A public, timestamped hypothesis framework exploring whether biological persistence can be interpreted through systemic proportionality constraints.
This repository proposes that DNA may be understood not merely as hereditary information, but as a compressed historical memory of biologically admissible configurations.
The central systems question is:
Why do some biological architectures persist across time while others collapse?
The working hypothesis:
Persistence may depend partly on congruity, the proportional balance between functional value and systemic burden.
This repository develops that idea across:
- DNA as structural memory
- evolution as congruity filtering
- aging as proportional drift
- disease as threshold crossing
- resilience loss
- exploratory AI parallels
Scientific posture:
- hypothesis driven
- explicitly falsifiable
- systems level
- open for empirical stress testing
The genome can be interpreted as a compressed historical archive of constraint compatible configurations.
A biological system persists when its functional value remains proportionate to its energetic cost, informational burden, and structural complexity.
In its simplest form:
[ C_{bio}(t)=\frac{V_{function}(t)} {E_{metabolic}(t)+I_{regulatory}(t)+S_{structural}(t)} ]
Where:
- (C_{bio}(t)) is biological congruity over time
- (V_{function}(t)) is functional value, viability, reproduction, repair, adaptation
- (E_{metabolic}(t)) is energetic cost, mitochondrial load, thermodynamic dissipation
- (I_{regulatory}(t)) is informational burden, regulatory complexity, transcriptional noise, epigenetic instability
- (S_{structural}(t)) is structural cost, tissue organization, proteostasis, cellular architecture, damage accumulation
A living system remains viable when these dimensions stay proportionate.
When they progressively diverge, aging, disease, fragility, and collapse emerge.
DNA is not a perfect design.
DNA is not a complete instruction manual.
DNA is a memory of survived constraint negotiations.
It stores traces of what remained biologically admissible across time.
Evolution, in this view, acts as a filter of congruity.
It does not preserve what is ideal.
It preserves what remains coherent enough not to collapse.
DNA records configurations that maintained viability under real environmental, energetic, and structural pressures.
Selection does not simply maximize fitness.
It filters configurations that preserve proportionality between value and systemic cost.
Aging can be interpreted as a progressive loss of proportionality among biological controllers.
Mitochondrial decline, epigenetic drift, proteostasis failure, inflammation, and tissue degradation are not isolated phenomena.
They can be read as coupled signs of systemic incongruity.
Disease emerges when biological incongruity crosses a critical threshold.
The system may still function locally, but global proportionality is no longer preserved.
Artificial intelligence systems also face proportionality constraints.
A model can be powerful, but systemically incongruent if its value does not justify its energy cost, complexity, infrastructure, and risk.
The biological framework therefore offers a bridge toward proportional AI governance.
This repository is not presented as a completed biological theory.
It is a structured hypothesis architecture.
Its goal is to define a testable systems framework around biological proportionality.
Future work should focus on measurable proxies such as:
- mitochondrial efficiency
- epigenetic drift
- transcriptional noise
- proteostasis decline
- senescence burden
- inflammatory load
- tissue integrity
- functional resilience
- recovery time after perturbation
The key empirical question is:
Does a measurable decline in biological congruity precede visible aging, disease progression, or loss of resilience?
The framework must be falsifiable.
It would be weakened if:
- biological systems with declining congruity proxies remain functionally stable over time
- aging markers progress without measurable divergence in energetic, informational, or structural proportionality
- disease progression cannot be associated with threshold crossing in systemic proportionality
- recovery capacity does not correlate with congruity restoration after perturbation
The goal is not to create a metaphor.
The goal is to build a measurable framework.
If you reference this framework, please cite:
Romeo, Andrea. DNA as Structural Congruity Memory. Zenodo. 2026.
DOI:
https://doi.org/10.5281/zenodo.20190210
This work is related to, but distinct from:
- systems biology
- evolutionary theory
- robustness theory
- complexity science
- aging biology
- resilience theory
- constraint based evolution
- biosemiotics
- AI governance
Its specific contribution is the formulation of biological persistence as systemic congruity.
Planned structure:
/docs
01_foundation.md
02_dna_as_memory.md
03_evolution_as_congruity_filter.md
04_aging_as_congruity_drift.md
05_disease_as_threshold_crossing.md
06_testable_predictions.md
07_falsification.md
08_ai_parallel.md
/figures
/references
/notebooks
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## Status
Version 0.1
Initial public Zenodo release published.
Framework under active scientific development.