(294a) Designing Metabolite Biosensors for Bioprocess Monitoring Using Synthetic Biology

Synthetic biology is an emerging discipline which seeks to use engineering principles to design or redesign biological systems.  One of the clear application areas for this technology is the design of new biomanufacturing systems for the production of useful compounds including specialty chemicals and biopharmaceuticals.

Sensors are key to any industrial manufacturing process, where they are used to monitor important variables that have been linked to product yield and quality and are often linked to process control systems that can take corrective actions if these variables are not within defined limits.  Synthetic biology systems offer an opportunity to deploy biological versions of these sensors-- gene circuits and/or proteins which perform the same functions of monitoring and correction.  These can be included as part of the system design and offer advantages in terms of ithe speed at which they can respond and the diversity of actions which can be selected.

Biosensors can be designed to exploit any part of biology as the recognition event including transcription, translation, or post-translational signal generation.  Each of these has different advantages and utilities.  In this talk, I will focus on two types of biosensors: transcription-based biosensors which utilise inducible promoters as the signal transduction element and post-translational biosensors based on Forster Resonance Energy Transfer (FRET) which use protein conformational change as a signal output.  We have successfully applied both of these for monitoring the concentration of important small molecule metabolites in the production of biopharmaceuticals in mammalian cell culture.  The targets of our biosensors include carbon and nitrogen sources and waste products generated by metabolism.  I will cover all aspects of sensor design and validation from selecting the appropriate biological recogntion element through biosensors construction and testing.  Finally, I will show how biosensor signal can be used predict more complex outcomes such as biomass or protein yield.