SYNTHETIC LOCUS

ai meets genomics

The core drive for this project is to develop the capability to build gene regulatory landscapes, with defined properties, guided by Deep Neural Network based machine learning approaches.  To then subsequently use this ability, to not only build exploitable synthetic regulatory landscapes but also to ask fundamental questions of the mechanisms underlying mammalian gene regulation.

Up to the present, the field has relied mostly on loss of function studies or the modelling of natural mutations to interrogate the basic DNA elements, proteins, and their emergent properties involved in gene regulation.  These include not only the deletion or modification of regulatory or structural elements in paradigm genetic loci but also the degron based removal of chromatin components such as CTCF and cohesin from the nucleus as a whole. Such experiments have led to a far greater understanding of the nuclear components, structures and processes involved in gene regulation.  However, it is clear that the knowledge required to build a fully regulated locus is of a very different order to that required to modify or break an existing regulated locus which is the product of evolution. At the moment it is not clear how well we actually understand gene regulation to be able build a fully regulated locus to order.  However, it is also clear that building synthetic loci has the potential to tell us much more about how these systems actually function than by altering or breaking existing loci. The ultimate goal of the work is to sufficiently understand regulation as a system and to develop the computational design processes and synthetic biology protocols to effectively build bespoke regulated loci at will.

IN COLLABORATION WITH:

Jim Hughes, PhD, University of Oxford

Ron Schwessinger, PhD, University of Oxford

Nigel Roberts, University of Oxford