Edinburgh Research Explorer
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Willingness to take PhD students: Yes

Synthetic biology and Bioengineering: 

- Synthetic genetic programs for customized information processing and process control in cells; 

- Biosensing, biomanufacturing and biocomputing applications; 

- Engineered phage therapy and bacterial stress responses

Lab website: http://wang.bio.ed.ac.uk/

Education/Academic qualification

Doctor of Philosophy (PhD), Imperial College London

Area of Expertise

Research expertisesynthetic biology, bioengineering, biotechnology, microbiology

Biography

Dr Baojun Wang leads the Synthetic Biological Circuit Engineering Lab at the University of Edinburgh and is a UKRI Future Leaders Fellow working at the interface between biology and engineering. He is Reader in Synthetic Biology in the School of Biological Sciences and the cross disciplinary SynthSys Centre for Synthetic and Systems Biology. Dr Wang received a PhD in Bioengineering from Imperial College London (2011) and was a Research Associate at Imperial College before joined the faculty of University of Edinburgh in 2013. His research interests include building novel customised gene circuits for sensing, information processing and computing of multiple cellular and environmental signals with applications in diverse areas, for example, biosensing, biocomputing, biomanufacturing and biotherapies.

Websites

http://wang.bio.ed.ac.uk/

Current Research Interests

Dr Wang's research is primarily centered around Synthetic Biology, a fast growing discipline at the interface of biology and engineering with significant expanding application in diverse areas. My interests include both fundamental and applied synthetic biology with focus on three synergistic research themes, i.e. the foundational technology, healthcare and industrial biotechnology applications. 

We use a bottom-up engineering approach to design synthetic biological circuits and systems using characterised, exchangeable biological parts and devices. We build novel customised gene circuits for sensing, advanced information processing and computing of multiple cellular and environmental signals with applications ranging from biosensing, biocomputing and biomanufacturing to biotherapy

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