Ontodynamique is a formal ontological framework built from two independent axioms — Axiom I (being = doing: every entity is an act of self-maintenance whose cost is drawn from the very structure it maintains) and Axiom V (exteriority admits degrees) — from which it derives finitude, irreversibility, operational closure, constitutive normativity, and a compositional gradient that classifies any finite system as closure (endogenous cost-bearing), normative carriage (externalized cost), or aggregate (no cycle).
The central empirical prediction is R-XVII: perturbations targeting the structure of a system (its self-maintenance machinery) produce systematically larger displacement than perturbations targeting its input (its metabolic flow), at matched intensity. This asymmetry is indexed on the topological target of the perturbation, not on its amplitude.
📖 Summary Ontodynamique
📖 Manuscript: ontodynamique.com
🧪 Interactive notebook: Google Colab
📄 Empirical paper: arXiv:2512.09352
🔬 DPDR pre-registration: OSF: https://osf.io/unj7f
The R-XVII signature converges across three causally disjoint biological domains:
| Domain | Dataset | n | Cohen's d | Ratio S/I | p |
|---|---|---|---|---|---|
| Gut microbiome | MDSINE2 (Gibson et al. 2025) | 9 subjects | 1.16 | 1.61× | 0.0006 |
| Coral reefs | GCBD (van Woesik & Kratochwill 2022) | 34,393 obs | 0.39 | 1.80× | 1.96 × 10⁻⁴⁸ |
| Cancer pharmacology | GDSC (Iorio et al. 2016) | 216,764 obs | 0.52 | 1.85× | < 10⁻³⁰⁰ |
Cross-domain convergence: ratio S/I ≈ 1.8×, CV = 7.2%.
Specificity: under intensity-based classification, ratios diverge (CV = 33%). Over 100,000 random binary partitions, none achieves a mean ratio ≥ 1.3 (p < 10⁻⁵).
Validation splits:
- Coral reefs (CR-02A): temporal split at 2010 — d_TEST = 0.400, ratio S/I = 2.18×, d_TEST within bootstrap CI of TRAIN.
- Cancer (CR-02B): 70/30 cell-line split, 10 seeds — median ratio = 1.846×, CV = 1.3%, 10/10 significant.
The deductive core is mechanized in Lean 4 across 14 files:
- 504 theorems, 0
sorry, no domain axiom beyond Lean's standard logical axioms (propext,Quot.sound) - 2 axioms (I = α + β, V) + 1 corollary (IV)
- I-γ ("no act without mode"), II, III, VII derived as theorems
- 10 separating models proving inter-axiom independence (I ⊥ V)
- 6 separating models proving mutual independence of I-β components
- R-XVIII asymmetry derived via
template_saving
Key files:
| File | Content |
|---|---|
Lean/Autodynamique.lean |
Structural trunk (101 theorems) |
Lean/gradient.lean |
R-XVII/R-XVIII compositional gradient |
Lean/Conscience.lean |
Subjectivity chain, valence, paliers |
Lean/DPDRDerived.lean |
DPDR predictions (20 theorems) |
Lean/InterAxiomIndependence.lean |
Separating models |
Lean/SeparatingModels.lean |
I-β component independence |
Lean/DomainRestriction.lean |
I-β weakening map (94 theorems classified) |
Lean/Bridjes.lean |
Bridge hypotheses (microbiome, software debt) |
ontodynamiqueTheory/
├── Lean/ # Lean 4 formalization (14 files)
├── ScriptMDSINE2/ # Microbiome analysis (MDSINE2)
│ ├── 01_phase1_raw_metrics.py
│ ├── 02_phase2_corrected.py
│ ├── 03_phase3_interaction_matrix.py
│ └── 04_robustness_metrics.py
├── ScriptCorail/ # Coral reef analysis (GCBD)
│ ├── corail.py # Core R-XVII asymmetry test
│ ├── robustness_reef.py # 4 response transformations
│ └── reef_temporal_split.py # CR-02A temporal validation
├── ScriptGDSC/ # Cancer pharmacology (GDSC)
│ ├── GDSC1.py # Full analysis with sensitivity sweep
│ ├── GDSC2.py # Pathway-only classification (v2)
│ └── gdsc_cellline_split.py # CR-02B cell-line validation
├── ScriptCrossDomain/ # Specificity & cross-domain tests
│ ├── SpecifityCheck.py # 100k permutations, reversibility, target count
│ └── Sentsivity.py # Sensitivity analysis
├── output/ # Generated figures and JSON results
├── Ontoaudit4.lean # Standalone audit file
├── requirements.txt
├── run_all.sh
└── README.md
Open the Colab notebook — it handles all dependencies, data download, Lean installation, and runs every script with full output.
Pre-built image with all dependencies (Python 3.10, Lean 4, MDSINE2) — no installation, no restart.
# Run all tests
docker pull anthonygosme/ontodynamique:latest
docker run --rm -v $(pwd)/output:/app/output anthonygosme/ontodynamique
# Run a specific section
docker run --rm -v $(pwd)/output:/app/output anthonygosme/ontodynamique --section gdsc
# Interactive shell
docker run --rm -it -v $(pwd)/output:/app/output anthonygosme/ontodynamique bashAvailable sections: lean, mdsine2, gdsc, corail, yeast_hom, yeast_het, crossdomain, meta, artificial
# 1. Clone
git clone https://github.com/anthonyGosme/ontodynamiqueTheory
cd ontodynamiqueTheory
# 2. Python environment
python3 -m venv venv && source venv/bin/activate
pip install -r requirements.txt
# 3. MDSINE2 (microbiome scripts only)
git clone https://github.com/gerberlab/MDSINE2.git
pip install MDSINE2/.
git clone https://github.com/gerberlab/MDSINE2_Paper.git
# 4. Lean 4 (formalization verification)
curl https://raw.githubusercontent.com/leanprover/elan/master/elan-init.sh -sSf | sh -s -- -y --default-toolchain stable
export PATH="$HOME/.elan/bin:$PATH"
# 5. Run everything
bash run_all.sh| Domain | Dataset | Source |
|---|---|---|
| Microbiome | MDSINE2 | gerberlab/MDSINE2_Paper |
| Coral reefs | GCBD | BCO-DMO 773466 (DOI: 10.26008/1912/bco-dmo.773466.2) |
| Cancer | GDSC | Sanger dose-response |
The R-XVII asymmetry has been tested for robustness along multiple axes:
- Microbiome: 5/5 distance metrics significant (Bray-Curtis, Jensen-Shannon, Aitchison, Hellinger, Canberra; all p < 0.001)
- Coral reefs: 23/23 DHW thresholds, 9/10 ocean regions, 4/4 response transformations significant; sigmoid threshold stable at DHW ≈ 7.9
- Cancer: stable from IC30 to IC70; 9/9 pathways significant; dose-matched control preserves ratio (1.81×)
- Cross-domain specificity: no random partition (n = 100,000) achieves mean ratio ≥ 1.3; reversibility partition dominated 2.8× by R-XVII; target-count partition not operable cross-domain
- Gibson, T.E. et al. (2025). Ecosystem-scale dynamics from microbiome data with MDSINE2. Nature Microbiology.
- van Woesik, R. & Kratochwill, C. (2022). Global coral bleaching and environmental data. BCO-DMO. DOI: 10.26008/1912/bco-dmo.773466.2
- Iorio, F. et al. (2016). A landscape of pharmacogenomic interactions in cancer. Cell, 166(3), 740–754.
- Gosme, A. (2025). Causal symmetrization as empirical signature of operational autonomy. arXiv:2512.09352.
- Gosme, A. (2026). DPDR protocol — pre-registered. OSF: https://osf.io/unj7f.
Anthony Gosme — Independent researcher
ontodynamique.com
This work is licensed under CC BY 4.0.