantitative descriptors to mechanistic insights and clinical outcomes.

This Research Topic aims to interface and integrate concepts from information theory with network physiology to advance the modeling and interpretation of nonlinear interactions among physiological systems. The main objective is to foster the development and application of information-theoretic frameworks that can extract meaningful patterns and governing principles from complex biological dynamics. We encourage studies that apply or extend these methods to elucidate organization, adaptability, resilience, and changes in network connectivity under various physiological and pathological states. A central goal is to highlight how these approaches can reveal hidden dependencies and causal relations, thereby opening new avenues for understanding whole-system behavior in health and disease.

Within the boundaries of modeling nonlinear interactions in physiological systems using information-theoretic approaches, this Research Topic invites contributions that explore, but are not limited to, the following themes:
• Quantitative characterization of physiological networks using information-theoretic measures
• Detection of nonlinear and directional dependencies among physiological signals
• Integration of information theory with computational and statistical models in network physiology
• Novel algorithms for multiscale and multiplexed physiological data analysis including modelling and simulation research
• Applications to health, disease, and physiological adaptation or synchronization
• Methodological advancements addressing challenges such as noise, dimensionality, and interpretability

Article types welcomed include original research, reviews, methods, perspectives, and data reports.

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Information Theory Meets Network Physiology: Modeling Nonlinear Interactions in Complex Systems