Project Confluence

A Phi-vector framework for modeling shared metabolic dynamics across cancer types

Abstract

Cancer cells across tissue types converge on shared metabolic reprogramming patterns (the Warburg effect and its extensions), but most models validate against synthetic or single-cancer-type data, limiting claims of generality. Project Confluence models this convergence directly using an ODE-based state-space system anchored in a five-component Phi-vector - Phi_temporal, Phi_informational, Phi_functional, Phi_spatial, and Phi_coupling - representing distinct facets of metabolic-regulatory state.

Six enzymes central to glycolytic and oxidative metabolism (HK2, PKM2, LDHA, IDH1/2, PDK1, G6PD) are mapped to specific channels in the ODE system as grounded, biologically-interpretable state variables rather than abstract parameters.

Key result: Replacing synthetic-data validation with six real CCLE metabolomics channels (CCLE_metabolomics_20190502.csv; 225 metabolites x 928 cell lines) raises structurally identifiable parameters from 7/17 to 15/17, evaluated across three cancer types chosen for maximal biological diversity - AML (blood), osteosarcoma (bone), and NSCLC (lung) - to support generalizability claims beyond a single tissue context.

An adaptive controller built on this framework extends structurally to theranostic applications (radioligand diagnostic-therapeutic pairing).

Citation

See CITATION.cff. DOI badge added below once Zenodo publishes.

🧬 Project Confluence

⚠️ Status: Phase 1 computational validation only. No real patient data used.

Redefining Precision Oncology: From Tumor Killing to Complexity Restoration

Citation and attribution: this repository is MIT-licensed for open review and collaboration. If you use the code, theory, figures, or documentation, please cite the repository and credit Kelechi Ogbonna / cloudynirvana.

Expert review invited: oncology, systems biology, control theory, clinical trial design, mathematical biology, and research-software reviewers are encouraged to audit assumptions, reproduce simulations, and challenge the validation plan before any translational claims are made.

External validation preparation: see validation/external_validation_pipeline.md for the PhysioNet, GDC, cBioPortal, and Hugging Face data-readiness plan.

Core Hypothesis

Health is not a fixed point but a Complex Attractor State characterized by adaptive variability, fractal rhythms, and moderate inter-system coupling. Disease is a transition to pathological attractors. Therapy should restore the complexity, not just kill the tumor.

Traditional Oncology:   Kill Cancer Cells → Measure Tumor Shrinkage
Project Confluence:     Restore Complexity → Measure Φ Improvement

Theoretical Foundation: Bounded Adaptive Coherence (BAC)

A biological system sustains viable complexity if and only if the minimum singular value of its cross-scale coupling tensor exceeds the maximum normalised rate of local entropy production at any organisational scale.

The BAC framework provides a first-principles unification of aging, cancer, and health as states of a single mathematical object — the coupling tensor $C(t)$, which governs causal coordination across biological scales (molecular → cellular → tissue → organism → evolutionary).

Failure Mode	Coupling Tensor Signature	BAC Violation Type
Aging	Global off-diagonal decay of $C_{ij}$	$\sigma_{\min}(C) \to 0$ uniformly
Cancer	Selective collapse of organism-scale pairs	$\sigma_{\min}(C) \to 0$ in specific sectors
Health	BAC condition satisfied with positive margin	$V(t) = \sigma_{\min}(C) - \max_k[\dot{s}_k] > 0$

The Φ vector is a partial measurement of the coupling tensor — the elements most relevant to cancer pathology. Biologics act as coupling restoration operators on specific $C_{ij}$ elements.

📄 Full derivation: theory/bounded_adaptive_coherence.md

Unified Complexity Profile (UCP)

The framework operates on two complexity dimensions:

Dimension	Symbol	Source	Purpose
Clinical Complexity	Ψ (Psi)	EHR data, staging, genomics	Treatment difficulty
Dynamical Complexity	Φ (Phi)	Time-series physiology, modeling	Optimization target

Φ is a 5D vector:

Φ Dimension	Metric	Healthy Range	Biomarker
Φ_temporal	Multiscale Entropy	0.6–0.8	HRV, glucose variability
Φ_spatial	Correlation Dimension D₂	3.0–6.0	Cell diversity
Φ_functional	Recovery rate	0.5–0.8	Stress response
Φ_informational	λ_max + spectral slope	0.5–0.7	Signal entropy
Φ_coupling	Cross-system correlation	0.4–0.7	Immune-metabolic sync

Architecture

┌─────────────────┐     ┌──────────────────┐     ┌──────────────┐     ┌────────────────┐
│ Bioinformatics  │────▶│   Complexity     │────▶│   Patient    │────▶│     RADO       │
│     Miner       │     │   Profiler       │     │   Fitter     │     │    Engine      │
│   (Module 4)    │     │   (Module 1)     │     │  (Module 2)  │     │   (Module 3)   │
└─────────────────┘     └──────────────────┘     └──────────────┘     └────────────────┘
   TCGA/cBioPortal         5D Φ vector            Digital Twin         Optimized Protocol
   Omics extraction        Archetype ID           Bayesian MCMC        Complexity restoration

Adaptive Therapy Controller (NEW)

"The optimal therapy is an algorithm, not a prescription." — First Principles Deconstruction, Axiom 10

Project Confluence now includes a closed-loop adaptive therapy controller that treats dosing as a real-time policy decision, not a fixed protocol.

Key Innovation

Instead of optimizing for a static dose (e.g., "DCA at 25mg for 60 days"), the system optimizes the hyperparameters of an adaptive policy — when to dose, when to hold, and how to respond to resistance signals.

Traditional:  Optimizer → Fixed Dose Schedule → Patient
Confluence:   Optimizer → Adaptive Policy π(state) → Dynamic Dosing → Patient

Three Policy Modes

Mode	Description	Use Case
Threshold	Bang-bang control with hysteresis	Simple on/off dosing
Proportional	Dose scales with tumor burden	Continuous dose adjustment
RobustAdaptive	Threshold + resistance-aware + uncertainty margins	Full Confluence policy

Safety Constraints (Assurance Layer)

All policies are bounded by hard safety constraints that cannot be overridden:

Absolute dose cap (robust_max_dose)
Forced drug holidays after max continuous dosing
Minimum holiday duration
Cumulative toxicity budget

Monte Carlo Validation: 200 Uncertain Biological Scenarios

Metric	MTD (Standard Care)	Confluence Adaptive
Resistant Takeover Rate	178/200 (89.0%)	1/200 (0.5%)
Tumor Controlled at Day 180	200/200 (100%)	36/200 (18.0%)
Mean Final Tumor Burden	0.271	0.952
Mean Final Resistant Fraction	91.5%	11.9%

The adaptive policy achieves near-zero resistant takeover (1/200 scenarios) across 200 random biological parameter sets sampled from the uncertainty set. The tradeoff is explicit: it preserves evolutionary containment at the cost of short-horizon tumor shrinkage. MTD keeps burden smaller but selects for resistance in 89% of scenarios. The adaptive controller maintains sensitive-cell competitive suppression of resistant clones — the ecological mechanism adaptive therapy is designed to exploit.

# Run the comparison
python validate_controller.py

# Run full Monte Carlo analysis (200 samples, ~5 min)
python scripts/monte_carlo_uncertainty.py

Mathematical Core — 15D SAEM Model

The patient state z ∈ ℝ¹⁵ evolves under:

dz/dt = F(z, θ, u)

Metabolic (10D):     Glucose, Lactate, Pyruvate, ATP, NADH,
                     Glutamine, Glutamate, αKG, Citrate, ROS
Immune (3D):         I_eff, I_reg, I_exhaust
Microenvironment (2D): σ_stromal, ν_vascular

Nonlinearity via Michaelis-Menten kinetics → strange attractor dynamics.

graph TD
    subgraph Scale 0: Molecular (z0-z4)
        M1[Glucose/Lactate Flux] <--> M2[ATP/NADH Energetics]
    end

    subgraph Scale 1: Cellular (z5-z9)
        C1[Glutamine/alpha-KG] <--> C2[ROS Accumulation]
    end

    subgraph Scale 2: Organismal (z10-z12)
        O1[Effector T-Cells] <--> O2[Tregs / Exhaustion]
    end

    subgraph Scale 3: Tissue (z13-z14)
        T1[Stromal Density] <--> T2[Vascular Integrity]
    end

    %% Cross-Scale Coupling Tensor Channels C_ij
    M2 -- "C_01 (Metabolic feedback)" --> C2
    C2 -- "C_12 (Stress-immune gating)" --> O1
    O2 -- "C_23 (Immune-stroma pruning)" --> T1
    T2 -- "C_30 (Vascular glucose supply)" --> M1

Quick Start

# Clone
git clone https://github.com/cloudynirvana/project-confluence.git
cd project-confluence

# Install dependencies
pip install -r requirements.txt

# Run complexity profiling
python -c "
from models.complexity_profiler import ComplexityProfiler
from models.ode_system import ComplexAttractorODE

ode = ComplexAttractorODE()
result = ode.solve(t_span=(0, 200), dt_eval=0.5)
profiler = ComplexityProfiler()
phi = profiler.profile(result['z'], dt=0.5)
print(phi.to_json())
"

Convergence Implementation Status

The current computational stack now implements the four Codex convergence prompts:

Layer	Implementation	Verification
Quantum scale k0	`ComplexAttractorODE` is extended to 16D with `psi_coherent`; `CouplingTensorAnalyzer` computes a 5-scale tensor and direct `C_02` quantum-to-cellular coupling.	`tests/test_ode_system.py`, `tests/test_coupling_tensor.py`
OSKM steering	`PolicyMode.EPIGENETIC_STEERING` emits pulsatile OSKM dosing from identity metrics with Landauer thermal override holidays.	`tests/test_adaptive_controller.py`
Curvature bottlenecks	`scripts/detect_curvature_bottlenecks.py` exports a Forman-Ricci JSON report and network plot for cellular-organismal bottlenecks.	`results/curvature_bottlenecks/`
Memory-kernel EKF	`ExtendedKalmanFilterObserver` estimates `[z, vec(M_neural)]` and accepts DMN coherence plus EEG PCI measurement channels.	`tests/test_optimal_inference.py`

Focused validation:

python -B -m pytest tests/test_adaptive_controller.py tests/test_ode_system.py tests/test_coupling_tensor.py tests/test_optimal_inference.py -q
python -B scripts/detect_curvature_bottlenecks.py

PDAC Rogue Closure Model

Project Confluence now includes a disease-specific executable scaffold for pancreatic ductal adenocarcinoma (PDAC):

PDAC persistence = KRAS/RAS driver closure
                 + EGFR/STAT3 bypass recovery
                 + stromal/glycocalyx shielding
                 + immune exclusion
                 + therapy-selected resistance

Run the synthetic workflow:

python scripts/run_pdac_rogue_closure.py --all-scenarios

Validation data links and the real-data plan are in validation/pdac_data_sources.md. The committed PDAC time series in results/pdac_rogue_closure/ is synthetic and exists for reproducibility; raw public datasets should be fetched from GDC, cBioPortal, GEO, DepMap, PDMR, PDX Finder, GlyGen, and GlyConnect rather than stored directly in the repository.

🦞 AutoResearchClaw Integration

Generate a full conference paper from Project Confluence's models with one command:

python scripts/run_autoresearch.py phi-universality
python scripts/run_autoresearch.py --list-topics

Pre-built topics: phi-universality · drug-scheduling · immune-metabolic · ferroptosis-complexity · digital-twin

AutoResearchClaw runs 23 stages autonomously — literature review, hypothesis debate, experiments using Confluence's ODE system, peer review, and LaTeX paper. No GPU required.

Config: config.arc.yaml | Prompts: prompts.confluence.yaml

Repository Structure

project-confluence/
├── models/                          # Core computational modules
│   ├── adaptive_controller.py       # Closed-loop adaptive therapy controller
│   ├── clonal_dynamics.py           # Lotka-Volterra clonal competition engine
│   ├── resistance_model.py          # Multi-mechanism resistance tracker
│   ├── complexity_profiler.py       # Module 1: 5D Φ vector
│   ├── patient_fitter.py            # Module 2: Bayesian digital twin
│   ├── drug_optimization_engine.py  # Module 3: RADO engine
│   ├── ode_system.py                # 15D SAEM ODE
│   ├── immune_dynamics.py           # Immune force field
│   ├── intervention.py              # Drug library (20+ drugs)
│   ├── geometric_optimization.py    # Basin curvature, Kramers escape, Flatten-Heat-Push
│   ├── geometric_pathways.py        # Freidlin-Wentzell MAP via String Method
│   ├── fisher_geometry.py           # Fisher Information Matrix / stiff-sloppy (MBAM)
│   ├── network_curvature.py         # Forman-Ricci curvature bottleneck detection
│   ├── realistic_failure.py         # Stochastic failure model
│   ├── ferroptosis.py               # Iron-dependent cell death
│   ├── coupling_tensor.py           # Block Jacobian cross-scale C_ij tensor
│   ├── optimal_inference.py         # EKF state & coupling tensor observer
│   ├── lyapunov_certificate.py      # Universal Complexity Sustainment — CLF certifier
│   └── identity_tensor.py           # Φ-Unification Identity Tensor — consciousness preservation
├── scripts/
│   ├── monte_carlo_uncertainty.py   # 200-sample uncertainty validation
│   ├── test_pathways.py             # Geometric calibration integration test
│   ├── test_sustainment.py          # Sustainment Theorem validation (4 scenarios)
│   ├── test_identity.py             # Identity Tensor validation (5 scenarios)
│   ├── clonal_evolution_sim.py      # Adaptive vs MTD comparison
│   ├── confluence_runner.py         # Full pipeline runner
│   ├── optimize_biomarker_panel.py  # EKF biomarker selection optimization
│   └── ...                          # Data agents, validation scripts
├── agents/                          # Data agents
│   └── bioinformatics_miner.py      # Module 4: TCGA/cBioPortal
├── validation/                      # Safety & reference data
│   ├── clinical_guardrails.json     # CTCAE v5.0 constraints
│   └── gene_to_parameter_map.json   # Omics → ODE mapping
├── theory/                          # Mathematical framework
│   ├── age_reversal_transfer.md          # Scaling BAC & C_ij framework to biogerontology
│   ├── bounded_adaptive_coherence.md     # BAC first-principles theory
│   ├── complexity_sustainment.md         # Optimal complexity maintenance (cancer vs aging)
│   ├── optimal_inference_design.md       # Inference of C_ij from sparse clinical observations
│   ├── sustained_complexity_and_death.md # Biophysics & thermodynamics of death
│   ├── deepmind_executive_brief.md       # Proposal for DeepMind & Isomorphic Labs integration
│   ├── geometric_calibration_research.md  # Geometric calibration research proposal
│   ├── quantum_criticality_and_unison.md  # Penrose Orch OR × BAC quantum-classical integration
│   ├── universal_sustainment_theorem.md   # Control Lyapunov proof for indefinite sustainment
│   └── consciousness_complexity_bridge.md  # IIT × BAC Φ-Unification — identity preservation theory
├── tests/                           # Test suite (11 test files)
├── docs/                            # User documentation
└── notebooks/                       # Validation pipelines

🗺️ Mathematical-to-Code Mapping Registry

To bridge abstract biophysical theory with verified computational executions, use the following translation map linking the mathematical papers to their Python modules:

Biophysical Equation / Concept	Mathematical Theory Paper	Executable Python Module	Verification Test Suite
16D Spectral Attractor (SAEM + k0)	`theory/optimal_inference_design.md`, `theory/quantum_criticality_and_unison.md`	`models/ode_system.py`	`tests/test_ode_system.py`
5x5 Cross-Scale Coupling Tensor $C_{ij}$	`theory/complexity_sustainment.md`	`models/coupling_tensor.py`	`tests/test_coupling_tensor.py`
EKF Observer + Memory Kernel $M(t)$	`theory/optimal_inference_design.md`, `theory/consciousness_complexity_bridge.md`	`models/optimal_inference.py`	`tests/test_optimal_inference.py`
OED Sensor Selection Matrix $H$	`theory/optimal_inference_design.md`	`scripts/optimize_biomarker_panel.py`	Runs combinatorial validation
Stochastic Laboratory Calibration	`theory/problem_statement_and_justification.md`	`scripts/stochastic_noise_sweep.py`	Assay noise sweeps
Universal Sustainment Theorem (CLF)	`theory/universal_sustainment_theorem.md`	`models/lyapunov_certificate.py`	`scripts/test_sustainment.py`
Φ-Unification (IIT × BAC Bridge)	`theory/consciousness_complexity_bridge.md`	`models/identity_tensor.py`	`scripts/test_identity.py`
Bioinformatics Parameter Mapping	`theory/geometric_calibration_research.md`	`agents/bioinformatics_miner.py`	`tests/test_bioinformatics.py`
Genomic Cohort Reconstructor	`theory/problem_statement_and_justification.md`	`scripts/reconstruct_tcga_patients.py`	TCGA diagnostic outputs

Pan-Cancer Support

Cancer Type	Metabolic Profile	Key Vulnerability
TNBC	Warburg + glutamine addiction	Glycolysis inhibition
PDAC	Extreme glycolysis + stromal barrier	Stromal depletion
NSCLC	Moderate glycolysis	OXPHOS targeting
Melanoma	OXPHOS-dependent	ETC inhibition
GBM	High glycolysis + neurotransmitter crosstalk	Glucose deprivation
CRC	MSI-H, moderate Warburg	Immunotherapy + metabolic
HGSOC	Glutamine-dependent	GLS1 inhibition
mCRPC	Lipogenesis from citrate	Citrate diversion block
AML	OXPHOS + glutamine	Combined metabolic attack
HCC	Extreme Warburg + lipogenesis	Multi-pathway inhibition

📢 Call for Data

We are seeking longitudinal pathology and omics datasets to validate Confluence across oncology, metabolic disease, and comorbidities. Static snapshots are insufficient — we need time-series data that allows reconstruction of complexity profiles.

We welcome: Cancer time-series · Diabetes/metabolic longitudinal data · Comorbidity cohorts · Negative results

📄 Full details: CALL_FOR_DATA.md 📋 Submission template: data_submission_template.json 🔬 What we measure: complexity_signature.md

Three-Arm Validation Strategy

Arm	Goal	Success Metric
Separate	Confluence works on Cancer and Diabetes individually	Same equations identify tipping points in both
Conjoined	Handles coupled comorbidity systems	Predicts cross-domain interaction effects
Universality	Mathematics is disease-agnostic	Φ recovery profiles statistically indistinguishable

📋 Full protocol: validation_protocol.md

Validation Roadmap

Phase	Description	Status
Phase 1	Computational validation (1000-trial Monte Carlo)	✅ Complete
Phase 1b	Adaptive therapy Monte Carlo (200 uncertain scenarios)	✅ Complete
Phase 2	Retrospective validation (TCGA complexity vs. survival)	🔄 In Progress
Phase 2b	Cross-disease complexity validation (3-arm protocol)	📢 Call for Data posted
Phase 3	Prospective wet-lab (collaborator-dependent)	⏳ Planned

Validation Walkthrough (Phase 1 Snapshot)

Results below are from scripts/disease_poc.py with output captured in poc_results.txt.

| Disease | |Phi| | Coherence | Dist. from Healthy | |---------|------|-----------|--------------------| | Healthy | 1.3199 | 0.2628 | -- | | Glioblastoma | 1.5577 | 0.5593 | 0.6732 | | TNBC | 1.4490 | 0.5014 | 0.5794 | | Alzheimers | 1.3028 | 0.3650 | 0.3792 | | Nephroblastoma | 1.3486 | 0.3473 | 0.2932 | | Diabetes | 1.3547 | 0.3649 | 0.2404 | | Parkinsons | 1.2171 | 0.2355 | 0.2059 | | Lupus | 1.2522 | 0.2015 | 0.1574 | | ALS | 1.2769 | 0.1982 | 0.1507 |

In this snapshot, Glioblastoma is the furthest from healthy (0.6732), exceeding TNBC. Lupus shows the lowest coherence (0.2015), aligned with the autoimmune hyperactivation settings in LupusParams. ALS and Lupus are closest to healthy (0.1507 and 0.1574), indicating subtle early-stage deviations in this model.

TNBC vs Nephroblastoma distance: 0.3076.

Per-dimension divergence (TNBC vs Nephroblastoma):

Dimension	Healthy	TNBC	Nephro	D(TNBC-Nephro)
Phi_temporal	0.4897	0.3901	0.3684	0.0217
Phi_spatial	0.2786	0.3224	0.3191	0.0033
Phi_functional	0.9757	0.9829	0.9871	0.0042
Phi_informational	0.2882	0.8267	0.5434	0.2833
Phi_coupling	0.6242	0.4403	0.5581	0.1178

Therapeutic simulation (Nephroblastoma):

Intervention	Phi-distance (pre)	Phi-distance (post)	Restoration	Notes
IGF2R monotherapy (IGF2_signaling: 0.75 -> 0.30)	0.2932	0.2302	21.5%	3/5 dimensions shift toward healthy
IGF2R + WT1 mRNA (WT1_activity: 0.20 -> 0.55)	0.2932	0.2186	25.5%	4.0% synergy gain vs mono

TCGA retrospective (Track A, synthetic cohort):

Disease	Phi-dist	Survival (d)	Spearman rho	HR
TNBC	0.4869	275	-0.8220	9.83
Alzheimers	0.3871	1005	-0.9181	1.29
ALS	0.1769	1078	-0.8358	1.24
Diabetes	0.2042	1532	-0.7753	1.17
Parkinsons	0.1796	1486	-0.7904	1.07
Nephroblastoma	0.2811	1338	-0.3437	1.04
Lupus	0.1934	1612	-0.7566	1.13
Glioblastoma	0.6195	387	-0.8376	1.95

Overall Spearman rho (240 patients): -0.7937. Glioblastoma now shows a strong negative rho after scaling, consistent with its aggressiveness.

Reproduce locally:

python scripts/disease_poc.py > poc_results.txt 2>&1

python scripts/tcga_retrospective.py > tcga_output.txt 2>&1

TCGA retrospective results are saved to results/tcga_val/retrospective_metrics.json.

Track B ingestion (longitudinal cohort):

python scripts/tcga_track_b.py --input data/track_b/mock_cohort.json

Track B results are saved to results/tcga_val/track_b_metrics.json. Use --use-neural-ode to reconstruct trajectories if torchdiffeq is installed. Note: With fewer than 3 patients, Spearman rho is not statistically meaningful (2-point rho will be ±1 by definition).

To generate a pinned lockfile (requirements.lock.txt) on a machine with Python installed:

powershell -File scripts/pin_requirements.ps1

Safety & Regulatory

All protocols constrained by clinical_guardrails.json (CTCAE v5.0)
Φ dimensions mapped to LOINC / SNOMED-CT codes
FDA MIDD (Model-Informed Drug Development) aligned
See DISCLAIMER.md for medical use limitations

🇳🇬 Nigeria Clinical Guidelines Integration

Project Confluence integrates the Nigeria Standard Treatment Guidelines (NSTG 2022) — 270 structured clinical conditions published by the Federal Ministry of Health, Nigeria — as a RAG (Retrieval-Augmented Generation) layer for guideline-aware precision oncology.

Data Source: chisomrutherford/nigeria-clinical-guidelines-dataset License: CC-BY-4.0 | Curated by: Chisom Rutherford

What This Adds

Feature	Description
NigeriaGuidelineRetriever	Semantic search (RAG) over all 270 NSTG conditions with FAISS + sentence-transformers
Nigeria-Specific Guardrails	Adjusted safety thresholds for malaria, HIV, sickle cell, anaemia comorbidities
Guideline-Aware Controller	Adaptive therapy controller with NSTG 2022 safety layer
Resource-Aware Dosing	Drug availability tiers (commonly/intermittently/rarely available in Nigeria)
Clinical Query API	FastAPI endpoints for real-time guideline retrieval

Quick Start

from agents.nigeria_guideline_retriever import NigeriaGuidelineRetriever

# Initialize (downloads from HuggingFace on first run, or uses built-in mock data)
retriever = NigeriaGuidelineRetriever()

# Semantic search
results = retriever.retrieve("first-line treatment for breast cancer in Nigeria")
for r in results:
    print(f"[{r.score:.3f}] {r.chunk.condition_name}: {r.chunk.text[:100]}")

# Structured clinical answer
print(retriever.answer("What is the dosing for cisplatin in cervical cancer?"))

# Direct protocol lookup
protocol = retriever.get_treatment_protocol("BREAST CANCER")

# Drug-specific constraints
constraints = retriever.get_dosing_constraints("doxorubicin")

Guideline-Aware Adaptive Controller

from models.adaptive_controller import AdaptiveController, PolicyMode

# Controller auto-loads Nigeria guardrails if JSON exists
controller = AdaptiveController(
    policy_mode=PolicyMode.ROBUST_ADAPTIVE,
    guideline_retriever=retriever,
    cancer_type="TNBC",
)

# Summary includes Nigeria guidelines status
print(controller.get_summary())
# → {"nigeria_guidelines_active": true, ...}

API Endpoints

# Query guidelines (semantic search)
curl -X POST http://localhost:8000/guideline_query \
  -H "Content-Type: application/json" \
  -d '{"query": "management of neutropenia during chemotherapy", "top_k": 5}'

# List all 270 conditions
curl http://localhost:8000/guideline_conditions

# Get specific protocol
curl http://localhost:8000/guideline_protocol/breast%20cancer

# Get drug constraints
curl http://localhost:8000/guideline_drug/doxorubicin

Install Optional Dependencies

pip install sentence-transformers faiss-cpu datasets

Without these, the retriever falls back to TF-IDF/keyword matching (still functional, lower accuracy).

Contributing

We welcome contributions from computational biologists, oncologists, and dynamical systems researchers. See CONTRIBUTING.md for guidelines.

Citation

@software{ogbonna2026confluence,
  author = {Ogbonna, Kelechi},
  title = {Project Confluence: Complexity-Restoring Precision Oncology Framework},
  year = {2026},
  url = {https://github.com/cloudynirvana/project-confluence}
}

License

MIT License — see LICENSE for details.

Disclaimer

This is a research framework for computational exploration. It is not a medical device, clinical decision support system, or diagnostic tool. See DISCLAIMER.md.

"The measure of health is not the absence of disease, but the presence of complexity."

Name		Name	Last commit message	Last commit date
agents		agents
api_service		api_service
data		data
docs		docs
mirofish_engine		mirofish_engine
models		models
results		results
scripts		scripts
tests		tests
theory		theory
validation		validation
.gitignore		.gitignore
CALL_FOR_DATA.md		CALL_FOR_DATA.md
CITATION.cff		CITATION.cff
DISCLAIMER.md		DISCLAIMER.md
LICENSE		LICENSE
NOTICE		NOTICE
README.md		README.md
config.arc.yaml		config.arc.yaml
confluence_disease_panels.fasta		confluence_disease_panels.fasta
progress_report.md		progress_report.md
prompts.confluence.yaml		prompts.confluence.yaml
requirements-validation.txt		requirements-validation.txt
requirements.txt		requirements.txt
thesis_proposal.md		thesis_proposal.md
validate_controller.py		validate_controller.py

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Project Confluence

Abstract

Citation

🧬 Project Confluence

Core Hypothesis

Theoretical Foundation: Bounded Adaptive Coherence (BAC)

Unified Complexity Profile (UCP)

Architecture

Adaptive Therapy Controller (NEW)

Key Innovation

Three Policy Modes

Safety Constraints (Assurance Layer)

Monte Carlo Validation: 200 Uncertain Biological Scenarios

Mathematical Core — 15D SAEM Model

Quick Start

Convergence Implementation Status

PDAC Rogue Closure Model

🦞 AutoResearchClaw Integration

Repository Structure

🗺️ Mathematical-to-Code Mapping Registry

Pan-Cancer Support

📢 Call for Data

Three-Arm Validation Strategy

Validation Roadmap

Validation Walkthrough (Phase 1 Snapshot)

Safety & Regulatory

🇳🇬 Nigeria Clinical Guidelines Integration

What This Adds

Quick Start

Guideline-Aware Adaptive Controller

API Endpoints

Install Optional Dependencies

Contributing

Citation

License

Disclaimer

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