CAREER: Self-organization mechanisms in Myxococcus xanthus swarms

智力价值近年来，自然界中微生物群落的普遍存在已变得显而易见。例如，细菌表面相关群落，即生物膜，是最常见的细菌生长模式，往往对环境压力或抗菌处理具有抵抗力。然而，目前还不清楚单个细胞如何自组织进入这些社区，以及社区作为一个整体如何对环境线索做出反应。该项目旨在发现动态单物种生物膜(群)中的自组织机制。在这些群体中，细菌表现出集体的表面运动，协同感知环境，执行集体发育程序，并经常分化成执行特殊功能的不同类型的细胞。生物膜形成机制的研究解决了与发育生物学中的问题类似的问题；将单个细胞的宏观表型和生化途径联系起来。即使当细胞的基因组组成已知时，将影响新出现的时空模式的突变与细胞间信号和运动机制联系起来仍然是一个挑战。从表型观测中破译这些机制是一个复杂的逆向工程问题，不能仅靠传统的实验研究来解决。将基于代理的建模和生物统计图像量化与实验相结合的互补方法将解决这一问题。黄色粘球菌是研究微生物合作、发育和集体运动的重要模式生物，其在生物膜扩散或聚集过程中的自组织作用是本研究的重点。这种细菌使用两个运动系统和多个感觉和信号通路在表面上移动，形成各种种群模式。这些模式揭示了单个细胞的运动性、协调性以及它们集体感知和响应环境提示的能力。这个项目将开发一种方法来解码不同的表型，并使用模拟实验观察到的模式的数学模型来揭示细胞间的相互作用。该项目的广泛影响包括开发新的方法，在传播细菌生物膜的过程中弥合亚细胞、细胞和多细胞尺度之间的差距。尽管该项目的重点是一个特定的模型系统(M.xantus)，但该项目将阐明集体运动行为背后的一般机制。50多个细菌属利用表面运动性形成各种类型的动态生物膜，其中许多在工业上很重要。为该项目开发的方法和软件将向世界各地的粘菌研究社区提供。所开发的工具和研究结果将被纳入以维基百科社区参与原则为基础的黄花分枝杆菌模式生物体数据库(XanthusBase)。在后基因组时代回答复杂的生物学问题将需要新一代生命科学家，他们在结合实验和计算方法方面接受过跨学科培训。为了扩大这一项目的影响，国际系统生物学研究所寻求改进和扩大不同层次的系统生物学教育，吸引不同的有才华的学生进入计算和系统生物科学领域，并为他们的培训做出重大贡献。该项目教育部分的基石是对博士后学者、研究生、本科生和高中教师及其学生的培训。此外，培训还包括一项外联计划，将来自任职人数不足群体成员的学生包括在内。

Intellectual MeritIn recent years the ubiquity of microbial communities in nature has become apparent. For example, bacterial surface-associated communities, biofilms, are the most common mode of bacterial growth and are oftentimes resistant to environmental stress or antimicrobial treatment. However, it is still not clear how individual cells self-organize into these communities and how a community as a whole responds to environmental cues. This project aims to discover the mechanisms of self-organization in dynamic single-species biofilms (swarms). In these swarms, bacteria display collective surface motility, cooperatively sense the environment, execute collective developmental programs, and often differentiate into distinct cell types that perform specialized functions. Research on mechanisms of biofilm formation addresses questions similar to those in developmental biology; connecting macroscopic phenotypes and biochemical pathways in individual cells. Even when the genome composition of the cells is known, relating mutations affecting emergent spatio-temporal patterns to mechanisms of intercellular signaling and motility remains a challenge. Decoding these mechanisms from phenotypic observations is a complex reverse-engineering problem that cannot be solved solely by traditional experimental research. A complementary approach combining agent-based modeling and biostatistical image quantification with experimentation will address this problem. This research focuses on self-organization in spreading or aggregating biofilms formed by Myxococcus xanthus, an important model organism for studying microbial cooperation, development, and collective motility. This bacterium uses two motility systems and multiple sensory and signaling pathways to move over surfaces, forming a variety of population patterns. These patterns reveal motility coordination of individual cells as well as their ability to collectively sense and respond to environmental cues. This project will develop an approach to decode diverse phenotypes and uncover intercellular interactions using mathematical models that mimic experimentally observed patterns.Broader ImpactBroader impacts of the project include the development of new methods that bridge gaps between subcellular, cellular, and multicellular scales in spreading bacterial biofilms. Despite the focus on a specific model system (M. xanthus), the project will elucidate general mechanisms behind collective motility behavior. More than 50 bacterial genera use surface motility to form various types of dynamic biofilms, and many of these are important in industry. The methods and software developed for this project will be made available to the Myxobacteria research community worldwide. Developed tools and results of the research will be incorporated into a M. xanthus model organism database (xanthusBase) based on Wikipedia principles of community participation. Answering complex biological questions in the post-genomic era will require a new generation of life scientists with cross-disciplinary training in combining experimental and computational methods. To broaden the impact of this project, the PI seeks to improve and expand Systems Biology education on various levels, attract a diverse pool of talented students to the field of computational and systems biosciences, and contribute significantly to their training. The cornerstone of the educational component of this project is training for postdoctoral scholars, graduate students, undergraduate students, and high school teachers and their students. In addition the training includes a plan for outreach to include students from members of underrepresented groups.