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Peer-reviewedCC BY 4.0Yearbook of Medical Informatics

Modeling Biological Systems: Trends in Biosystem Identification and Biosignal Coupling

  • Kyriaki Kostoglou1
  • Gernot R. Müller-Putz2
  1. 1Institute of Neural Engineering, Graz University of Technology Graz Austria
  2. 2BioTechMed Graz Austria
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Abstract

Objectives: Biosystem identification is a multidisciplinary approach to modeling and understanding the dynamic behavior of biological systems through the analysis of physiological signals. Complementary to biosystem identification, biosignal coupling quantifies interactions between different physiological signals, revealing important dependencies in systems like neuro-cardio-respiratory interactions and brain network dynamics. This commentary paper explores current trends and emerging methodologies in these areas.

Methods: We present a survey of advanced time-varying and nonlinear modeling approaches, explore sophisticated techniques for biosignal coupling, and highlight the integration of machine learning and deep learning frameworks within biosystem identification. We also discuss applications in real-time monitoring, personalized medicine, and clinical diagnostics, illustrating the potential of these methodologies in both biomedical research and healthcare.

Conclusion: Despite significant advancements in the field, the ability to model complex physiological systems and extract metrics with direct clinical relevance remains an ongoing challenge and area of active research. Continued progress in computational methods and data acquisition technologies hold great promise for broadening the scope and enhancing the impact of biosystem identification.

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Cite

BibTeXRIS
BibTeX
@article{kostoglou2026modeling,
  title = {{Modeling Biological Systems: Trends in Biosystem Identification and Biosignal Coupling}},
  author = {Kyriaki Kostoglou and Gernot R. Müller-Putz},
  journal = {Yearbook of Medical Informatics},
  year = {2026},
  month = nov,
  doi = {10.1055/s-0046-1816599},
  url = {https://doi.org/10.1055/s-0046-1816599},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Modeling Biological Systems: Trends in Biosystem Identification and Biosignal Coupling
AU  - Kyriaki Kostoglou
AU  - Gernot R. Müller-Putz
PY  - 2026
DA  - 2026/11/03
JO  - Yearbook of Medical Informatics
DO  - 10.1055/s-0046-1816599
UR  - https://doi.org/10.1055/s-0046-1816599
AB  - Objectives: Biosystem identification is a multidisciplinary approach to modeling and understanding the dynamic behavior of biological systems through the analysis of physiological signals. Complementary to biosystem identification, biosignal coupling quantifies interactions between different physiological signals, revealing important dependencies in systems like neuro-cardio-respiratory interactions and brain network dynamics. This commentary paper explores current trends and emerging methodologies in these areas.  Methods: We present a survey of advanced time-varying and nonlinear modeling approaches, explore sophisticated techniques for biosignal coupling, and highlight the integration of machine learning and deep learning frameworks within biosystem identification. We also discuss applications in real-time monitoring, personalized medicine, and clinical diagnostics, illustrating the potential of these methodologies in both biomedical research and healthcare.  Conclusion: Despite significant advancements in the field, the ability to model complex physiological systems and extract metrics with direct clinical relevance remains an ongoing challenge and area of active research. Continued progress in computational methods and data acquisition technologies hold great promise for broadening the scope and enhancing the impact of biosystem identification.
SN  - 0943-4747
SN  - 2364-0502
ER  -