Single mutations frequently alter several aspects of cell behavior but rarely reveal whether a particular statistically significant change is biologically significant. To determine which behavioral changes are most important for multicellular self-organization, we devised a new methodology using Myxococcus xanthus as a model system. During development, myxobacteria coordinate their movement to aggregate into spore-filled fruiting bodies. We investigate how aggregation is restored in two mutants, csgA and pilC, that cannot aggregate unless mixed with wild-type (WT) cells. To this end, we use cell tracking to follow the movement of fluorescently labeled cells in combination with data-driven agent-based modeling. The results indicate that just like WT cells, both mutants bias their movement toward aggregates and reduce motility inside aggregates. However, several aspects of mutant behavior remain uncorrected by WT, demonstrating that perfect recreation of WT behavior is unnecessary. In fact, synergies between errant behaviors can make aggregation robust.

中文翻译：

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数据驱动模型揭示了对于粘菌聚集很重要的突变细胞行为。

单突变经常改变细胞行为的几个方面，但很少揭示特定的统计学显着变化是否具有生物学显着性。为了确定哪种行为变化对于多细胞自组织最重要，我们设计了一种新的方法，以黄球菌为模型系统。在发育过程中，粘细菌协调其运动，聚集到充满孢子的子实体中。我们研究了如何在两个突变体csgA和pilC中恢复聚集，除非与野生型（WT）细胞混合，否则无法聚集。为此，我们结合数据驱动的基于代理的模型，使用细胞跟踪来跟踪荧光标记的细胞的运动。结果表明，与WT细胞一样，这两个突变体均偏向聚集体移动，并降低聚集体内的运动性。但是，WT仍无法纠正突变行为的几个方面，这表明不需要完全恢复WT行为。实际上，错误行为之间的协同作用可以使聚合变得可靠。