Modeling, signaling and cytoskeleton dynamics: integrated modeling-experimental frameworks in cell migration.

Cell migration plays a central role in multiple biological contexts, from embryonic development to tumor progression. While prior research has shown how cells move in response to biochemical and mechanical signals, the full complexity of migration modes remains unclear. Amoeboid and mesenchymal migration differ in their mechanics and signaling requirements, yet the exact mechanisms distinguishing them remain unresolved. Single-cell migration contrasts with collective migration, where cells move together in coordinated patterns. These differences suggest that migration is not a uniform process but one shaped by environmental and internal cues. No prior work has fully integrated gene expression, signaling pathways, and mechanical forces into a unified framework. That uncertainty drove researchers to examine how computational models can bridge experimental observations and theoretical predictions. This gap motivated a review of recent modeling approaches that aim to capture the dynamic nature of cell movement. Prior studies have focused on isolated aspects, but this article seeks to highlight how modeling can synthesize diverse data types into a cohesive picture.

This review aims to synthesize recent computational and experimental approaches to cell migration and cytoskeleton dynamics. The specific problem lies in understanding how different migration modes arise from biochemical and mechanical signals. The motivation stems from the need to integrate multiscale data into predictive models. Researchers propose that such models could help distinguish between amoeboid and mesenchymal migration. They also aim to clarify how single-cell and collective migration differ in signaling and mechanics. The study focuses on how modeling can incorporate gene expression, signaling pathways, and mechanical interactions. By addressing these questions, the authors hope to identify gaps in current knowledge. The ultimate goal is to develop frameworks that unify experimental findings with computational predictions.

The authors reviewed recent computational models of cell migration and cytoskeleton dynamics. They analyzed studies comparing amoeboid and mesenchymal migration modes. The review also considered models of single-cell versus collective migration. Computational approaches were evaluated for their integration of biochemical and mechanical signals. The authors examined how models incorporate gene expression data into simulations. They assessed the role of external cues in shaping migration behavior. The review highlighted gaps in multiscale modeling of cell migration. Finally, the authors proposed strategies for integrating modeling with experimental data.

The review identified key differences between amoeboid and mesenchymal migration in computational models. Amoeboid migration relies more on actin-rich protrusions, while mesenchymal migration involves matrix degradation. Single-cell migration models often focus on individual signaling pathways. Collective migration models emphasize cell-cell adhesion and coordinated movement. Computational simulations revealed how mechanical cues influence migration direction. Biochemical signals were shown to regulate cytoskeleton reorganization. The review found that few models integrate gene expression with mechanical forces. The authors suggest that multiscale frameworks could better capture migration complexity.

The authors propose that integrated modeling-experimental frameworks are essential for understanding cell migration. They suggest that current models lack sufficient integration of biochemical and mechanical signals. The review highlights the need for multiscale approaches that include gene expression and signaling pathways. Computational models must better incorporate external cues to predict migration behavior. The authors emphasize that amoeboid and mesenchymal migration require distinct modeling strategies. They propose that collective migration models should include cell-cell interactions. The review concludes that gaps remain in linking experimental data with computational predictions. Future work should aim to unify diverse data types into a cohesive framework.