Analysis of the spatio-temporal dynamics of a Rho-GEF-H1-myosin activator-inhibitor reaction-diffusion system
Bibliographic record
Abstract
This study presents a detailed mathematical analysis of the spatio-temporal dynamics of the RhoA-GEF-H1-myosin signalling network, modelled as a coupled system of reaction-diffusion equations. By employing conservation laws and the quasi-steady state approximation, the dynamics is reduced to a tractable nonlinear system. First, we analyse the temporal system of ordinary differential equations (ODE) in the absence of spatial variation, characterizing stability, bifurcations and oscillatory behaviour through phase-plane analysis and bifurcation theory. As parameter values change, the temporal system transitions between stable dynamics; unstable steady states characterized by oscillatory dynamics; and co-existence between locally stable steady states, or co-existence between a locally stable steady state and a locally stable limit cycle. Second, we extend the analysis to the reaction-diffusion system by incorporating diffusion to the temporal ODE model, leading to a comprehensive study of Turing instabilities and spatial pattern formation. In particular, by adding appropriate diffusion to the temporal model: (i) the uniform steady state can be destabilized leading to the well-known Turing diffusion-driven instability (DDI); (ii) one of the uniform stable steady states in the bistable region can be driven unstable, while the other one remains stable, leading to the formation of travelling wave fronts; and (iii) a stable limit cycle can undergo DDI leading to the formation of spatial patterns. More importantly, the interplay between bistability and diffusion shows how travelling wavefronts can emerge, consistent with experimental observations of cellular contractility pulses. Theoretical results are supported by numerical simulations, providing key insights into the parameter spaces that govern pattern transitions and diffusion-driven instabilities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".