NSF2026: EAGER: Emergent Collective Behavior in a Developmental Model

在环境生物学(BIO/DEB)和综合活动办公室的NSF 2026基金项目的支持下，锡拉丘兹大学的Alison Patteson教授获得了这笔热切的赠款。这个项目解决了NSF 2026 Idea Machine自下而上出现复杂性的主题，重点关注生物科学的一个子领域。这个项目的首要目标是确定在模式细菌黄色粘球菌中管理紧急集体行为的潜在原则。细菌通常被认为是单细胞的，但许多物种是多细胞的群体。聚集、蜂拥和捕食等行为需要细胞协调它们的行动。我们知道，这些行为的能力是由基因控制的，但仅从遗传学上还不可能理解由此产生的模式。细胞依赖于复杂的信号通路来感知、整合和响应来自其他细胞和来自环境线索的各种信号来协调它们的行为。这个项目将制作一个公开可用的视频档案，记录遗传上不同的细菌的集群行为。这将为开发紧急集体行动模型提供独特的资源，并提供对多细胞进化的洞察。该项目采用两种方法。首先是使用高通量视频显微镜收集野生型和突变型菌株的集体子实体聚集的大型视频数据库。第二种方法是基于基因敲除对所产生的集体行为的影响，开发和扩展黄色粘球菌聚集的自推进粒子模型。识别细菌如何从相互作用的信号网络中自组织将是理解生物材料中出现的重要一步。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

With support from the Division of Environmental Biology (BIO/DEB) and the NSF 2026 Fund Program in the Office of Integrated Activities, Professor Alison Patteson of Syracuse University is awarded this EAGER grant. This project addresses the NSF 2026 Idea Machine topic of Emergence of Complexity from the Bottom Up, focusing on a subfield of the biological sciences. The overarching goal of this project is to identify the underlying principles governing emergent collective behavior in the model bacteria Myxococcus xanthus. Bacteria are often thought of as single celled, but many species act as multi-celled groups. Behaviors such as aggregation, swarming, and predation require cells to coordinate their actions. We know that the capacity for these behaviors is genetically controlled, but it is not yet possible to understand the resulting patterns from genetics alone. Cells rely on complex signaling pathways to sense, integrate, and respond to a variety of signals from other cells and from environmental cues to coordinate their behaviors. This project will produce a publicly available video archive of swarming behaviors in genetically distinct bacteria. This will provide a unique resource for developing models of emergent collective action and provide insights into the evolution of multicellularity.This project employs two approaches. The first is to assemble a large video database of collective fruiting body aggregation in wild-type and mutant strains using a high-throughput video microscope. The second approach is to develop and extend self-propelled particle models of Myxococcus xanthus aggregation based on the effects of genetic knockdown on the resultant collective behavior. Identifying how bacteria self-organize from interacting signaling networks would be a significant step forward in understanding emergence in living materials.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.