Inferring nonlinear dynamics of cell migration (2404.07390v2)

Abstract: The motility of eukaryotic cells is strongly influenced by their environment, with confined cells often developing qualitatively different motility patterns from those migrating on simple two-dimensional substrates. Recent experiments, coupled with data-driven methods to extract a cell's equation of motion, showed that cancerous MDA-MB-231 cells persistently hop in a limit cycle when placed on two-state adhesive micropatterns (two large squares connected by a narrow bridge), while they remain stationary on average in rectangular confinements. In contrast, healthy MCF10A cells migrating on the two-state micropattern are bistable, i.e., they settle into either basin on average with only noise-induced hops between the two states. We can capture all these behaviors with a single computational phase field model of a crawling cell, under the assumption that contact with non-adhesive substrate inhibits the cell front. Our model predicts that larger and softer cells are more likely to persistently hop, while smaller and stiffer cells are more likely to be bistable. Other key factors controlling cell migration are the frequency of protrusions and their magnitude of noise. Our results show that relatively simple assumptions about how cells sense their geometry can explain a wide variety of different cell behaviors, and show the power of data-driven approaches to characterize both experiment and simulation.

• The paper demonstrates that combining experiments with a phase field model captures distinct nonlinear migration patterns in cancerous and non-cancerous cells under varied confinements.
• The model shows that cell size, stiffness, and noise magnitude critically determine transitions between persistent hopping and bistability.
• The findings imply that simple changes in adhesion geometry and cell tension can qualitatively shift migration dynamics, offering insights for cancer metastasis and wound healing.

Inferring Nonlinear Dynamics of Cell Migration

The paper, "Inferring nonlinear dynamics of cell migration," explores the complex phenomena of cell motility, particularly focusing on the migration patterns of eukaryotic cells, which are heavily influenced by their immediate environment. This paper examines how confined cells adopt different motility behaviors compared to those migrating on simple two-dimensional substrates, using a combination of experimental observations and data-driven modeling.

The research specifically investigates the behaviors of cancerous MDA-MB-231 cells and healthy MCF10A cells within controlled micropattern geometries. The authors elucidate that MDA-MB-231 cells exhibit persistent hopping in a limit cycle on a two-state adhesive micropattern, whereas they tend to remain stationary on average in rectangular confinements. In contrast, MCF10A cells demonstrate bistability, settling into either of the available basins with noise-induced transitions between states.

To capture and predict these behaviors, the authors employ a computational phase field model. This model assumes that contact with non-adhesive substrates inhibits the cell front, which is crucial for understanding the cell dynamics observed experimentally. The model's capability to predict different cell behaviors based on cell size, stiffness, protrusion frequency, and noise magnitude is a significant highlight. Larger, softer cells were predicted to persistently hop, whereas smaller, stiffer cells were more likely to exhibit bistability.

This phase field framework operates within a complex landscape of assumptions and parameter dynamics. It takes into account cell size and tension, deducing that relatively straightforward changes in these parameters can lead to profound changes in behavior, such as the transition between limit cycle and bistability. This insight offers a potential qualitative explanation for the differences observed between the motility patterns of cancerous and non-cancerous cells, based on measurable biophysical properties.

Furthermore, the research underscores the importance of geometry in cell motility. The model accurately captures behaviors across varying adhesive geometries, reinforcing the significance of environmental constraints on cell dynamics. A crucial element in the model is the interaction between the cell's intrinsic properties, like polarity and mechanical tension, and its extrinsic environment modeled by adhesion patterns.

In terms of broader implications, the paper suggests that techniques similar to those they have employed could be used to infer cellular dynamics in a range of applications, possibly extending to areas like cancer metastasis or wound healing, where cell migration plays a critical role. The combination of experimental data with computational modeling provides a potent approach to predict cellular behavior across different scenarios.

The authors speculate on future research directions, particularly the potential of data-driven models to explore the dynamic responses of cells to a variety of geometrical and mechanical cues in their environment. This insight could further inform our understanding of how cellular behavior is regulated in complex tissue environments and lead to advanced artificial intelligence applications in cellular biology to predict and manipulate cell behavior in vitro.

In conclusion, the research provides a substantial contribution to the field of cellular biophysics, modeling complex cell dynamics with significant predictive power and offering a robust framework to further examine the influences of environmental and intrinsic cell properties on observed motility patterns.