PROJECT MNEMOSYNE-731 | RHENOVA–MNEMOSYNE COMPUTATIONAL ENGINE ARCHITECTURE

Document Code: M731-P1-E1-RMCEA-0001

Phase: Phase I — Infrastructure & Systems Mapping

Deliverable Class: Computational & AI Infrastructure

1. Purpose

To define the computational architecture required to model, simulate, score, and predict PROJECT MNEMOSYNE-731 disease mechanisms using:

RHENOVA redox–hypoxia variance logic
trauma-memory systems mapping
neuroimmune/autonomic instability modeling
electron-flow disruption scoring
contextual memory relapse forecasting
multi-neurosystem collapse prediction
SCF-PCR therapeutic reconstruction sequencing

2. Master Engine Definition

RHENOVA–MNEMOSYNE Computational Engine

The RHENOVA–MNEMOSYNE Computational Engine is defined as:

A multi-layer computational intelligence system designed to convert trauma, memory, immune, autonomic, metabolic, redox, hypoxia, and cognitive data into predictive models of disease progression, relapse risk, and therapeutic reconstruction pathways.

3. Master Computational Equation

$RMCE =Data_{MultiOmic}+Data_{Physiologic}+Data_{Cognitive}+Data_{Contextual}+RHENOVA_{Redox/Hypoxia}+SCF\text{-}CMF_{Current}$

$\rightarrow Prediction+Risk\ Stratification+PCR\ Therapeutic\ Sequencing$

4. Core Engine Objectives

Objective	Output
Map trauma-linked biology	contextual memory imprint models
Predict relapse states	Memory Relapse Risk Score
Quantify neuroimmune shock	Neuroimmune Shock Index
Model electron-flow collapse	Electron Flow Integrity Score
Track multi-neurosystem failure	Multi-Neurosystem Collapse Index
Guide SCF-PCR interventions	preventative, curative, restorative sequencing
Validate framework hypotheses	empirical evidence architecture

5. System Input Architecture

A. Clinical Inputs

Data Type	Examples
Patient history	trauma timeline, grief, guilt, chronic stress
Symptom profile	brain fog, fatigue, dissociation, autonomic symptoms
Cognitive testing	memory, attention, executive function
Behavioral patterning	avoidance, regression, reactivity
Disease status	Alzheimer’s, PTSD, post-viral syndrome, HAND-like symptoms

B. Physiological Inputs

System	Data
Autonomic	HRV, resting heart rate, baroreflex
Respiratory	breathing variability, CO₂ tolerance
Neurocardiac	ECG variability, orthostatic response
Sleep	sleep stages, fragmentation, REM/NREM profile
Neuroelectrical	EEG coherence, theta-gamma coupling

C. Multi-Omic Inputs

Omic Layer	Data
Genomics	APOE, FKBP5, NR3C1, BDNF, HLA
Transcriptomics	IL6, TNF, NFKB1, HIF1A, CLOCK
Epigenomics	NR3C1, FKBP5, BDNF methylation
Proteomics	cytokines, NfL, pTau, Aβ42/40
Metabolomics	ATP, NAD⁺, ROS, lactate-pyruvate
Microbiomics	SCFA, LPS burden, diversity
Connectomics	hippocampal-limbic-PFC networks

D. RHENOVA Inputs

RHENOVA Variable	Meaning
GSH:GSSG	redox buffering
8-OHdG	oxidative DNA injury
MDA	lipid peroxidation
HIF-1α	hypoxia activation
pO₂ / SpO₂	oxygenation status
lactate-pyruvate	metabolic strain
ROS burden	oxidative load

6. Engine Module Architecture

MODULE 1 — Contextual Memory Imprint Processor

Function

Maps life events into biological-context variables.

$Life\ Event\rightarrow Contextual\ Encoding\rightarrow CMIN$

Outputs

Output	Meaning
Contextual Memory Imprint Score	biological weight of memory
Trigger Sensitivity Index	likelihood of relapse activation
Developmental Drift Load	age-stage vulnerability
Emotional Weight Score	limbic imprint intensity

MODULE 2 — Psychological Drift Event Analyzer

Function

Quantifies PDE severity, recurrence, and biological consequence.

$PDE =Intensity\times Threat\times Biological\ Vulnerability-Resilience$

Outputs

Output	Meaning
PDE Severity Grade	event biological impact
PDE Recurrence Burden	repeated activation load
Trauma Propagation Risk	disease-progression probability
DDS Risk Estimate	developmental drift potential

MODULE 3 — Neuroimmune Shock Modeler

Function

Models cytokine-autonomic-inflammatory coupling.

$NIS =IL6+TNF+CRP+ANS\ Instability+ROS$

Outputs

Output	Meaning
Neuroimmune Shock Index	inflammatory collapse score
Cytokine Drift Score	immune instability
Microglial Risk Estimate	CNS inflammatory risk
Immune Replay Probability	likelihood of inflammatory relapse

MODULE 4 — Autonomic Failure Predictor

Function

Maps vagal collapse, sympathetic dominance, and HRV instability.

$AF =Threat\ Load+ANS\ Reactivity-Vagal\ Tone$

Outputs

Output	Meaning
Autonomic Failure Score	ANS instability burden
Vagal Reserve Index	recovery capacity
Sympathetic Dominance Score	hyperactivation level
Neurocardiac Risk Tier	cardiac-autonomic instability risk

MODULE 5 — RHENOVA Redox–Hypoxia Engine

Function

Models ROS/hypoxia-driven pathogenesis.

$RHI =ROS+Hypoxia+Oxidative\ Damage-Redox\ Buffering$

Outputs

Output	Meaning
Redox-Hypoxia Instability Score	environmental stress burden
Oxidative Damage Index	molecular injury risk
Hypoxia Drift Score	oxygen instability
Mitochondrial Stress Forecast	energy-collapse probability

MODULE 6 — Electron Flow Integrity Simulator

Function

Quantifies mitochondrial coherence and electron leakage risk.

$EFI =\frac{ATP+NAD^++Oxygen+ETC\ Integrity}{ROS+Inflammation+Hypoxia}$

Outputs

Output	Meaning
Electron Flow Integrity Score	mitochondrial efficiency
ATP Stability Index	energy reserve
Electron Leakage Risk	oxidative escalation
Cognitive Fatigue Predictor	brain-energy instability

MODULE 7 — Multi-Neurosystem Collapse Predictor

Function

Integrates instability across nine neurosystems.

$MNCI =\frac{NI+NA+NC+NE+NM+NV+NCN+NEL+ND}{9}$

Outputs

Output	Meaning
Multi-Neurosystem Collapse Index	total systemic failure risk
Dominant Collapse Axis	primary failing system
Propagation Map	system-to-system spread
Collapse Stage	MNCI severity classification

MODULE 8 — Memory Relapse Forecasting System

Function

Predicts relapse/replay probability.

$MRS =Trigger\times Memory\ Weight\times ANS\ Vulnerability\times Immune\ Sensitization$

Outputs

Output	Meaning
Memory Relapse Risk Score	replay probability
Trigger Vulnerability Map	key activation cues
Non-Lucid Relapse Risk	Alzheimer’s-style replay risk
Temporal Collapse Score	past-present confusion risk

MODULE 9 — SCF-CMF Current Mapping Engine

Function

Maps biological disruption to Conscience Currents.

CMF Current	Engine Measurement
Awareness	EEG/cognitive coherence
Emotion	limbic stress + cytokine load
Embodiment	HRV/body-state regulation
Energy	ATP/electron-flow integrity
Time	circadian/sleep synchronization
Transformation	BDNF/plasticity/adaptation

MODULE 10 — SCF-PCR Therapeutic Sequencing Engine

Function

Converts patient-system state into intervention sequence.

$Fault\ Stage\rightarrow PCR\ Mode\rightarrow Therapeutic\ Stack$

Outputs

PCR Mode	Engine Output
Preventative	stabilize, buffer, reduce risk
Curative	suppress active loops, correct faults
Restorative	rebuild, regenerate, reintegrate

7. Core Scoring Systems

Score	Function
CMIS	Contextual Memory Imprint Score
PDE-S	Psychological Drift Event Severity
NISI	Neuroimmune Shock Index
AFS	Autonomic Failure Score
EFI	Electron Flow Integrity Score
RHI	Redox-Hypoxia Instability Score
MNCI	Multi-Neurosystem Collapse Index
MRS-R	Memory Relapse State Risk
CCS	Cognitive Coherence Score
PRS	PCR Readiness Score

8. Data Pipeline

$Raw\ Data\rightarrow Cleaning\rightarrow Feature\ Extraction\rightarrow Domain\ Scoring\rightarrow Cross\text{-}System\ Integration\rightarrow Risk\ Prediction\rightarrow PCR\ Recommendation\rightarrow Outcome\ Tracking$

9. Computational Model Types

Model Type	Use
Rule-based SCF Engine	interpretable scoring
Bayesian Network	causal uncertainty mapping
Dynamical Systems Model	PB/PR trajectory simulation
Graph Neural Network	multi-system interaction mapping
Time-Series Model	longitudinal relapse forecasting
Digital Twin	patient-specific simulation
Explainable AI Layer	clinical interpretability

10. Validation Architecture

Validation Layer	Purpose
Internal Consistency	ensure model coherence
Retrospective Data Testing	validate against historical data
Prospective Cohort Testing	real-world predictive accuracy
Biomarker Correlation	molecular plausibility
Intervention Response Tracking	therapeutic validation
Regulatory Auditability	transparent decision logic

11. Minimum Viable Computational Platform

Phase I MVP Components

Component	Required
Patient data schema	yes
Biomarker registry	yes
SCF-CMF scoring formulas	yes
Manual scoring dashboard	yes
Risk stratification tables	yes
PCR sequencing logic	yes
Evidence logging system	yes

12. Dashboard Architecture

Core Dashboard Views

Dashboard	Function
Patient Systems Map	overview of instability
Neuroimmune Map	cytokine/autonomic coupling
Electron Flow Panel	ATP/ROS/redox status
Memory Relapse Map	trigger and replay risk
CMF Current Dashboard	awareness/emotion/energy/time states
PCR Planning Panel	therapeutic sequencing
Validation Evidence Tracker	proof-building registry

13. Database Schema Overview

Core Tables

Table	Contents
Patient_Profile	demographics, history
Trauma_Context	PDE and contextual memory data
Biomarkers	omics and lab values
Physiology	HRV, EEG, sleep
Cognitive_Assessments	testing results
Engine_Scores	computed indices
Therapeutic_Plans	PCR recommendations
Outcomes	follow-up data
Evidence_Log	validation evidence

14. Evidence Generation Outputs

Output	Scientific Use
biomarker correlations	mechanism validation
relapse prediction accuracy	empirical testing
longitudinal score change	intervention evidence
subgroup clustering	phenotype discovery
mechanism diagrams	theoretical validation
causal maps	hypothesis refinement

15. Clinical-Research Use Cases

Use Case	Engine Function
Alzheimer’s non-lucid relapse	forecast replay states
PTSD	map PDE and ANS burden
Long COVID brain fog	redox-neuroimmune modeling
HAND-like cognitive drift	immune-metabolic cognitive mapping
trauma-linked fatigue	electron-flow simulation
developmental drift	DDS risk estimation

16. Security & Ethics Requirements

Requirement	Purpose
trauma-informed consent	protect vulnerable patients
de-identification	privacy
explainable scoring	prevent black-box misuse
audit logs	regulatory traceability
clinical oversight	avoid algorithm-only decisions
safety escalation pathways	patient protection

17. Phase I Build Priorities

Priority	Deliverable
1	define data dictionary
2	construct scoring formulas
3	build manual dashboard prototype
4	create synthetic test cases
5	define validation datasets
6	map PCR decision rules
7	produce evidence registry

18. Final Engine Statement

The RHENOVA–MNEMOSYNE Computational Engine Architecture establishes the computational backbone of PROJECT MNEMOSYNE-731. It converts trauma-memory biology, neuroimmune signaling, autonomic failure, electron-flow disruption, redox-hypoxia variance, and cognitive instability into measurable, modelable, and clinically interpretable indices.

This engine enables the project to transition from theoretical framework to evidence-generating translational research platform.

Next Deliverable:

E2 — DBI Cognitive Network Model