@inproceedings{01H4D8NH0EDC7RHQP25GXTX97G,
  abstract     = {{The bio-revolution is upon us, with industrial biotechnology at the forefront of solving today's 
social, economic, and environmental issues through the use of micro-organisms or enzymes in 
sustainable bioprocesses. However, turning natural organisms into highly efficient microbial 
cell factories is still a slow and tedious process due to the complexity of their metabolism and 
the lack of efficient, high-throughput screening methods. Biosensors have the potential to 
change this. These synthetic genetic tools, based on reprogrammed transcription factor 
systems, enable real-time, in vivo monitoring and regulation of cellular processes, thus 
accelerating the development of microbial cell factories.
The goal of this research is to push the boundaries of the current biosensor technology by 
exploring the family of LysR-type transcriptional regulators (LTTRs). This group of 
transcription factors respond to a vast array of industrially relevant molecules yet remains 
understudied to this day. We tackled this discrepancy in two steps. Firstly, the relevant
characteristics of these transcriptional regulators, such as response curves, ligand specificity as 
well as orthogonality for integration of multiple sensors within one host, were studied in vivo
and in vitro to gain a better understanding of the LTTRs. Secondly, these features were 
optimized towards the use as biosensors via protein engineering methods such as domain 
swapping and specificity engineering.
By combining this new-found knowledge on LTTRs with a novel, high-throughput biosensor 
construction platform, we have rapidly expanded the number of available biosensors and
enhanced their capabilities. This puts us one step closer to creating tailor-made biosensors for 
each desired compound, thus truly unlocking the full potential of biosensors for industrial 
biotechnology.}},
  author       = {{Demeester, Wouter and De Paepe, Brecht and Planas, Antoni and De Mey, Marjan}},
  booktitle    = {{Metabolic Engineering Conference 2023, Abstracts}},
  language     = {{eng}},
  location     = {{Singapore}},
  title        = {{Unlocking transcription factor-based biosensors for industrial biotechnology}},
  year         = {{2023}},
}