Bayesian modelling for developmental systems biology

器官的形成由部署在细胞中的基因调控网络(GRN)的作用控制，这些GRN通过细胞接触和细胞信号网络与相邻细胞相互作用。这一建议旨在开发新的非参数贝叶斯方法来提取功能GRN中基因产物之间的定量输入/输出关系，这些GRN协调推动器官发生的模式、生长和形态发生。弗雷泽实验室正在改进和部署技术，这些技术将能够通过离散细胞群体的多重成像和基因组图谱来可靠地创建基因表达报告器，创建可用于观察GRN功能的各个方面的“测试点”。用不同的标记标记GRN的多个组分使得以特定细胞和时间分辨的方式读出GRN的定量状态是可行的。为了读出这些“测试点”，弗雷泽的团队正在改进在发育中胚胎的连续空间内对多个基因产物进行定量成像所需的设备。本项目将开发的基于贝叶斯的新型计算工具将被用于在不同的粒度尺度上对GRN进行逆向工程和分析，其基础是这些“测试点”的定量成像。最终，这项研究将能够通过结合实时、多基因报道、多重成像和贝叶斯建模方法来详细阐述更完整的GRN及其与关键形态发生事件的联系。一旦得到验证，成像和计算工具套件将广泛适用于在研究较少和可访问的系统中定义GRN。

Organ formation is controlled by the action of gene regulatory networks (GRNs) deployed in cells that are interacting with their neighbours through cell contacts and cell signalling networks. This proposal is designed to develop novel nonparametric Bayesian methods to extract the quantitative input/output relationships among gene products in the functioning GRNs that coordinate the patterning, growth and morphogenesis driving organogenesis. The Fraser Laboratory is refining and deploying technologies which will enable the reliable creation of gene expression reporters through multiplex imaging and genome profiling of discrete cell populations, creating "test points" that can be used to watch aspects of a GRN as it functions. Tagging multiple components of the GRN with distinct labels makes it feasible to read out the quantitative state of a GRN in a cell-specific and time-resolved fashion. To read out these "test points", Fraser's group is refining the equipment needed for quantitative imaging of multiple gene products over the contiguous space of a developing embryo. The novel Bayesian-based computational tools to be developed in this project will be used to reverse engineer and analyse GRNs at various scales of granularity, based on the quantitative imaging of these "test points". Ultimately, this research will permit elaboration of a more complete GRN and its linkage to key morphogenetic events by combining real-time, multiple-gene reporters, multiplex imaging and Bayesian modelling approaches. Once validated, the kit of imaging and computational tools will be broadly applicable for defining the GRN in less well-studied and accessible systems.