A model for the contraction kinetics of cytoskeletal gel slabs
Bibliographic record
Abstract
Cytoskeletal gels are engineered prototypes that mimic the contractile behavior of a cell in vitro. They are composed of an active polymer matrix and a liquid solvent. Their contraction kinetics is governed by two dynamic phenomena: mechanotransduction (molecular motor activation) and solvent diffusion. In this paper, we solve the transient problem for the simple case of a thin gel slab in uniaxial contraction under two extreme conditions: motor-limited or slow motor (SM) activation regime, and diffusion-limited or fast motor (FM) activation regime. The former occurs when diffusion is much faster than mechanotransduction, while the latter occurs in the opposite case. We observe that in the SM regime, the contraction time scales as t/t0∼(λ/λ0)−3, with t0 being the nominal contraction time, and λ and λ0 being the final and initial stretches of the slab. t0 is proportional to 1/w˙, where w˙ is the average mechanical power generated by the molecular motors per unit reference (dry polymer) volume. In the FM regime, the contraction time scales as t/t1∼(1−λ/λ0)2, with t1 being the nominal contraction time, here proportional to the ratio L2/D, where L is the reference (dry polymer) thickness, and D is the diffusivity of the solvent in the gel. The transition between the SM and FM regimes is defined by a characteristic power density w˙∗, where w˙≪w˙∗ gives the SM regime and w˙≫w˙∗ gives the FM regime. Intuitively, w˙∗ is proportional to D/L2, where, at a given power density w˙, a thinner gel slab (smaller L) or including smaller solvent molecules (higher D) is more likely to be in the SM regime given that solvent diffusion will occur faster than motor activation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".