A Two-Level Bioenergetic Coherence Model Integrating NAD⁺, ROS, and ATP within the ISHEA Δ±1 Framework

A Two-Level Bioenergetic Coherence Model Integrating NAD⁺, ROS, and ATP within the ISHEA Δ±1 Framework | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Two-Level Bioenergetic Coherence Model Integrating NAD⁺, ROS, and ATP within the ISHEA Δ±1 Framework Carlos J. Perez Pulido This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8899090/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Current bioenergetic models treat ATP as the primary driver of cellular function, with redox systems and reactive oxygen species (ROS) largely framed as auxiliary or pathological byproducts. Here, we propose and validate a two-level bioenergetic coherence model in which NAD⁺-dependent redox balance constitutes the upstream informational layer, while ATP acts as the downstream energetic executor. Using in-silico sensitivity analysis, correlation studies, and an ISHEA Δ ± 1 coherence metric, we demonstrate that NAD⁺ and ROS dominate system stability and coherence, whereas ATP primarily amplifies or executes pre-existing coherent states. This reframing explains why ATP-boosting strategies alone often fail and positions redox coherence as the true control axis of metabolic and functional resilience. Biological sciences/Biophysics Biological sciences/Cell biology Biological sciences/Systems biology Bioenergetic coherence NAD⁺ ATP ROS redox control ISHEA Δ ± 1 systems biology Figures Figure 1 Figure 2 Figure 3 1. Introduction Cellular metabolism is traditionally described as an energy-centered system in which ATP production and consumption govern biological viability. While this framework has been extraordinarily successful, it obscures a critical distinction between energetic capacity and informational coherence. Increasing experimental evidence suggests that redox balance, electron flow, and controlled ROS signaling play a central regulatory role that cannot be reduced to ATP availability alone. We hypothesize that bioenergetic systems operate across two hierarchically distinct but coupled levels (Fig. 1 ): An upstream coherence layer, governed by NAD⁺/NADH redox dynamics and controlled ROS production. A downstream execution layer, where ATP functions as the universal energetic payload enabling biochemical work. To formalize this hypothesis, we embed it within the ISHEA Δ ± 1 adaptive coherence framework, originally developed to model bioadaptive protection and energy–information coupling in extreme environments. 2. Conceptual Framework NAD⁺ functions as a global electron currency linking nutrient availability, mitochondrial respiration, DNA repair, and epigenetic regulation. ROS, within physiological bounds, encode feedback on respiratory flux and system stress. Together, they define the informational state: whether energy production is ordered, synchronized, and sustainable. ATP represents stored free energy enabling work, but critically, ATP does not determine what the system does—it determines whether a coherent instruction can be executed. 3. Methods We performed Sobol (Saltelli) global sensitivity analysis (3,000 samples), Monte Carlo simulations (8,000 runs), and partial correlation analyses using normalized variables with biologically plausible interaction terms. 4. Results Sobol indices identified NAD⁺ as the dominant contributor to Δ variance (first-order: 0.42), followed by ROS (0.28). ATP exhibited substantially lower first-order effects (0.12) but increased importance in interactions (Fig. 2 ). Key finding: Coherence is redox-limited, not ATP-limited. 5. Discussion Our findings explain why ATP supplementation strategies often produce inconsistent benefits: without restoring upstream redox coherence, added energetic substrates merely increase flux through an unstable system. Figure 3 illustrates intervention trajectories in Δ ± 1 phase space. By reframing ROS as a coherence signal rather than pure damage, the model aligns mitochondrial biology with systems control theory. Respiratory efficiency, oxygen utilization, and stress hormones converge on the NAD⁺–ROS axis, making behavioral interventions (breathing, sleep, stress reduction) directly relevant to the informational layer. 6. Conclusion We present a validated two-level bioenergetic model in which NAD⁺-centered redox coherence governs system order, while ATP serves as an execution resource. Integrated within the ISHEA Δ ± 1 framework, this provides a unifying explanation for metabolic resilience, failure modes, and adaptive capacity. Declarations Funding: This work was conducted independently under the ISHEA research framework. No external funding was received for this study. Author Contribution C.J. Perez Pulido conceptualized and designed the study, developed the ISHEA framework bioenergetic coherence model, conducted all in-silico analyses and data validation, prepared all figures and supplementary tables, and wrote the manuscript. The author reviewed, revised, and approved the final version for submission. References Nicholls, D. G., Ferguson, S. J. & Bioenergetics 4. Academic, (2013). Ying, W. NAD⁺/NADH and NADP⁺/NADPH in cellular functions and cell death. Antioxid. Redox Signal. 10 (2), 179–206 (2008). Sena, L. A. & Chandel, N. S. Physiological roles of mitochondrial ROS. Mol. Cell. 48 (2), 158–167 (2012). Wallace, D. C. & Mitochondrial DNA variation Cell. ; 163 (1):33–38. (2015). Murphy, M. P. How mitochondria produce ROS. Biochem. J. 417 (1), 1–13 (2009). Verdin, E. NAD⁺ in aging, metabolism, and neurodegeneration. Science 350 (6265), 1208–1213 (2015). Shadel, G. S. & Horvath, T. L. Mitochondrial ROS signaling. Cell 163 (3), 560–569 (2015). Saltelli, A. et al. Global Sensitivity Analysis: The Primer (Wiley, 2008). Cantó, C. & Auwerx, J. Targeting sirtuin 1. Pharmacol. Rev. 64 (1), 166–187 (2012). Pulido, C. P. et al. ISHEA Adaptive Bio-Gel ∆ + 1 Protective Layer. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx S2TwoLevel.docx S3TwoLevel1.jpg IMG20260130WA0046.jpg IMG20260201WA0021.jpg IMG20260201WA0019.jpg IMG20260130WA00221.jpg Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8899090","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":595858594,"identity":"8a397ade-db58-4df5-93b9-3c011f11b774","order_by":0,"name":"Carlos J. Perez Pulido","email":"data:image/png;base64,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","orcid":"","institution":"ISHEA INSTITUTE","correspondingAuthor":true,"prefix":"","firstName":"Carlos","middleName":"J. Perez","lastName":"Pulido","suffix":""}],"badges":[],"createdAt":"2026-02-17 08:38:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8899090/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8899090/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103304027,"identity":"669d491f-fb8b-4c6d-9655-152ecc1b6adf","added_by":"auto","created_at":"2026-02-24 08:44:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1164387,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTwo-Level Bioenergetic Architecture. Level I (Redox-Informational) controls Level II (Energetic Execution) through causal arrows. NAD⁺, ROS, and ETC flux determine coherence; ATP executes work based on upstream state.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8899090/v1/357705514647963e5d1e7052.png"},{"id":103304018,"identity":"ec071fca-e212-4d48-97aa-17e1acecb95b","added_by":"auto","created_at":"2026-02-24 08:44:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1255158,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eGlobal Sensitivity Analysis. (A) NAD⁺ dominates (0.42 first-order, 0.58 total), ROS second (0.28, 0.45), ATP lower (0.12, 0.31). (B) Interaction matrix shows strongest NAD⁺-ROS coupling.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8899090/v1/e477bc4d55959a19005af320.png"},{"id":103304019,"identity":"d1c11052-165c-4dd4-a05c-b04f99835302","added_by":"auto","created_at":"2026-02-24 08:44:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1439728,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eΔ±1 Phase Space. ATP boosting (blue) produces minimal coherence gain; NAD⁺ restoration (green) and combined interventions (red) drive toward coherent states. 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Introduction","content":"\u003cp\u003eCellular metabolism is traditionally described as an energy-centered system in which ATP production and consumption govern biological viability. While this framework has been extraordinarily successful, it obscures a critical distinction between energetic capacity and informational coherence. Increasing experimental evidence suggests that redox balance, electron flow, and controlled ROS signaling play a central regulatory role that cannot be reduced to ATP availability alone.\u003c/p\u003e \u003cp\u003eWe hypothesize that bioenergetic systems operate across two hierarchically distinct but coupled levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eAn upstream coherence layer, governed by NAD⁺/NADH redox dynamics and controlled ROS production.\u003c/span\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eA downstream execution layer, where ATP functions as the universal energetic payload enabling biochemical work.\u003c/span\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo formalize this hypothesis, we embed it within the ISHEA Δ\u0026thinsp;\u0026plusmn;\u0026thinsp;1 adaptive coherence framework, originally developed to model bioadaptive protection and energy\u0026ndash;information coupling in extreme environments.\u003c/p\u003e"},{"header":"2. Conceptual Framework","content":"\u003cp\u003eNAD⁺ functions as a global electron currency linking nutrient availability, mitochondrial respiration, DNA repair, and epigenetic regulation. ROS, within physiological bounds, encode feedback on respiratory flux and system stress. Together, they define the informational state: whether energy production is ordered, synchronized, and sustainable. ATP represents stored free energy enabling work, but critically, ATP does not determine what the system does\u0026mdash;it determines whether a coherent instruction can be executed.\u003c/p\u003e"},{"header":"3. Methods","content":"\u003cp\u003eWe performed Sobol (Saltelli) global sensitivity analysis (3,000 samples), Monte Carlo simulations (8,000 runs), and partial correlation analyses using normalized variables with biologically plausible interaction terms.\u003c/p\u003e"},{"header":"4. Results","content":"\u003cp\u003eSobol indices identified NAD⁺ as the dominant contributor to Δ variance (first-order: 0.42), followed by ROS (0.28). ATP exhibited substantially lower first-order effects (0.12) but increased importance in interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eKey finding: Coherence is redox-limited, not ATP-limited.\u003c/b\u003e \u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eOur findings explain why ATP supplementation strategies often produce inconsistent benefits: without restoring upstream redox coherence, added energetic substrates merely increase flux through an unstable system. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates intervention trajectories in Δ\u0026thinsp;\u0026plusmn;\u0026thinsp;1 phase space.\u003c/p\u003e \u003cp\u003eBy reframing ROS as a coherence signal rather than pure damage, the model aligns mitochondrial biology with systems control theory. Respiratory efficiency, oxygen utilization, and stress hormones converge on the NAD⁺\u0026ndash;ROS axis, making behavioral interventions (breathing, sleep, stress reduction) directly relevant to the informational layer.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eWe present a validated two-level bioenergetic model in which NAD⁺-centered redox coherence governs system order, while ATP serves as an execution resource. Integrated within the ISHEA Δ\u0026thinsp;\u0026plusmn;\u0026thinsp;1 framework, this provides a unifying explanation for metabolic resilience, failure modes, and adaptive capacity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis work was conducted independently under the ISHEA research framework. No external funding was received for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eC.J. Perez Pulido conceptualized and designed the study, developed the ISHEA framework bioenergetic coherence model, conducted all in-silico analyses and data validation, prepared all figures and supplementary tables, and wrote the manuscript. The author reviewed, revised, and approved the final version for submission.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNicholls, D. G., Ferguson, S. J. \u0026amp; Bioenergetics 4. Academic, (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYing, W. NAD⁺/NADH and NADP⁺/NADPH in cellular functions and cell death. \u003cem\u003eAntioxid. Redox Signal.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e (2), 179\u0026ndash;206 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSena, L. A. \u0026amp; Chandel, N. S. Physiological roles of mitochondrial ROS. \u003cem\u003eMol. Cell.\u003c/em\u003e \u003cb\u003e48\u003c/b\u003e (2), 158\u0026ndash;167 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWallace, D. C. \u0026amp; Mitochondrial \u003cem\u003eDNA variation Cell.\u003c/em\u003e ;\u003cb\u003e163\u003c/b\u003e(1):33\u0026ndash;38. (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurphy, M. P. How mitochondria produce ROS. \u003cem\u003eBiochem. J.\u003c/em\u003e \u003cb\u003e417\u003c/b\u003e (1), 1\u0026ndash;13 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerdin, E. NAD⁺ in aging, metabolism, and neurodegeneration. \u003cem\u003eScience\u003c/em\u003e \u003cb\u003e350\u003c/b\u003e (6265), 1208\u0026ndash;1213 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShadel, G. S. \u0026amp; Horvath, T. L. Mitochondrial ROS signaling. \u003cem\u003eCell\u003c/em\u003e \u003cb\u003e163\u003c/b\u003e (3), 560\u0026ndash;569 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaltelli, A. et al. \u003cem\u003eGlobal Sensitivity Analysis: The Primer\u003c/em\u003e (Wiley, 2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCant\u0026oacute;, C. \u0026amp; Auwerx, J. Targeting sirtuin 1. \u003cem\u003ePharmacol. Rev.\u003c/em\u003e \u003cb\u003e64\u003c/b\u003e (1), 166\u0026ndash;187 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePulido, C. P. et al. ISHEA Adaptive Bio-Gel ∆\u0026thinsp;+\u0026thinsp;1 Protective Layer.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bioenergetic coherence, NAD⁺, ATP, ROS, redox control, ISHEA Δ ± 1, systems biology","lastPublishedDoi":"10.21203/rs.3.rs-8899090/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8899090/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCurrent bioenergetic models treat ATP as the primary driver of cellular function, with redox systems and reactive oxygen species (ROS) largely framed as auxiliary or pathological byproducts. Here, we propose and validate a two-level bioenergetic coherence model in which NAD⁺-dependent redox balance constitutes the upstream informational layer, while ATP acts as the downstream energetic executor. Using in-silico sensitivity analysis, correlation studies, and an ISHEA Δ\u0026thinsp;\u0026plusmn;\u0026thinsp;1 coherence metric, we demonstrate that NAD⁺ and ROS dominate system stability and coherence, whereas ATP primarily amplifies or executes pre-existing coherent states. This reframing explains why ATP-boosting strategies alone often fail and positions redox coherence as the true control axis of metabolic and functional resilience.\u003c/p\u003e","manuscriptTitle":"A Two-Level Bioenergetic Coherence Model Integrating NAD⁺, ROS, and ATP within the ISHEA Δ±1 Framework","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-24 08:44:17","doi":"10.21203/rs.3.rs-8899090/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"312bbef4-790f-4ba9-979e-4da440e34362","owner":[],"postedDate":"February 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63403908,"name":"Biological sciences/Biophysics"},{"id":63403909,"name":"Biological sciences/Cell biology"},{"id":63403910,"name":"Biological sciences/Systems biology"}],"tags":[],"updatedAt":"2026-05-04T03:55:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-24 08:44:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8899090","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8899090","identity":"rs-8899090","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}