---
title: "Computable Metabolic Organ Clock Linking Cell-Lineage Architecture to Organ Bioenergetics"
id: "biorxiv-9-the-metabolic-organ-clock-a-computable-framework-linking-cell-lineage"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-9-the-metabolic-organ-clock-a-computable-framework-linking-cell-lineage"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.09.29.755490v1?rss=1"
published_at: "2026-09-30T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Computable Metabolic Organ Clock Linking Cell-Lineage Architecture to Organ Bioenergetics
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-9-the-metabolic-organ-clock-a-computable-framework-linking-cell-lineage
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.09.29.755490v1?rss=1)
- **Published At:** 2026-09-30T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The authors curate a 396-node human **cell-lineage tree** from zygote to terminal somatic identities and annotate terminal cell types with mitochondrial abundance, dominant fuel pathway, and lineage origin. - They test whether organ-level standard metabolic rate (**SMR**) is predicted better by **lineage depth** (developmental time) or by terminal fate identity (anatomical **compartment identity**). - Lineage depth explains almost none of the variance in a cell’s **metabolic tier** (r = 0.11, R2 ≈ 1.2%). - Compartment identity explains roughly 75% of variance in metabolic tier across terminal cell types. - The mean **mitochondrial volume fraction** of an organ’s terminal cell types correlates with literature-derived organ SMR across five reference-man organ groups (r = 0.90). - The authors propose a five-layer computable framework: lineage as a formal tree grammar; information-theoretic tree structure; metabolic switches as runtime operators; compilation to organ-level energetic networks; and execution as organism-level bioenergetics. - They introduce the **metabolic commitment-horizon model**: a cell’s metabolic tier is fixed at a discrete lineage-commitment event and remains constant afterward, rather than changing continuously with differentiation time. - In 5-fold cross-validation on the 396-node tree, a nearest-commitment-ancestor predictor outperformed a global-mean null (MAE 0.752 vs 0.809, p = 0.015), but did not significantly outperform a lineage-depth regression (delta MAE = -0.042, 95% CI -0.098 to +0.012, p = 0.175). - The commitment-horizon model is supported descriptively but not yet definitively discriminated by prediction on these data. - The authors link the within-species findings to the classical cross-species rate-of-living hypothesis, interpret insulin resistance as a perturbation of the proposed clock, survey machine-learning tools for future extensions, and provide eight testable predictions and falsification conditions. - Competing interests: both authors are employees of Ainnocence Inc.; no external funding reported.
## Clinical Analysis & Structured Key Points
The Metabolic Organ Clock: A Computable Framework Linking Cell-Lineage Architecture to Emergent Organ Bioenergetics | bioRxiv Skip to main content New Results The Metabolic Organ Clock: A Computable Framework Linking Cell-Lineage Architecture to Emergent Organ Bioenergetics Alejandra Martin Sevilla , Lurong Pan doi: https://doi.org/10.64898/2026.09.29.755490 Alejandra Martin Sevilla Ainnocence Inc. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lurong Pan Ainnocence Inc. Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: lpan{at}ainnocence.com Abstract Info/History Metrics Preview PDF Abstract 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. Competing Interest Statement A.M.S. and L.P. are employees of Ainnocence Inc., a company developing computational tools for drug discovery. No external funding supported this work. Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Back to top Previous Next Posted September 30, 2026. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following The Metabolic Organ Clock: A Computable Framework Linking Cell-Lineage Architecture to Emergent Organ Bioenergetics Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share The Metabolic Organ Clock: A Computable Framework Linking Cell-Lineage Architecture to Emergent Organ Bioenergetics Alejandra Martin Sevilla , Lurong Pan bioRxiv 2026.09.29.755490; doi: https://doi.org/10.64898/2026.09.29.755490 Share This Article: Copy Citation Tools The Metabolic Organ Clock: A Computable Framework Linking Cell-Lineage Architecture to Emergent Organ Bioenergetics Alejandra Martin Sevilla , Lurong Pan bioRxiv 2026.09.29.755490; doi: https://doi.org/10.64898/2026.09.29.755490 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Areas All Articles Animal Behavior and Cognition (8060) Biochemistry (18854) Bioengineering (15004) Bioinformatics (44704) Biophysics (22704) Cancer Biology (19845) Cell Biology (27040) Clinical Trials (138) Developmental Biology (14034) Ecology (21139) Epidemiology (2067) Evolutionary Biology (25543) Genetics (16226) Genomics (23601) Immunology (18797) Microbiology (42719) Molecular Biology (18166) Neuroscience (93961) Paleontology (703) Pathology (2999) Pharmacology and Toxicology (5122) Physiology (8169) Plant Biology (16078) Scientific Communication and Education (2100) Synthetic Biology (4589) Systems Biology (10286) Zoology (2398)
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