Computational modeling of type I collagen fibers to determine the extracellular matrix structure of connective tissues
Bibliographic record
Abstract
A method is presented for generating computer models of biological tissues. The method uses properties of extracellular matrix proteins to predict the structure and physical chemistry of the elements that make up the tissue. The method begins with Protein Data Bank coordinate positions of amino acids as input into TissueLab software. From the amino acid sequence, a type I collagen-like triple helix backbone was computationally constructed and boundary spheres were added based on known chemical and physical properties of the amino acids. Boundary spheres determined the contact surface characteristics of the collagen molecules and intermolecular interactions were then determined by considering the relationships of the contact surfaces and by resolving the energy-minimum state using feasible sequential quadratic programming. From this, the software created fibrils that corresponded exactly to known collagen parameters and were further confirmed by finite element modeling. Computationally derived fibrils were then used to create collagen fibers and three-dimensional collagen matrices. By resolving the energy-minimum state, large complex components of the extracellular space as well as other structures can be determined to provide three-dimensional structure of molecules, molecular interactions and the tissues that they form.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".