AI Insight
This study presents an agent-based mathematical model that incorporates fractional Brownian motion to capture temporal correlations in cell migration trajectories, going beyond classical models that rely on uncorrelated white noise. The model demonstrates that positively correlated noise significantly enhances directional persistence in migrating cells, even when angular reorientation is strong, by acting as a form of cellular memory that stabilizes trajectory patterns inherited from initial conditions. When applied to taxis scenarios, the interplay between the Hurst exponent and angular reorientation rate produces emergent behaviors that allow cells to simultaneously maintain persistence and respond to external directional signals.
Why it matters
Understanding how cells sustain directional movement has direct relevance to cancer invasion, wound healing, and immune cell trafficking, where migration persistence plays a critical role in disease progression and therapeutic outcomes. This computational framework could serve as a tool for interpreting experimental cell tracking data and designing strategies to modulate cell motility in biomedical contexts.
by Ignacio Montenegro-Rojas, Martín Andaur-Lobos, Karol Soler-Orozco, Diego Castelli-Lacunza, Cristina Bertocchi, Anastasios Matzavinos, Andrea Ravasio
The persistence of cell migration is a fundamental property of motile behavior, enabling cells to maintain directionality while adapting to fluctuations and external cues. This feature underlies essential processes such as development, immune responses, and cancer invasion. Classical mathematical models have offered key insights into directed migration, yet they often neglect temporal correlations arising from cellular mechanisms that stabilize polarity and protrusion dynamics. Here, we introduce an agent-based model based on stochastic differential equations that integrates fractional Brownian motion to explicitly incorporate translational autocorrelation in cell trajectories. We simulate migration as a function of angular reorientation and the strength of correlated noise. In this framework, temporal correlation stabilizes trajectory features inherited from initial conditions, whereas angular reorientation introduces variability that enables transitions between erratic and directed motion. Our simulations show that, unlike models driven by white noise, positive correlation markedly enhances persistence even under strong angular reorientation. Moreover, the combination of Dr and H gives rise to emergent behaviors, particularly in the presence of taxis, where persistence and responsiveness are jointly tuned. These results identify correlated noise as a proxy for intrinsic cellular memory and provide a versatile computational framework to interpret the diversity and complexity of migratory behaviors.
Source: Modeling cell migratory persistence through temporal correlations and angular noise