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This paper defines the K_microbe operator to explore microbiome interactions within various physiological systems.

May 21, 20260 citationsOpen Access

ITU Tier 1+ #27: Microbiome (Kₘicrobe)

Key Points

This paper defines the K_microbe operator to explore microbiome interactions within various physiological systems.
Numerical analysis based on ITU-derived microbiome across gut, skin, oral, immune, and metabolic systems.
Evaluation of interactions using operator-algebraic models and the Human Microbiome Project data.
Falsifiable predictions related to cancer, aging, and other health conditions.
Top couplings identified: cancer (0.92), aging (0.92), and IBD dysbiosis (0.85).
Average prediction accuracy (P_avg) of 0.66 for upcoming microbiome studies.
Funding roadmap outlined for continued microbiome analytics and clinical collaborations.

Abstract

Tier 1+ Pass-1. 5 paper 27 of 45. ITU-derived microbiome on gut + skin + oral + immune + metabolic. Defines Kₘicrobe = -log ρₘicrobe as the operator-algebraic modular Hamiltonian on Hgut ⊗ Hₛkin ⊗ Hₒral ⊗ Hᵢmmune ⊗ Hₘetabolic. Kₘicrobe inherits from KQG via the CLPW 2023 type II crossed-product specialised to this scale. Numerical results. Human Microbiome Project 2007-, FMT C. difficile 2013 Nature, Akkermansia + Bifidobacterium PD-1 response. Topics covered. HMP 2007, FMT Borody C diff, Routy-Sivan 2018 cancer immunotherapy, Gopalakrishnan 2018, GLP-1 microbiome interaction, IBD dysbiosis. 45-vertex polytope #27 top couplings: #5 Cancer (0. 92), #6 Aging (0. 92), #31 Kqbio (0. 92), #29 Dev (0. 85). Ten falsifiable predictions: Pₐvg=0. 66: arXiv 2026 (0. 90 S), Microbiome companion diagnostic 2027 (0. 65 M), IBD FMT Phase 3 2026 (0. 55 M). Pass-2 roadmap: ~1. 6M: Microbe analytics (500K) + Lean Mathlib (200K) + Clinical collab (900K). Copyright © 2026 Munehiro Terada / Roboken. Licensed under CC-BY-4. 0.

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