Genetic programs for advanced cellular information processing and behaviour control

Lead Research Organisation: University of Edinburgh
Department Name: Sch of Biological Sciences

Abstract

Bacterial cells live in an ever changing environment and therefore are equipped with specific genetically-encoded sensors and signalling networks to continuously perceive and process the various environmental signals. In this sense, cells can be viewed as replicating living computers but with biochemical inputs and outputs. This project aims to design and construct synthetic gene circuits to program live bacterial cells with designer functions, in particular for advanced sensing, computing, information processing and control of multiple cellular and environmental signals with application, for example, in microbial cell factories.

You will be guided to construct various genetic programs including novel sensors, genetic logic gates, amplifiers, computing and memory circuits. The layering and integration of these circuit modules will lead to a programmable biological computer. The biological computer will then enable the programmed cells to have a range of intelligent capabilities for application in areas including biosensing, biomanufacturing and biotherapies. By example, the engineered tools can be applied to significantly enhance the production yields of some difficult-to-express, large or toxic therapeutic proteins in industrial scale bioreactors. You will be guided to develop new biological circuit design principles by exploiting design principles in other engineering systems such as modularity, orthogonality, systematic characterization and modelling to increase the predictability and scalability of gene circuit design and assembly.

The project will provide the student a comprehensive training of advanced molecular cloning and genetic tools, innovative microbiology and synthetic biology techniques and computational modelling skills. The research thus gives the student an inter-disciplinary research experience and cutting edge technologies exposure to prepare well for his/her future research career. The student may also benefit from the opportunity to work collaboratively with some of our industrial partners in biotechnology

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/R511912/1 01/10/2017 31/12/2022
1943681 Studentship EP/R511912/1 30/09/2021 30/09/2021 Yiyu Xiang