Chemistry is an integral part of our modern industrial economy and an aspect of the production of nearly all goods and commodities. Many chemical reactions require significant amounts of energy and costly, environmentally harmful catalysts. Dr. Howe’s research aims to discover alternative processes using enzymes, exploring not just how enzymes work, but how they work so efficiently, in order to inform how these biocatalysts can be applied to improve industrial processes.

Enzymes are not harmful to the environment and often accelerate difficult chemical transformations with incredible efficiency and specificity. In one particularly striking example, a reaction that would require hundreds of millions of years to occur spontaneously is carried out by an enzyme in a matter of milliseconds. As a result, a long-standing goal of protein science has been to identify and exploit these sorts of enormous rate accelerations through the development of “designer enzymes” that facilitate new, industrially useful chemical reactions.

In order to create efficient designer enzymes, more information is needed to understand how these catalysts work. Current enzyme design principles are unrefined, and the resulting designer biocatalysts invariably required further engineering. In order to progress to a point where enzymes with novel activities can be designed rapidly, this research program aims to develop a robust understanding of the origins of enzymatic catalysis.

Dr. Howe’s work will evaluate how enzyme mechanisms change over the course of evolution. The proposed research project is multidisciplinary, bringing together fields, such as synthetic, physical organic and computational chemistry, biochemistry, molecular biology and enzymology.

Through this research, he hopes to understand how enzymes can be tailored into designer biocatalysts to be used to complement or replace more traditional industrial processes, reshaping the $52-billion Canadian chemical industry into a more efficient, environmentally friendly operation.

Dr. Drover is recognized for his innovative research that aims to discover sustainable, clean energy solutions derived from chemical synthesis and metal-based reactivity, potentially leading to alternative energy sources that can power our everyday lives.

One of Dr. Drover’s current projects explores the design of molecular catalysts that can be leveraged for sustainable and practical applications. Such systems enhance reaction rates, providing an outcome that holds enormous potential for industrial and energy uses.

Dr. Drover’s research will help achieve a sustainable environment for future generations with a focus on new innovations to curtail greenhouse gas emissions, discover superior/more efficient routes for the synthesis of specialty chemicals, such as pharmaceuticals for health, agrochemicals to enhance crop yield and advanced chemicals, including those found in organic light-emitting polymers for lighting and other display technologies.

Major depressive disorder, or depression, is a highly prevalent and disabling illness worldwide. Currently one out of three patients is diagnosed as having treatment-resistant depression, a type of depression in which symptoms have not improved despite trying at least two antidepressant medications.

Dr. Voineskos’ work is focused on identifying neurophysiological biomarkers which distinguish the brains of individuals with treatment-resistant depression from the healthy brain, as well as demonstrate how brain stimulation, such as repetitive transcranial magnetic stimulation (rTMS), enacts its effect on the brain. In an attempt to enhance the therapeutic efficacy of a type of rTMS called iTBS, she is now looking at how to personalize this treatment by identifying predictive biomarkers of response in the brain.

The proposed research will use the advanced neurophysiological approach of combining investigatory transcranial magnetic stimulation with electroencephalography (abbreviated as TMS-EEG) to directly understand the differences in neurophysiological biomarkers between the brains of patients who have responded to iTBS and those who have not.

The aim is to identify a biological target in the brain able to predict treatment response. The biomarker levels will also be compared to data from a large group of healthy subjects to clarify the neurophysiological changes in depression and whether successful iTBS reverts the markers to healthy brain levels.

Dr. Voineskos’ work is poised to clarify the neurophysiological dysfunction found in depression and how iTBS exerts its effect, resulting in clinical measures that may predict response to this brain stimulation treatment.

The innovative predictive system which will be studied and further developed may also significantly improve remission rates in pre-screened individuals, sparing patients with treatment-resistant depression the unnecessary risk and frustration of ineffective treatment.

Predicting human behaviour is an important component of modern economic analysis. The ability to accurately model how individuals will change their behaviour in response to changing economic circumstances is valuable to policy- and decision-makers in a myriad of sectors. Dr. Allen’s work makes analysis more transparent in terms of what key features of data will be useful for analysis and how to relax modelling assumptions to get more credible results.

For example, a classic model assumes that people buy less of a good when the price goes up, however, this may be inconsistent with data. His research focusses on how to answer standard research and policy questions like this with aggregate data, where previous work has only focused on individual data. His work also investigates how to use models that are explicitly imperfect.

It’s currently unclear how to use typical models to forecast when they are imperfect. Dr. Allen’s research addresses this gap by outlining a way to measure how bad the assumptions of a model are and how to forecast using a model that is not perfect.

The central idea of Dr. Allen’s work is to “build in” that the model is an approximation. This allows for a forecast under the assumption the deviations from the model are “small.” Conceptually, this provides a novel way to use a model that is not perfect, which can increase the credibility of economic predictions.

Dr. Diamond seeks to unravel one of the biggest questions regarding the foundations of our universe, the quest to find dark matter particles in the laboratory. Success would revolutionize the very core of modern physics and bring a better understanding to the universe.

The Standard Model theory developed by particle physicists describes the fundamentals of matter and their interactions. Its predictions have been confirmed by generations of experimental tests. Yet, astrophysics provides convincing evidence that the Standard Model describes only about five per cent of the universe with about a quarter of the universe consisting of dark matter, which can only be observed through gravitational effects in astrophysical phenomena.

Experiments seeking to spot rare interactions of dark matter particles with Standard Model particles have yielded no conclusive discoveries so far, and neither has the world’s most powerful particle accelerator, the Large Hadron Collider (LHC) at CERN. In order to change this, Dr. Diamond worked throughout her early career to develop innovative dark matter search techniques at the LHC and at the SLAC Linear Accelerator.

She is currently engaged in the experiment SuperCDMS (Cryogenic Dark Matter Search). Using sensors to detect vibrations and electrical charges, they look for interactions of dark matter particles in germanium and silicon crystals kept at liquid helium temperatures.

The collaboration is building the next generation of the experiment in SNOLAB, Canada’s premier astroparticle physics facility located two kilometres underground in the Vale Creighton Mine near Sudbury, famous for discovering oscillations of the neutrinos that are now part of the Standard Model.

The evolution of this project continues to attract top scientists and large international investments of human resources and capital. Aiming for world-leading detection ability for dark matter particles spanning the next decade, SuperCDMS holds the potential to make another historic physics breakthrough in Canada.