B.Sc. (Queen’s), Ph.D. (Alberta), P.Eng. (Ontario)
Room: WB341 | Tel.: 416-978-5889 | Email: firstname.lastname@example.org
Bill Burgess Teacher of the Year Award for Large Classes, Department of Chemical Engineering & Applied Chemistry, University of Toronto, 2014
Fellow, American Association for the Advancement of Science, 2009
Fellow, Chemical Institute of Canada, 1998
Syncrude Canada Innovation Award, 1997
Professional Engineers Ontario
My past research and contributions have been in the fields of process identification, control and design. In more recent years, I have decided to extend my research in these rather mature fields into the emerging field of systems biology. I have benefited tremendously from my collaboration with my colleague, Professor Krishna Mahadevan. I have also initiated a new collaboration with another colleague, Professor Prasad Dhurjati, at the University of Delaware. My goal in all of this work is to bring a system (identification, control and design) perspective to each project while learning evermore about biological systems.
Optimization of Biological Systems
An important component of metabolic engineering is computational modeling and model-based design of microbial metabolic networks. Computational methods are important for metabolic engineering due to the complexity of biological systems and the need for the systematic design and optimization of organisms that serve as chemical production platforms. Currently, genome-scale models of cell metabolism are being constructed for various organisms with increasing ease. These constraint-based models contain information on the reaction stoichiometry of an organism’s metabolic network and involve more than a thousand reactions and hundreds of metabolites. While this level of detail helps us understand cell metabolism at a systems-level, it also presents challenges when we want to design and/or optimize these systems using specific engineering objectives.
One contribution has been made through the development of a new algorithm that overcomes the computational complexity inherent in existing computational algorithms for designing optimal genetic manipulations to maximize microbial production of biochemicals.
A related, second contribution has been the development of a new algorithm to design strains that are robust (insensitive) to genetic and environmental perturbations that are encountered in industrial settings.
A third contribution addresses a fundamental problem in model-based design of microbial strains, namely that the practical utility of a design depends on the precision of the model. One method for improving model precision when the model contains uncertain parameters is to perform sensitivity analysis on the parameters. This has been addressed through the development of a new method for incorporating the results of such sensitivity analysis to improve model precision using models of metabolism that describe both reaction fluxes and metabolic concentrations.
Dynamic Metabolic Engineering
Recent advances in synthetic biology have equipped us with new tools for bioprocess optimization at the genetic level. In synthetic biology, genetic constructs are engineered and coupled with the natural genetic machinery in order to reprogram the cellular processes. Genetic devices can be thought of as the analogues of electronic parts such as sensors, switches, logic operators and actuators that perform specific tasks at different stages of the gene expression process. Genetic engineering has provided us with regulatory parts such as promoters, ribosome binding sites, riboswitches and RNA regulators to control gene expression at the transcription and postranscription/translation stage. Natural quorum sensing systems have been coupled with genetic devices to perform novel cellular tasks and reengineered to induce expression of recombinant proteins.
Previously, it has been shown that the dynamic control of metabolic fluxes can increase the amount of product formed in an anaerobic batch fermentation of Escherichia coli. In order to apply this control strategy, the genetic toggle switch is used to manipulate key fluxes of the metabolic network. A fourth contribution is the development of an integrated in silico design for the dynamic control of gene expression based on a density-sensing unit and a genetic toggle switch. This controller, when coupled to the metabolism of E. coli, is expected to result in increased bioprocess productivity.
A fifth contribution is the analysis of a mathematical model of this dynamic metabolic engineering strategy for serine production in E. coli. The model of the quorum sensing and the toggle switch involves many parameters of which a small number are identified as having a significant effect on serine concentration. Simulations conducted in this reduced parameter space have identified the optimal ranges for these key parameters to achieve productivity values close to their maximum theoretical values.
L. Yang, S. Srinivasan, R. Mahadevan and W.R. Cluett (2015). Characterizing metabolic pathway diversification in the context of perturbation size, Metabolic Engineering, 28, pp. 114-122.
N. Venayak, N. Anesiadis, W.R. Cluett and R. Mahadevan (2015). Engineering metabolism through dynamic control, Current Opinion in Biotechnology, 34, pp. 142-152.
N. Anesiadis, H. Kobayashi, W.R. Cluett and R. Mahadevan (2013). Analysis and design of a genetic circuit for dynamic metabolic engineering. ACS Synthetic Biology, 2, pp. 442-452.
K. Zhuang, L. Yang, W.R. Cluett and R. Mahadevan (2013). Dynamic strain scanning optimization: an efficient strain design strategy for balanced yield, titer, and productivity. DySScO strategy for strain design. BMC Biotechnology, 13 (8).
L. Yang, W.R. Cluett and R. Mahadevan (2011). EMILiO: a fast algorithm for genome-scale strain design. Metabolic Engineering, 13 (3), pp. 272-281.
N. Anesiadis, W.R. Cluett and R. Mahadevan (2008). Dynamic metabolic engineering for increasing bioprocess productivity. Metabolic Engineering, 10 (5), pp. 255-266.
L. Yang, R. Mahadevan and W.R. Cluett (2008). A bilevel optimization algorithm to identify enzymatic capacity constraints in metabolic networks. Computers and Chemical Engineering, 32 (9), pp. 2072-2085.