Creating synthetic regulatory modules to drive cell and tissue specific expression

分子生物学最具挑战性的技术问题之一是以高度特异的方式驱动基因表达，使其仅在细胞或组织中特异表达。尽管存在一些系统来驱动广泛组织类型中的基因表达，但在稀有或高度特异的靶细胞中驱动表达可能是非常困难的。在这个项目中，我们将使用分子生物学和机器学习的方法来识别组织特定基因表达的规则，以便我们可以设计合成的调控元件，可以驱动任何类型的细胞的兴趣表达。UKRI-BBSRC的优先领域：理解生命规则，应用机器学习和合成生物学。

One of the most challenging technical problems for molecular biology is driving gene expression in a highly specific manner so that it is only expressed specific in cells or tissues. Although some systems exist for driving gene expression in broad tissue types, it can be very difficult to drive expression in rare or highly specific target cells. In this project, we will use molecular biology and machine learning approaches to identify the rules of tissue specific gene expression so that we can design synthetic regulatory elements that can drive expression in any cell type of interest.UKRI-BBSRC priority areas: Understanding the rules of life, applying machine learning and synthetic biology.