Two-level bioenergetic coherence model: NAD+, ROS and ATP in the ISHEA Δ±1 framework

Supplementary Material: Energy and Proteic Periodic Tables

1. Energy Periodic Table

Purpose:
Organizes and visualizes the energy components of biological and physical systems according to their coherence potential, functional role, and interactions with other elements.

Axes:

X-axis (Energy type):
Metabolic, electrical, mechanical, thermal, informational.

Y-axis (Coherence capacity):
Low → Medium → High. Represents the potential to generate organized information flow efficiently.

Cells:
Each cell represents an “energy element,” for example:

Element Energy Type Function Notes

ATP Metabolic Direct cellular energy High coherence potential; central to metabolism and signaling
FOXP Regulatory protein Neuro-energetic and genetic modulation Influences stress control and neural organization
Cortisol Hormonal Metabolism and stress regulation Excess levels reduce coherence
Δ±1 Index Informational / Physical Integrated planetary energy state measurement Correlates energy flows with dynamic systems
Glucose Metabolic Primary energy source Needs conversion for maximal coherence

Interpretation:
This table helps identify which energies are synergistic vs. which may induce dispersion or incoherence, from cellular to global scales.

---

2. Proteic Periodic Table

Purpose:
Organizes amino acids and peptides according to bioenergetic coherence potential, molecular functionality, and self-organization capacity.

Axes:

X-axis (Amino acids / Peptides):
From simple monomers (glycine, alanine) to peptides formed under non-equilibrium conditions (dipeptides, tripeptides, etc.).

Y-axis (Functional role in biological coherence):
Structural → Signaling → Regulation → Self-organization.

Cells:
Each cell represents an amino acid or peptide with bioenergetic characteristics:

Element Type Function Notes

Gly Monomer Structural basis Facilitates peptide bonds; highly flexible
Ala Monomer Hydrophobic / structure Participates in protein folding
Gly-Gly Dipeptide Basic structure / energy Result from formation under simulated interplanetary conditions
Gly-Ala Dipeptide Signaling and structure Example of minimal self-organization
Mixed tripeptide Peptide Bioenergetic regulation Increases molecular coherence and integrated information capacity

Interpretation:
This table shows how molecular complexity increases from monomers to functional peptides, correlating structure, energy, and self-organization potential.

---

Usage Notes

1. Both tables are conceptual and modular, allowing new elements to be added as experiments or findings emerge.

2. They can be combined with coherence models (C = I/E) to map energetic efficiency and informational load at molecular or systemic scales.

3. Serve as visual and analytical tools, bridging biochemistry, bioenergetics, physics, and planetary science.

This folder contains all the code, parameter files, and documentation needed to reproduce the ISHEA Δ±1 simulation.

ISHEA Δ±1 Simulation Code Repository

---

README.md

ISHEA Δ±1 Bioenergetic Coherence Model

This repository contains the simulation code, parameter configuration files, and step-by-step replication instructions
for the study:

Pérez Pulido, C. (2026). "A Two-Level Bioenergetic Coherence Model Integrating NAD⁺, ROS, and ATP within the ISHEA Δ±1 Framework."

DOI Repository: https://doi.org/10.17605/OSF.IO/FYQGS

---

run_simulation.py

import yaml
import pandas as pd
import numpy as np

Load parameters from YAML file

with open('config/parameters.yaml', 'r') as f:
params = yaml.safe_load(f)

Example input data (initial ATP values)

input_data = pd.DataFrame({
'ATP_initial': np.linspace(1, 10, 10)
})

Simple simulation: apply ATP factor

results = input_data.copy()
results['ATP_sim'] = input_data['ATP_initial'] * params['ATP_factor']

Save results

results.to_csv('results/example_output.csv', index=False)

print("Simulation completed successfully.")

---

config/parameters.yaml

ATP_factor: 1.05
ROS_factor: 0.98
NAD_factor: 1.02

---

requirements.txt

numpy
pandas
pyyaml
scipy
matplotlib

---

docs/instructions.md

How to Run the Simulation

1. Install Python 3.10+
2. Install dependencies:
   pip install -r requirements.txt
3. Run the simulation:
   python run_simulation.py
4. The results will be saved in results/example_output.csv

---

Folder Structure Suggestion

ISHEA_Model_OSF/
├── run_simulation.py
├── requirements.txt
├── config/parameters.yaml
├── results/ (empty folder to store outputs)
├── docs/instructions.md
└── README.md

---

💡 Tips for OSF Wiki:

1. Use Code blocks for .py and .yaml files so users can copy them cleanly.

2. Use plain text blocks for README.md and instructions.md.

3. Keep file names exactly as above.

4. The results/ folder can be empty; it’s just to indicate where output files will go.

Esta carpeta contiene todo el código, archivos de parámetros y documentación necesarios para replicar la simulación ISHEA Δ±1.
This folder contains all the code, parameter files, and documentation needed to reproduce the ISHEA Δ±1 simulation.

Script: create_osf_repository.py

import os

Nombre de la carpeta del proyecto

base_folder = "ISHEA_Model_OSF"

Estructura de carpetas

folders = [
base_folder,
f"{base_folder}/config",
f"{base_folder}/results",
f"{base_folder}/docs"
]

Crear carpetas

for folder in folders:
os.makedirs(folder, exist_ok=True)

Contenido de los archivos

files_content = {
f"{base_folder}/run_simulation.py": """import yaml
import pandas as pd
import numpy as np

Cargar parámetros desde archivo YAML

with open('config/parameters.yaml', 'r') as f:
params = yaml.safe_load(f)

Datos de entrada de ejemplo (simulación inicial de ATP)

input_data = pd.DataFrame({
'ATP_initial': np.linspace(1, 10, 10)
})

Simulación sencilla: aplicar factor de ATP

results = input_data.copy()
results['ATP_sim'] = input_data['ATP_initial'] * params['ATP_factor']

Guardar resultados

results.to_csv('results/example_output.csv', index=False)

print("Simulation completed successfully.")
""",
f"{base_folder}/config/parameters.yaml": """ATP_factor: 1.05
ROS_factor: 0.98
NAD_factor: 1.02
""",
f"{base_folder}/requirements.txt": """numpy
pandas
pyyaml
scipy
matplotlib
""",
f"{base_folder}/docs/instructions.md": """# Cómo ejecutar la simulación

1. Instalar Python 3.10+
2. Instalar dependencias:
   pip install -r requirements.txt
3. Ejecutar la simulación:
   python run_simulation.py
4. Los resultados se guardarán en results/example_output.csv
   """,
   f"{base_folder}/README.md": """# ISHEA Δ±1 Bioenergetic Coherence Model

Esta carpeta contiene el código de simulación, los archivos de parámetros y las instrucciones de replicación
para el estudio:

Pérez Pulido, C. (2026). A Two-Level Bioenergetic Coherence Model Integrating NAD⁺, ROS, and ATP within the ISHEA Δ±1 Framework.

DOI del repositorio: https://doi.org/10.17605/OSF.IO/FYQGS
"""
}

Crear archivos con su contenido

for filepath, content in files_content.items():
with open(filepath, "w", encoding="utf-8") as f:
f.write(content)

print(f"Repositorio listo en la carpeta '{base_folder}' con todos los archivos base.")

---

✅ Cómo usarlo

1. Guarda el script como create_osf_repository.py en tu PC.

2. Abre terminal o CMD en la carpeta donde guardaste el script.

3. Instala pyyaml si no lo tienes:

pip install pyyaml pandas numpy matplotlib scipy

4. Ejecuta el script:

python create_osf_repository.py

5. Se generará la carpeta ISHEA_Model_OSF con:

ISHEA_Model_OSF/
├── run_simulation.py
├── requirements.txt
├── config/parameters.yaml
├── results/ (vacía)
├── docs/instructions.md
└── README.md

ADDENDUM — LEGAL NOTICE & INTELLECTUAL PROPERTY DECLARATION

ISHEA Δ±1 Framework — Two-Level Bioenergetic Coherence Model
Carlos J. Pérez Pulido | ISHEA Institute | © 2026 All rights reserved
Effective Date: February 2026 | Document Version: 1.0

⚠️ PLEASE READ CAREFULLY BEFORE USING THIS WORK
By accessing, downloading, citing, or using any part of this repository, you agree to be bound by the terms of this Legal Notice.

---

1. Copyright & Ownership

This work, including:

Theoretical framework

Mathematical formulations

Computational model

ISHEA Δ±1 coherence metric

Simulation data & figures

Supplementary materials & documentation

is exclusive intellectual property of:
Carlos J. Pérez Pulido — ISHEA Institute

© 2026 All rights reserved under national and international copyright law (Berne Convention).
Unauthorized use of the ISHEA name, framework, or nomenclature is prohibited.

---

2. License Terms — CC BY-NC-ND 4.0

Condition Permitted (✓) / Prohibited (✗) Notes

Attribution (BY) ✓ Must cite author & DOI
NonCommercial (NC) ✗ No use in products/services/for-profit ventures without written authorization
NoDerivatives (ND) ✗ No remixing, transforming, or building upon this work without authorization
Share freely ✓ May share verbatim copies with attribution, non-commercially
Academic citation ✓ Scientific publications may cite with full attribution
Teach/lecture ✓ Educational non-commercial use allowed with attribution

Full license text: CC BY-NC-ND 4.0 legalcode

---

3. Prohibited Uses ❌

Without prior written authorization:

Commercial use (products, software, services, consulting, proprietary research)

Creation of derivative works or translations/localizations

Claiming authorship or co-authorship of this framework

Reproducing >300 words without attribution

Using ISHEA name, brand, or framework in products/services/marketing

Automated bulk downloading, scraping, or redistribution

Training ML models on this work

---

4. Requesting Authorization

To request authorization for uses not covered by CC BY-NC-ND 4.0:

1. Contact the author via OSF repository or Research Square preprint.

2. Include:

Intended use

Organization

Commercial/non-commercial nature

Scope and duration

3. Written authorization required; verbal agreements not binding

4. Commercial licensing fees may apply; academic collaborations reviewed case-by-case

Preprint Contact: https://doi.org/10.21203/rs.3.rs-8899090/v1
OSF Repository DOI: https://doi.org/10.17605/OSF.IO/FYQGS

---

5. Mandatory Citation Requirements

Minimum required citation:

Format:

Pérez Pulido, C.J. (2026). A Two-Level Bioenergetic Coherence Model Integrating NAD⁺, ROS, and ATP within the ISHEA Δ±1 Framework. OSF Repository / Research Square Preprint.
OSF DOI: https://doi.org/10.17605/OSF.IO/FYQGS
Preprint DOI: https://doi.org/10.21203/rs.3.rs-8899090/v1

Update citation to the peer-reviewed version once published.

Failure to cite constitutes license violation and breach of academic integrity.

---

6. Preprint & Peer Review Status

Currently under peer review at Scientific Reports (Nature Portfolio)

Preprint posted: February 2026

Content represents author's independent analysis

Timeline:

First submission: Feb 17, 2026

Submission checks: Feb 21, 2026

Editor assigned: Feb 21, 2026

Revision requested: Feb 23, 2026

---

7. Data and Simulation Disclosure

All data in-silico computational simulations

No human or animal subjects, clinical trials, or proprietary third-party data

Simulation parameters calibrated within ISHEA Δ±1 framework

Methodology detailed in Supplementary Materials S2–S3

---

8. Enforcement & Remedies

Violations may result in:

Complaint to institutional research integrity offices

Copyright infringement claims

Request for retraction of infringing publications

Civil claims for damages

Author reserves all rights to enforce IP protections.

---

9. Governing Law

Interpreted under international copyright law (Berne, WIPO)

Applicable national IP laws

Disputes resolved in the author’s domicile jurisdiction unless agreed otherwise

---

© 2026 Carlos J. Pérez Pulido — ISHEA Institute. All rights reserved
Licensed CC BY-NC-ND 4.0 | Non-commercial | Attribution required | No derivatives
OSF DOI: https://doi.org/10.17605/OSF.IO/FYQGS
Preprint DOI: https://doi.org/10.21203/rs.3.rs-8899090/v1
Version 1.0 — February 2026

ISHEA Δ±1 Two-Level Bioenergetic Coherence Model

The ISHEA Δ±1 Two-Level Bioenergetic Coherence Model is a theoretical mathematical framework that describes cellular bioenergetics as a two-layered system integrating redox regulation and energy execution. The model is formulated within the ISHEA Δ±1 bounded coherence metric and represents systemic states within the interval [-1, +1].

Overview

The model proposes that cellular bioenergetic organization operates across two hierarchically coupled levels:

Level I – Redox-Informational Layer
Defined primarily by NAD⁺/NADH dynamics and regulated reactive oxygen species (ROS) signaling. This level is described as governing systemic redox balance and electron flow coordination.

Level II – Energetic Execution Layer
Defined by adenosine triphosphate (ATP) availability, which enables biochemical work and metabolic activity.

Within this structure, NAD⁺ and ROS are treated as regulatory variables influencing system coherence, while ATP is characterized as an execution variable dependent on upstream redox conditions.

Mathematical Structure

The model defines a coherence parameter (Δ) using a bounded nonlinear transformation (hyperbolic tangent) applied to weighted, normalized biological variables. The transformation constrains Δ to the range [-1, +1], where:

Positive values represent coherent systemic states

Values near zero represent transitional states

Negative values represent dysregulated states

The formulation includes first-order weighted contributions and interaction terms between variables. Sensitivity analyses and computational simulations are used to estimate parameter influence.

Conceptual Position

The model differs from traditional ATP-centered bioenergetic frameworks by emphasizing redox balance as a primary organizing axis. It frames reactive oxygen species not exclusively as damaging byproducts but as context-dependent regulatory signals within physiological ranges.

Scope

The ISHEA Δ±1 model is theoretical and computational. It does not constitute a clinical diagnostic tool or experimentally validated biomarker. It is intended as a systems-level modeling approach within bioenergetics and integrative systems biology.

Addendum — Derivative & Computational Extensions

See ADDENDUM_DERIVATIVE_SCOPE.md for clarification of intellectual scope and permitted derivative implementations.