Computational Intractability Generates the Topology of Biological Networks
Bibliographic record
Abstract
Virtually all molecular interactions networks, independent of organism and physiological context, have majority-leaves minority-hubs (mLmH) topology. Current generative models of this topology are based on controversial hypotheses that, controversy aside, demonstrate sufficient but not necessary evolutionary conditions for its emergence. Here we show that the circumvention of computational intractability provides sufficient and (assuming P!=NP) necessary conditions for the emergence of the mLmH property. Evolutionary pressure on molecular interaction networks is simulated by randomly labelling some interactions as 'beneficial' and others 'detrimental'. Each gene is subsequently given a benefit (damage) score according to how many beneficial (detrimental) interactions it is projecting onto or attracting from other genes. The problem of identifying which subset of genes should ideally be conserved and which deleted, so as to maximize (minimize) the total number of beneficial (detrimental) interactions network-wide, is NP-hard. An evolutionary algorithm that simulates hypothetical instances of this problem and selects for networks that produce the easiest instances leads to networks that possess the mLmH property. The degree distributions of synthetically evolved networks match those of publicly available experimentally-validated biological networks from many phylogenetically-distant organisms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".