A Systematic Review of Multiscale Mathematical Modelling of Cellular Mechan transduction Signalling: Methods, Architectures, and Future Research Directions
Bibliographic record
Abstract
Cellular mechanotransduction—the process by which cells convert mechanical stimuli into biochemical signals—plays a fundamental role in regulating cellular behavior, tissue development, and disease progression. Understanding this phenomenon requires integrative modeling frameworks capable of capturing interactions across multiple spatial and temporal scales, from molecular signaling networks to tissue-level mechanical responses. This systematic review presents a comprehensive analysis of multiscale mathematical models for mechanotransduction signaling. Advances in computational biology and applied mathematics have enabled frameworks that integrate mechanical deformation, intracellular signaling pathways, and extracellular matrix interactions. These models commonly combine continuum mechanics, reaction–diffusion systems, agent-based modeling, and stochastic simulations to describe the bidirectional coupling between mechanical forces and biochemical processes. Key signaling pathways such as Rho GTPase and YAP/TAZ are modeled using coupled reaction–diffusion and elasticity equations, illustrating how cell shape and substrate stiffness influence signaling dynamics. Multiscale approaches include hierarchical, concurrent, and hybrid frameworks, each balancing computational efficiency and biological realism. Emerging models also incorporate chemical–mechanical coupling to simulate tissue growth and morphogenesis. Despite progress, challenges remain in data integration, experimental validation, and computational complexity, though machine learning is improving predictive capabilities and simulation efficiency.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".