Current Reserach: Emergent Behaviors in M. xanthus
Time Lapse: 3hr, 40X Magnification
A flock of birds, containing thousands of individuals flying at high speeds, is able to execute complex behaviors and patterns at the scale of the flock. This seemingly simple behavior baffled scientists until recently. Explanations such as thought-transference (Selous 1931), electromagnetic communication (Heppner 1974), and chorus-line like maneuvers (Potts 1984) were proposed. However a much simpler explanation was found when the maneuvers of individual birds were modeled (Reynolds 1987). Reynold's modeling showed that behaviors of a flock of birds can emerge from three simple rules: 1) avoid crowding out your neighbors, 2) steer towards the average heading of your neighbors, and 3) steer towards the average position of your neighbors. Like many other complex behaviors and patterns in nature, a flock of birds does not need a leader or even high order thought processing. These behaviors instead emerge from chaotic systems following simple rules.
To understand how order can emerge from chaotic systems, the interactions between individuals must be determined using scientific observation and hypothesis testing. These interactions are then used to create computational models that simulate the behaviors of the individual members based on the rules of interaction. Computational models allow for the study of behaviors that are not apparent by studying the individual, a hallmark property of emergent complexity. Many important systems such as the interactions between stem cells to create organs, or between neurons to create thought processes have complex and vast interactions that currently can not be modeled in full. To begin to understand how nature has utilized the properties of emergent complexity, model systems must be developed. I study Myxoccous xanthus as a model system for emergent complexity by trying to undestand the cell-to-cell interactions that lead to rippling and development.
816 Biological Sciences Building
BS in Bioinformatics from Rochester Institute of Technology (2007-2011)
Data-driven modeling reveals cell behaviors controlling self-organization during Myxococcus xanthus development.
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.
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