Metabolic Engineering for Fuel and Chemicals
One of the advantages of genome-scale metabolic models is that it allows a systems biology based approach to engineering cells and in particular metabolism. The availability of a predictive model of whole cell metabolism allows a rational and systematic design of metabolic networks for fuels such as ethanol, butanol etc. and chemicals such as propanediol and butanediol.
Our group has developed a suite of novel algorithms that are highly computational efficient and are scalable and lead to a plethora of genetic engineering designs. We are now developing algorithms to improve the strain selection by considering the macroscopic bioprocess parameters of yield, titer and productivity. Finally, we have recently developed several exciting methods to consider the effect of uncertainty and environmental perturbations to develop robust strain designs which exploit the inherent principles of robustness native to biological systems. We have also developed concepts of orthogonal pathway design and improved methods for pathway prediction. In addition, we now working on applying these tools for the metabolic engineering of bio-nylon and 1,3 butanediol using a novel 2 step pathway.
Dynamic Control of Metabolism
With the rapid development of new tools in synthetic biology and systems biology, the dynamic control of gene expression has recently gained significant interest. Dynamic control has been implemented at the transcriptional, translation and post-translation levels, with each level of control offering different dynamic time scales and unique tools for their implementation. We have shown that implementation of dynamic control has the potential to greatly improve bioprocess productivity.
Recent progress in this field has been summarized in a review article.
Metabolic Modeling and Engineering for Human Health Applications
It has been estimated that the gut microbiome consists of ~1000 species of microbes which contributes to ~2 kg by weight, ~ 1 million genes, and a wide diversity of metabolic functions.There are several factors that affect the gut microbiome including diet, lifestyle, antibiotic use, colonization during birth, breastfeeding etc. and the dysfunction of gut microbiome has been implicated in diseases including obesity, diabetes, inflammatory bowel disease (IBD). In collaboration with researchers from Immunology (Prof. Philpott) and Pharmacy (Prof. Pardee), SickKids (Prof. Kim), we are developing communities of probiotic bacteria both native and engineered for therapeutic and diagnostic applications.
Whole Body Modeling of Human Metabolism for Personalized Medicine and Personalized Nutrition
In many instances of disease including diabetes, cancer and obesity, metabolism is drastically altered motivating the further investigation of metabolism for improved understanding of such disorders. However, as metabolism is regulated by a multitude of mechanisms that operate at different time scales including transcriptional regulation, substrate cycling, allosteric control and covalent modification of enzymes, understanding of these interacting mechanisms is essential to enable the design of therapeutic strategies for treating metabolic disorders. Our group is developing detailed models of metabolism under these disease states to elucidate the mechanisms underlying such shifts and to identify therapeutic targets. We believe that ultimately such models could be tailored to individuals that could result in personalized nutrition and medicine.
Metabolic Engineering of Low Cost Antibiotics
Tuberculosis (TB) is a treatable infectious disease which affects 9 million people and kills 1.4 million annually, mostly in developing countries, and which is increasingly resistant to treatment by standard first-line drugs. Multi-drug resistant TB (MDR-TB) is estimated to infect close to 500,000 people causing fatality in a third of the cases. In some regions, MDR-TB accounts for 28% of all TB infections. Treatment of MDR-TB requires more expensive second-line drugs that can cost up to $5000/patient; these drugs are therefore too costly for many patients in the developing world.
Currently, the second line TB drugs are synthesized using filamentous bacteria that are slow growing. Filamentous growth results in increased resistance to oxygen transport into the culture reducing the antibiotic yields, increasing the cost of synthesis of these crucial antibiotics. Recently, the biosynthetic pathways of a number of antibiotics have been elucidated. We are using tools derived from the emerging field of synthetic biology to engineer more industrially robust microorganisms to produce these and other antibiotics in order to significantly reduce the cost of synthesis of these drugs and to provide platforms for the discovery of novel antibiotics.
This project is supported by Grand Challenges Canada as part of the Stars in Global Health program. Grand Challenges Canada is funded by the Government of Canada and is dedicated to supporting Bold Ideas with Big Impact in global health. You can read updates about this project here.
Genome-scale Modeling of Metabolism
Methods for Modeling and Engineering Microbial Communities
Genome-scale models have been used extensively to describe microbial community dynamics including competition, cross-feeding, biofilms and syntrophy. Hence, this modeling approach is now an established tool for analyzing the metabolic potential of an environmental microbiome. We have developed novel microbial community modelling tools such as the Dynamic Multi-Species Metabolic Modeling (DyMMM), which has been applied extensively. We are currently extending these microbial community modeling tools and apply them for engineering microbial communities for applications in gut microbiome and environmental biotechnology.
Multi-Scale Optimization and Control of Biological Processes
Biological systems span several orders of magnitude in time scales (e.g., enzymatic reactions that take place in seconds-minutes, protein synthesis which takes a few minutes, cellular growth which takes a few hours, and finally, evolution at the organism levels which can take many days to years). Similarly, these systems are spread across and interact at multiple length scales from a few nanometers in an intracellular environment to several meters in the case of microbial communities in ground water and oceans. Several engineering disciplines (e.g., mechanical, electrical, & chemical) routinely use quantitative models for design and optimization of processes of interest. However, such rational approach to design and optimization has been possible in the life science only recently due to the lack of predictive large-scale models of biological processes in the past. Research activities in our group include the design of dynamic model-driven engineering strategies for biological process optimization and control across different length and time scales (i.e., from microscopic (intracellular) processes to macroscopic (bioreactor) processes). Applications can include metabolic engineering (e.g., increasing the rate of electrical current in microbial fuel cells, designing dynamic gene manipulation strategies for increased product yields), biomedical engineering (drug design and dosage), bioreactor control and optimization (designing optimal substrate and inducer feeding strategies), and bioremediation (determining the spatiotemporal substrate addition strategies to effectively stimulate microbial activity).
Model-based Engineering for Environmental Biotechnology
Model-based Engineering for Microbial Fuel Cells
In addition to the bioremediation of toxic metals, Geobacteraceae members can also transfer electrons onto an electrode in a microbial fuel cell environment. Although the coulombic efficiency of this process is high, the rates of electron transfer is low. Hence, approaches for understanding the metabolic processes in a microbial fuel cell and redesign of metabolism is required for increasing the rate of electron transfer. We have utilized metabolic models for engineering the metabolism in this bacteria to increase the rate of respiration as way of increasing the electron transfer onto the electrodes.