The Metabolic Organ Clock: A Computable Framework Linking Cell-Lineage Architecture to Emergent Organ Bioenergetics

Cell-type atlases now catalogue hundreds of distinct human cell identities across development, each annotated with mitochondrial abundance, dominant fuel pathway, and lineage of origin, yet no computable model connects the topology of the lineage tree to the metabolic rate the resulting organs display in the adult body. Using a curated 396-node human cell-lineage tree spanning the zygote to terminal somatic identities, we test whether organ-level standard metabolic rate (SMR) is better predicted by developmental time (lineage depth) or by terminal fate identity (anatomical compartment). We find that lineage depth explains essentially none of the variance in a cell's metabolic tier (r = 0.11, R2 approximately 1.2%), whereas compartment identity explains roughly three-quarters of it (approximately 75%), and that the mean mitochondrial volume fraction of an organ's constituent terminal cell types tracks the organ's classical literature-derived SMR with r = 0.90 across five canonical reference-man organ groups. These results motivate a five-layer computable framework: lineage as a formal tree grammar, information-theoretic structure of the tree, metabolic switches as runtime operators, compilation to organ-level energetic networks, and execution as organism-level bioenergetics. We also introduce the metabolic commitment-horizon model: the hypothesis that a cell's metabolic tier is fixed at a discrete lineage-commitment event and thereafter held constant, rather than accumulated continuously with differentiation time. In a worked held-out test (5-fold cross-validation, n = 396), a nearest-commitment-ancestor predictor beat a global-mean null (MAE 0.752 vs 0.809, p = 0.015) but was not significantly better than a lineage-depth regression (delta MAE = -0.042, 95% CI -0.098 to +0.012, p = 0.175), so we report the commitment-horizon model as supported descriptively but not yet discriminated predictively on these data. We connect this within-species result to the classical, cross-species rate-of-living hypothesis, treat insulin resistance as an acquired perturbation of the clock, survey machine-learning and deep-learning tools that could extend the framework on primary single-cell data, and close with eight testable predictions and falsification conditions.