Dynamic Relativistic Framework for Genetic Editing in Oncology

Project Description (OSF) This project presents a conceptual and in silico framework that addresses a critical source of variability in genetic editing outcomes in oncology: the dynamic state of the tumor system at the time of intervention. While CRISPR-based technologies enable precise molecular editing, their clinical and preclinical results remain inconsistent, with frequent emergence of resistance and limited durability. This work proposes that these limitations arise not primarily from the genetic tools themselves, but from their application within dynamically unstable tumor systems. To address this gap, the project introduces a Dynamic Relativistic Framework (ΔR) designed to characterize, normalize, and select tumor dynamic regimes prior to genetic interventions. By integrating measures of adaptive instability and resistance dynamics, the framework provides a decisional layer that distinguishes between interventions that act as transient evolutionary perturbations and those that achieve stable structural reorganization of the system. Through rigorous conceptual development and in silico modeling, the project demonstrates that incorporating relativistic differentials can produce a multiplicative improvement in the functional fixation, durability, and robustness of genetic edits, without modifying the underlying CRISPR technology. This work is intended as an open, falsifiable, and extensible proposal aimed at bridging molecular biology, dynamic systems theory, translational oncology, and pharmacological strategy. It provides a formal basis for designing more effective experimental validations and for reducing late-stage failure risk in advanced therapeutic development.