Molecular modelling of the links between growth, environment and metabolism using Grb10 knockdown in Zebrafish.

与环境相关的生长和新陈代谢的协调控制是活生物体的基本特征。控制这些过程的基因网络之间的相互作用的表征允许可能操纵这些途径，以促进更好的生长曲线，重要的农业，和健康老龄化的表征，重要的medicine.We最近确定了GRB 10作为一个重要的基因连接生长和代谢响应纬度暴露于夏季日光1。Grb 10是调节胰岛素和IGF-I受体信号传导的衔接蛋白。我们已经表明，在斑马鱼的瞬时Grb 10敲低产生的重量与长度比降低的增长率的增加，并增加耗氧量，这意味着一个“更瘦”的代谢更活跃的表型。该项目将确定Grb 10敲除产生这种表型并改变与光照相关的代谢率的机制。该项目将体内整体动物代谢呼吸测量和生长表型测量与转录组数据的计算和数学分析联系起来，以了解生长期如何与代谢率协调以及受环境决定因素的影响。候选人将学习如何使用分子生物学中的传统实验室技能，结合动物处理，使用对照和CRISPR修饰动物对生长和代谢进行准确的表型测量。然后，这些观察结果将使用机器学习方法与转录组数据相关联。将与Zfin、Gene Expression Omnibus和ArrayExpress等数据库中储存的可用数据进行比较。因此，该项目将教学生“常规地将计算和数学技术应用于高质量的定量生物数据”，以及如何访问和利用BBSRC要求的“全面，综合和可互操作的数据资源”。

The co-ordinated control of growth and metabolism in relation to the environment is an essential feature of living organisms. Characterisation of the interactions between the networks of genes that control these processes allows possible manipulation of these pathways to facilitate both better growth profiles, important for agriculture, and the characterisation of healthy ageing, important for medicine.We have recently identified GRB10 as an important gene linking growth and metabolism in response to latitude as measured by exposure to summer daylight1. Grb10 is an adaptor protein modulating insulin and IGF-I receptor signalling. We have shown that transient Grb10 knockdown in Zebrafish generates an increase in growth rate with a reduced weight-to-length ratio and that oxygen consumption is increased, implying a "leaner" more metabolically active phenotype. This project will identify the mechanisms through which Grb10 knockdown produces this phenotype and modifies metabolic rate in relation to exposure to light.This project links in vivo whole animal metabolic respirometry and measurements of growth phenotype with the computational and mathematical analysis of transcriptomic data to understand how periods of growth are co-ordinated with metabolic rate and affected by environmental determinants. The candidate will learn how to use traditional bench skills in molecular biology, in combination with animal handling, to make accurate phenotypic measurements of growth and metabolism using both control and CRISPR modified animals. These observations will then be linked with transcriptomic data using machine learning approaches. Comparisons will be made to available data deposited in databases such as Zfin, Gene Expression Omnibus and ArrayExpress. This project will, therefore, teach the student to "routinely apply computational and mathematical techniques to high-quality quantitative biological data" and how to access and utilise "comprehensive, integrated and interoperable data resources" as required by the BBSRC.