Data-driven modeling reveals cell behaviors controlling self-organization during Myxococcus xanthus development

Significance Coordinated cell movement is critical for a broad range of multicellular phenomena, including microbial self-organization, embryogenesis, wound healing, and cancer metastasis. Elucidating how these complex behaviors emerge within cell populations is frequently obscured by randomness in individual cell behavior and the multitude of internal and external factors coordinating cells. This work describes a technique of combining fluorescent cell tracking with computational simulations driven by the tracking data to identify cell behaviors contributing to an emergent phenomenon. Application of this technique to the model social bacterium Myxococcus xanthus suggested key aspects of cell coordination during aggregation without complete knowledge of the underlying signaling mechanisms. Collective cell movement is critical to the emergent properties of many multicellular systems, including microbial self-organization in biofilms, embryogenesis, wound healing, and cancer metastasis. However, even the best-studied systems lack a complete picture of how diverse physical and chemical cues act upon individual cells to ensure coordinated multicellular behavior. Known for its social developmental cycle, the bacterium Myxococcus xanthus uses coordinated movement to generate three-dimensional aggregates called fruiting bodies. Despite extensive progress in identifying genes controlling fruiting body development, cell behaviors and cell–cell communication mechanisms that mediate aggregation are largely unknown. We developed an approach to examine emergent behaviors that couples fluorescent cell tracking with data-driven models. A unique feature of this approach is the ability to identify cell behaviors affecting the observed aggregation dynamics without full knowledge of the underlying biological mechanisms. The fluorescent cell tracking revealed large deviations in the behavior of individual cells. Our modeling method indicated that decreased cell motility inside the aggregates, a biased walk toward aggregate centroids, and alignment among neighboring cells in a radial direction to the nearest aggregate are behaviors that enhance aggregation dynamics. Our modeling method also revealed that aggregation is generally robust to perturbations in these behaviors and identified possible compensatory mechanisms. The resulting approach of directly combining behavior quantification with data-driven simulations can be applied to more complex systems of collective cell movement without prior knowledge of the cellular machinery and behavioral cues.