CAREER: Modelling Emergent Behaviour of Gene Networks Controlling Plant Stem Cells

气候的不确定性，耕地的日益稀缺，以及开发可再生生物能源的潜力，都将我们的注意力集中在我们对植物的依赖程度上。提高植物生产力的关键之一是更好地了解对其生长发育的控制。植物拥有干细胞，干细胞的分裂和分化对植物的生长和新器官的构建至关重要。目前对植物干细胞调控的理解并没有很好地解决所涉及的复杂的调控网络。该项目将使用模式植物拟南芥，在实验上确定在发育中的根中管理干细胞调节网络的基本特征，并将开发描述这些网络行为的准确数学模型。根获取植物所必需的营养物质，了解控制根干细胞行为的规则将有助于植物未来的发展，以促进作物改良。该项目将通过开发植物生物学和生物工程之间的课程，为学生提供交叉培训和教育。这些课程将根据不同目标受众的需求量身定做。研究团队将吸引更广泛的受众，从K-12学生到成年人，向公众介绍包括植物干细胞、植物生物工程和作物改良在内的重要问题。在系统水平上理解调节干细胞的分子机制的一个关键是能够在活的有机体的背景下分析网络的动力学。确定管理干细胞调控网络及其新出现的行为的基本特征将有助于准确理解管理细胞多能性的设计规则。该项目综合了生物、数学和工程科学的技术，以阐明调节植物干细胞命运和维持的机制。该项目的基础将基于两个拟南芥根干细胞群体的转录图谱，即皮层/内胚层初始细胞(CEI)和木质部(XYL)，以及维持干细胞处于未分化状态的静止中心细胞。转录动力学的贝叶斯模型和模型的扰动测试表明，MONOPTEROS3(TMO3)的靶标是CEI和XYL干细胞的关键调控因子。新的成像技术已经被开发出来，它将能够定量测量负责干细胞之间信号传递的分子因子的扩散、浓度和相互作用。通过将这些数据与数学模型相结合，该项目将更好地理解干细胞分裂是如何在空间和时间上受到精确调控的。该项目的研究目标是：1)表征负责干细胞调控的转录签名，并检验相互作用的转录网络在不同干细胞之间运行的假设；2)生成结合了空间和时间信息的数学模型，以描述干细胞之间的交流。在确定哪些参数对生成稳健的数学模型至关重要之后，将生成描述调节器信号如何在单元之间传播的模型，并将识别能够重置决策信号的扰动。这些努力将为比较植物和动物的干细胞发育提供一个框架，并将有助于在面临气候不确定性的情况下未来的植物育种努力。

Climate uncertainty, a growing scarcity of arable land, and the potential to develop renewable sources of bioenergy all focus our attention on the degree to which we depend on plants. One key to improving plant productivity is to understand better the control of their growth and development. Plants possess stem cells, whose division and differentiation is central to plant growth and the building of new organs. Current understanding of plant stem cell regulation does not address well the complex networks of regulation that are involved. This project will use the model plant Arabidopsis to experimentally identify the essential features that govern stem cell regulatory networks in the developing root, and will develop accurate mathematical models that describe the behaviour of these networks. The root captures nutrients essential to the plant, and knowledge of the rules controlling root stem cell behaviour will facilitate the future development of plants for crop improvement. The project will provide cross-training and education to students through the development of courses at the interface between plant biology and bioengineering. These courses will be tailored to the needs of a diverse target audience. The research team will engage a wider audience, ranging from K-12 students to adults, to inform the public on important issues including plant stem cells, plant bioengineering, and crop improvement. A key to systems-level understanding of the molecular mechanisms regulating stem cells is the ability to analyse the dynamics of networks in the context of a living organism. Identifying the essential features that govern stem cell regulatory networks and their emergent behaviours will contribute to an accurate understanding of the design rules governing cell pluripotency. This project integrates techniques derived from biological, mathematical, and engineering science to elucidate mechanisms that regulate plant stem cell fate and maintenance. The project's foundation will be based on the transcriptional profile of two Arabidopsis root stem cell populations, the cortex/endodermal initials (CEI) and xylem (XYL), as well as the quiescent centre cells that maintain the stem cells in their undifferentiated status. Bayesian modelling of transcriptional dynamics, and perturbation testing of the models, have revealed TARGET OF MONOPTEROS3 (TMO3) to be a key regulator of both CEI and XYL stem cells. Novel imaging techniques have been developed that will enable quantitative measurements of diffusion, concentration, and interactions of molecular factors responsible for signalling between stem cells. By integrating these data with mathematical modelling, the project will achieve an improved understanding of how stem cell division is precisely regulated in space and time. The project's research goals are to: 1) characterize the transcriptional signature responsible for stem cell regulation and to test the hypothesis that interacting transcriptional networks operate across different stem cells; and 2) generate mathematical models that incorporate spatial and temporal information to describe communication between stem cells. After determining which parameters are critical to generate a robust mathematical model, models will be generated that describe how regulator signals are propagated between and among cells, and will identify perturbations capable of resetting decision-making signals. These efforts will provide a framework for comparing stem cell development in plants and animals, and will contribute to future plant breeding efforts in the face of climate uncertainty.