Contemporary psychiatric models increasingly utilize systems biology to supplement symptom-based nosology. While localized neurotransmitter hypotheses remain foundational, they often lack the spatiotemporal resolution to capture the non-linear, multi-scale dysfunctions associated with severe mental illnesses. This study introduces a multi-scale computational framework conceptualizing Major Depressive Disorder (MDD) as an emergent property of systemic bioenergetic instability, quantified via the Bioenergetic Stability Index (ISB). The methodology integrates three distinct computational layers: (1) Temporal Dynamics, utilizing Coupled Ordinary Differential Equations (ODEs) to model astrocytic ATP-dependent Michaelis-Menten kinetics and identifying a Saddle-Node Bifurcation as a critical metabolic boundary; (2) Spatiotemporal Propagation, extending the ODE framework into a Partial Differential Equation (PDE) system utilizing a Graph Laplacian operator mapped onto the 148-node Destrieux Atlas and empirical AHBA transcriptomic gradients; and (3) Causal Inference, employing bidirectional Mendelian Randomization (MR) to parameterize peripheral allostatic loads—specifically mucosal inflammation associated with Gastroesophageal Reflux Disease (GERD) and its systemic transduction via vagal afferent signaling. An in silico Pan-Ancestry simulation (N=30,000) indicates that under borderline environmental stress parameters, the modeled East Asian (EAS) cohort exhibits a 78.4% projected incidence of simulated bioenergetic collapse, potentially reflecting specific Gene-Environment (GxE) interactions. These findings suggest that uniform diagnostic thresholds may underestimate metabolic vulnerability in EAS populations. This theoretical framework provides a mathematically tractable blueprint for future precision psychiatry and necessitates prospective validation utilizing in vivo high-resolution 1H-MRS imaging.
Cefiyana Cefiyana (Sat,) studied this question.