Chronic Disease as Stuck Program Modes of the Candida albicans Biochemical Computer

This paper introduces the stuck-program model, a framework for understanding chronic diseases as phased biological programs that failed to transition. The model derives from the biochemical computer framework (Craddock, Biochemical Computer; Craddock, Saline Oscillation), which describes Candida albicans as a coevolved fungal symbiont operating phased programs within the mammalian host using documented cross-kingdom signaling capabilities. Each program phase employs specific organism capabilities to manage host physiology. When a phase transition signal fails, the capability runs indefinitely, producing a host phenotype that conventional medicine classifies as chronic disease.

A three-gate selection methodology is presented for identifying candidate conditions: (1) documented organism mechanism mapping directly to the disease's central pathology, (2) unexplained persistence in conventional medicine, and (3) demographic or geographic clustering patterns consistent with quorum sensing or colonization density dynamics.

Nine conditions are analyzed in companion papers: type 2 diabetes, anorexia nervosa, irritable bowel syndrome, obesity, Parkinson's disease, endometriosis, autism, addiction, and Alzheimer's disease.

Three cross-disease cellular and host architecture mechanisms are developed: the bifurcated SOX9 rheostat producing exhausted and inflammatory-locked phenotypes within the same SOX9-governed tissue under chronic Candida governance stress; APOE4 as host-symbiont architecture variant calibrated for pathogen-rich environments with cross-cultural cognitive evidence across multiple cohorts; and the cholesterol axis as a substrate-and-signaling junction connecting steroidogenic regulation, membrane biology, and dietary-input modulation.

A unified therapeutic framework combining substrate change, antifungal pressure, exercise, and sustained duration is proposed, along with the identification of organism colonization density as the single measurable variable that should predict disease severity, intervention response, and relapse risk across all conditions.