Search

Analyzing the motion of multiple biological agents, be it cells or individual animals, is pivotal for the understanding of complex collective behaviors. With the advent of advanced microscopy, detailed images of complex tissue formations involving multiple cell types have become more accessible in recent years. However, deciphering the underlying rules that govern cell movements is far from trivial. Here, we present a novel deep learning framework for estimating the underlying equations of motion from observed trajectories, a pivotal step in decoding such complex dynamics. Our framework integrates graph neural networks with neural differential equations, enabling effective prediction of two-body interactions based on the states of the interacting entities. We demonstrate the efficacy of our approach through two numerical experiments. First, we used simulated data from a toy model to tune the hyperparameters. Based on the obtained hyperparameters, we then applied this approach to a more complex model with non-reciprocal forces that mimic the collective dynamics of the cells of slime molds. Our results show that the proposed method can accurately estimate the functional forms of two-body interactions -- even when they are nonreciprocal -- thereby precisely replicating both individual and collective behaviors within these systems., Comment: Accepted at NeurIPS 2024. Some contents are omitted due to arXiv's storage limit. Please refer to the full paper at OpenReview (NeurIPS 2024) or https://github.com/MasahitoUWAMICHI/collectiveMotionNN

Cell alignment often forms nematic order, which can lead to anomalous collective cell flow due to the so-called active force. Although it is appreciated that cell migration is driven by traction force, a quantitative evaluation of the relationships between the traction force, the nematic patterning, and the cell flow velocity is still elusive. Here we have found that cellular traction force aligns almost perfectly and is proportional in amplitude to the gradient of the nematic order tensor, not only near the topological defects but also globally. Furthermore, the flow in the monolayer was best described by adding nonlinear forces and a diffusion term derived from symmetry considerations. These nonlinear active forces enhance density instability but suppress bending instability, explaining why cell accumulation and dispersion can occur in neural progenitor cell culture while their ordering pattern is stable., Comment: 5 pages, 4 figures, Supplementary 14 pages, 11 figures

Many of the biological phenomena involve collective dynamics driven by self-propelled motion and nonequilibrium force (i.e., activity) that result in features unexpected from equilibrium physics. On the other hand, biological experiments utilizing molecular motors, bacteria, and mammalian cells have served as ideal setups to probe the effect of activity in materials and compare with theory. As has been established, however, biomolecules are chiral in nature, which can lead to the chiral patterning of cells and even to the left-right symmetry breaking in our body. The general mechanism of how the dynamics of bio-matters can couple with its own inherent chirality to produce macroscopic patterns is yet to be elucidated. Here we report that cultured neural progenitor cells (NPCs), which undergo self-propelled motion with nematic cell-to-cell interactions, exhibit large scale chiral patterns when flowing out from containers made by gel. Moreover, a robust chiral cell flow is produced along the boundary when the NPCs are cultured on substrates with edges. Perturbation by actomyosin inhibitors allowed control over the chirality, resulting in the switching of the direction of the chiral patterning and boundary flow. As predicted by a hydrodynamic theory analogous to the non-Hermitian Schrodinger equation, we find an edge-localized unidirectional mode in the Fourier spectrum of the cell density, which corresponds to the topological Kelvin wave. These results establish a novel mechanism of flow that emerges from a pool of bipolar cells, and demonstrate how topological concepts from condensed matter physics can naturally arise in chiral active systems and multi-cellular phenomena., Comment: 21 pages, 4+7 figures