Guest Lecture: Design of bioswitches and cellular robots for metabolic engineering and synthetic biology

November 3, 2017 @ 10:00 am – 11:00 am
Wallberg WB215
200 College St
Toronto, ON M5T 3A1

Design of bioswitches and cellular robots for metabolic engineering and synthetic biology

Professor An-Ping Zeng,
Institute of Bioprocess and Biosystems Engineering,
Hamburg University of Technology

Despite impressive progresses in systems metabolic engineering and synthetic biology there are still several unsolved major problems in their practical applications for developing effective metabolic pathways and microorganisms for biosynthesis, including:

  1. identification of targets for advanced pathway engineering of productive strains, especially under industrially relevant and in vivo conditions;
  2. effective means with proper dynamic range and sensitivity for dynamic and concerted control of metabolic pathways;
  3. designed elements or devices from synthetic biology often not work well within the host cells, especially for highly productive strains;
  4. mathematical models of cellular processes often miss regulatory details inside cells and thus fail to guide biomolecular and cellular design.

In this presentation, I will illustrate some of our recent efforts to address these questions. First, I will present results on rational design of bioswitches (riboswitch and ligand-introduced allosteric regulation) with improved dynamic range and sensitivity for dynamic control of metabolic pathways. Then, I will address the question how we can use the host cell as “a computer or robot” to identify targets, evaluate designed parts and even to evolve the best design for a specific purpose? Cells are capable of information processing, rapid replication and performing tasks adaptively and ultra-sensitively. The key issue is how to let the cells “to compute or evaluate” the processes we are interested in and how “to output” the right results corresponding to the different inputs? To this end, we have used E. coli as an example to rein the computation abilities of cells by designing high throughput input and sensitive output systems based on its interaction with bacteriophage. The method is successfully demonstrated for target identification, evaluation of designs, evolution and selection of key enzymes for lysine bioproduction in E. coli.