国際学会のフォーマットに収まる 簡潔・高密度 の文章。

🔵 International Conference Version (Concise)

Title:
A Structural Approach to Allostery: A Mathematical Journey Through Tensors, Distances, and Dynamic Networks

Abstract (Short Version):
This work originates from a cross-disciplinary attempt to resolve discrepancies observed in clinical outcomes, learning processes, and human movement—phenomena that classical statistics could not adequately explain.
To address these limitations, we revisited foundational concepts in quantum mechanics (Dirac’s formulation), statistical thermodynamics, pattern recognition, Fourier analysis, and molecular dynamics (MD).

Through this process, we rediscovered that biological and behavioral systems are best understood not as static entities but as dynamic tensor fields.
This led to the development of Allosteric Engineering (AE), a framework that represents allosteric communication as the propagation of structural deformation across a Cα–Cα distance network, integrating both local geometry and nonlocal energetic influence.

AE provides a unified mathematical language—based on tensors, matrices, distances, and phase transitions—capable of describing structure and information flow across molecules, human movement, and organizational behavior.
This presentation outlines the mathematical foundations of AE and demonstrates its application to the JAK1 JH1 kinase domain, revealing reproducible allosteric pathways emerging from distance-network topology.

短い“口頭発表の導入用”

Conference Opening Statement:
Our research began with a simple question: Why do systems with similar conditions produce different outcomes?
To answer this, we revisited the foundations of statistics, quantum mechanics, thermodynamics, pattern recognition, Fourier analysis, and MD.
Across these fields, we rediscovered a common structure: systems behave as tensors and matrices evolving over time.
This insight led to Allosteric Engineering, a method that models allostery as tensor propagation through a distance network.
Today, I will show how this framework reveals hidden allosteric pathways in the JAK1 JH1 kinase domain.