Bayesian modelling for developmental systems biology

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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.