ABSTRACT: With fossil fuels becoming scarcer and more expensive, biotechnology offers an alternative way to produce fuels, chemicals, drugs and proteins. Using bacteria and algae as the catalysts of the conversions offers several advantages. Although microorganisms naturally produce a wide variety of chemicals, further engineering is required to achieve yield and productivity target values, and develop cost-effective production processes.
Bioengineers typically manipulate the process conditions to improve growth and production. Recent advances in synthetic biology have enabled us to better understand and manipulate microorganisms at the genetic level. Thus, the optimization can now be applied in both the macroscopic (reactor) and the genetic level.
Since microorganisms generally produce a mix of chemicals, the most intuitive genetic engineering strategy is to eliminate competing byproducts by deleting the relevant genes. Although this approach has achieved yield improvement for several products, it has also resulted in lower growth rate and productivity. As a result of the growth impairment, productivity targets are not met and the bioprocesses are economically infeasible. Thus, gene deletions may not always be the best strategy to achieve high yield and productivity targets.
To balance the trade-off between yield and productivity, the use of dynamic or time-dependent gene expression has been suggested instead of gene deletions, which is a static approach. In our work, we design a model-based dynamic gene expression control system to implement the dynamic strategy. The control circuit is composed of a genetic sensor and a controller module. This control system facilitates the design of programmable, self-monitoring, self-regulating microorganisms that satisfy the bioengineering objectives for chemicals production.
Biosketch: Nikolaos Anesiadis earned his PhD from the Department of Chemical Engineering and Applied Chemistry at University of Toronto. His research is focused on the interface between metabolic engineering and synthetic biology. In particular, he is interested in engineering microorganisms to overproduce target chemicals and building genetic circuits to optimize economic variables of bioprocesses such as yield and productivity. His vision is to develop genetically engineered microorganisms that support the commercialization of bioprocesses.
Teaching courses on process control, numerical methods and bioprocess engineering have helped Nikolaos to develop strong computational and experimental skills to implement his research objectives. The quality of his teaching has been recognized with the 2011 Best Departmental Teaching Assistant award. Dr. Anesiadis has served as a reviewer for the journals IET Systems Biology and Chemical Engineering Science.