Applying the OSI Reference Framework as a Conceptual Language for Multi-Layered Biological Systems: A State-Based Medium Communication Hypothesis
This theoretical paper introduces a novel interdisciplinary framework that applies the architectural logic of the Open Systems Interconnection (OSI) reference model as a descriptive language for multi-layered biological regulation. Developed from a systems-engineering perspective, the hypothesis explores whether decentralized, closed-loop physiological control systems coordinate their states through transient physical changes in shared tissue media, rather than relying exclusively on conventional biochemical signaling. The author proposes a four-tier bio-cybernetic hierarchy to model information processing across different physical and temporal scales: Layer 1 (Physical Layer): Parametric modulation of the extracellular matrix (ECM) and interfacial hydration layers. Layer 2 (Data Link Layer): Threshold-based receptor gating (e.g., Piezo and TRPV channels) that filters background noise and converts continuous physical variables into discrete biological events. Layer 3 (Network / Transport Layer): Fast neural timing and neuromuscular coordination (including the olivocerebellar loop and hypothesized high-frequency 400–1000 Hz activity). Layer 4 (Application Layer): Slower intracellular metabolic regulation and resource management involving mTORC1 / AMPK timing. To address the engineering problem of co-channel interference, the paper introduces the concept of frequency-division multiplexing (FDM), proposing that biological systems achieve functional separation through distinct characteristic time scales. The objective of this exploratory work is to provide a structured, falsifiable engineering lexicon to bridge macro-mechanical tissue behavior, neural timing, and metabolic processes, ultimately calling for new empirical and electrophysiological validation methods.
Authors
- Dmytro Taran (ORCID: https://orcid.org/0009-0007-0067-5218)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-09-29
- DOI
- https://doi.org/10.5281/zenodo.23045951
- Primary Topic
- Neuroscience and Neural Engineering
- Type
- preprint