Reconstruction of a Malignant Cellular Attractor from Public Genomic and Nuclear Constraint Statistics

This study presents a non-interventional computational framework for reconstructing malignant cellular organization as a stable attractor in an abstract constraint-based state space. Using aggregated statistical summaries from publicly available cancer datasets—including gene expression, epigenetic topology, chromatin contact structure, nuclear morphology, mutation landscapes, and signaling pathway statistics—we infer a high-level representation of the malignant nuclear state without invoking procedural biology or therapeutic modeling. The analysis demonstrates consistent convergence to a reproducible attractor characterized by zero topology variance and stable global metrics across independent simulation runs. Exploration of adjacent regions in state space reveals lower-stability basins, supporting the interpretation of malignancy as one among multiple possible organizational regimes rather than a singular mechanistic outcome. By operating exclusively on public summary data and enforcing physical, causal, and informational constraints, this work provides a rigorously defined, hypothesis-generating model of malignant organization. The framework offers a systems-level perspective on cancer that complements reductionist approaches while avoiding clinically actionable claims.