Projects 2022

Taxol is a potent chemotherapeutic agent currently used extensively in clinical practice. Yet, existing methods for taxol production are insufficient for the current and foreseen demand, maintaining a high price of the drug. A sustainable solution is production of taxol in cell factories by reconstruction of its biosynthetic pathway. Recently, yeast has been successfully employed for production of valuable compounds, such as the antimalarial drug artemisinin. Still, taxol escapes microbial production mainly due to the complexity of its biosynthesis. Through evolution, taxol biosynthesis synchronizes several enzymatic activities in a multi-step, multi-compartment and non-linear fashion that is highly challenging to reproduce in simple microbes. In this project, we will develop yeast platforms for high level production of taxol precursors and reconstruct canonical or artificial biosynthetic steps in the taxol pathway for efficient production of taxol intermediates and derivatives. To this end, three objectives will be pursued: 1. Engineering lipid droplets as storage compartments for accumulation of hydrophobic taxol intermediates to avoid toxicity and increase their local concentration for substrate channeling in the downstream steps. 2. Gene mining of Taxus chinensis genome to identify yet unknown steps in taxol biosynthesis or novel biosynthetic activities that can be introduced by design in the taxol pathway. 3. Production of novel taxol-like compounds by combinatorial biosynthesis to introduce new hydroxylation and/or methylation steps in the taxol pathway.

In this project, students will modify the yeast lipid metabolism to engineer lipid droplets formation, gene mine candidate genes for different enzymatic activities that naturally occur in taxol biosynthesis or could be potentially engineered in this pathway and overexpressed these genes in yeast cells. The student will design a combinatorial biosynthetic approach for production of novel derivatives that could have improved activities. Specific processes, including physiological parameters, heterologous pathways efficiency, expression of active soluble or membrane proteins (e.g. methyltransferases, cytochrome P450s, aromatic prenyltransferases), formation of metabolic complexes and channeling of intermediate and final products will be considered for the development of modular yeast platforms. This project includes: (1) Identification of critical nodes for intervention in yeast metabolism (2) Gene mining of potential candidates for specific activities (3) Selection and design of parts and modules to be used in the systemic approach (4) Design of tools to engineer yeast cells.

Colorimetric and electrochemical biosensors own outstanding advantages such as easy instrumentation, high clinically relevant sensitivity, possibility of miniaturization and low cost. We combine bottom-up and top-down fabrication techniques to develop advanced nanostructured platforms for colorimetric and electrochemical detection of pathogens (such as SARS-CoV2), cancer biomarkers and bacteria gene diagnosis. To implement the platforms for point-of-care applications we aim to fabricate a fully functional and fully integrated colorimetric and electrochemical biosensor devices via a three-dimensional (3D)-printing approach to provide faster results in a portable fashion. 3D printing offers significant advantages in terms of scalability, equipment size, prototyping and manufacturing speed. The first objective of the project is to design and fabricate colorimetric and electrochemical portable device. The second objective is to implement the device for colorimetric and electrochemical detection of heat-inactivated SARS-CoV2 using developed protocols in Mahshid Lab. The 3D printed device will offer a wide range of application towards universal point‐of‐care systems.

Design of the holder using CAD or Solidworks fabrication of the holder using the 3D printer validation of the device with established assays preparing solutions and reagents for the experiments.

Background: One of the important causes of “accidental” death during, or occasioned by, surgery is air embolism, with obscure causes [2]. Importantly, many advanced surgery procedures today rely on catheters and on laparoscopy, which are sources of pressured gas introduced in human body, and conceptually prime causes of gas embolism. Objectives: The project aims to understand the physical processes involved in gas embolism in a surgery theatre, and to progress in finding better alternatives for the designs of the devices that are the source of pressured gas, e.g., catheters, insufflation devices.

The project involves the following modules: (i) articulate the physical phenomena causing air embolism; (ii) analysis of the present designs of devices deploying pressured air in the body; (iii) construct a model, both computational (e.g., CFD) and experimental (e.g., microfluidics mimicking blood vessels), on which gas embolism can be studied in vitro; and (iv) propose alternative, safer designs of catheter and insufflation device heads.

This is a creative open-ended modeling project that aims at parametric design of 3D tessellated structures. Two-dimensional tessellated arrays have evolved independently in multiple clades of animals and plants. They exist as a microstructural feature of the skeleton and the integument (skeletons of sharks and rays, carapace in turtles, seedcoats in plants). The numerous regular interfaces inbetween the repetitive tiles ensure flexibility and extraordinary toughness, fatigue-resistant behavior, and even autexicity of entire structures. It has recently been discovered that mineral in mammalian bones (including human bone) forms micrometer-scale tessellations of prolate ellipsoids (geometric approximation) that stagger in close-packing and collectively form vast 3D space-filling arrays throughout the organic matrix of bone tissue. This project seeks to determine and quantify the mechanical advantages of such 3D tessellations on skeletal biomechanics through modeling and mechanical testing of 3D-printed structures.

Design a generic 3D ellipsoid packing array using Rhinoceros/Grasshopper software. Identify variable parameters: oblateness/prolateness, stagger, ellipsoid-to-matrix ratio. Test the 3D models using simulated loading and FEA. Mechanically test 3D-printed models.

The oscillating device is a physiotherapy appliance for clinical conditions having abnormal painful muscular tone in the face and neck region, and a habitual (acquired) abnormal position of the lower jaw (mandible) and neck. The first prototype has been designed and assembled by a Capstone team in 2020-2021, and then refined by another team in 2021-2022 to improve the performance and physical characteristics of the device. This biomedical device lowers the muscular tone of the craniofacial complex by applying mechanical vibrations in the range up to 150 Hz. Such vibrations induce relief in habitual muscular tone and thus alleviate posteriorly misplaced (retrognathic) occlusion of the mandible, and clenching of teeth. When the mandible regains its physiologic position where teeth are normally out of contact at rest, the backwards displacement of the tongue and the pharynx is also expected to diminish – thus improving breathing. It is expected that applying vibration in short bouts will alleviate dental clenching, temporomandibular joint pain and dysfunction, obstructive sleep apnea, certain varieties of neck pain, and will improve head posture and facial appearance in the subject. The summer student will map the optimal amplitude and frequency range of the device using verbal feedback from consenting volunteers. They will survey the literature and customize a self-evaluation questionnaire regarding muscular hypertonicity, facial tension and sleep quality. Finally, they will apply optical 3D imaging methodology to the facial features of the study participants and use image correlation to quantify the position of the mandible.

Feedback-based mapping of the operational range. Questionnaire design. Image correlation protocol optimization.

This is an image processing module for a project on treating dental enamel microcracks. A tooth-toughening approach using small mineral-binding peptides and crosslinking strategies will be developed and assessed relative to crack quantification by micro-computed tomography. The project will be co-supervised by Dr. Reznikov (Bioengineering) and Dr. McKee (Dentistry). Following crack induction, samples of extracted human teeth will be scanned using micro-computed tomography before crosslinking treatment. Enamel cracks will be imaged and their collective surface area quantified, along with specimen geometry. This information will be used for normalization of the toughening effect of the experimental treatment, to be assessed using a 3-point bending test of crosslink-treated and control specimens. Crack volume/area calculation will require unbiased and accurate segmentation of the cracks and tooth tissue on a reconstructed 3D image. Crack segmentation will be accomplished using a deep learning algorithm and Dragonfly software. Considering the unbalanced character of the segmentation target classes (i.e., relatively small crack volume with respect to specimen volume), we will design and train a customized convolutional neural network capable of identification of sparse features in a 3D image. This project module requires knowledge of digital image processing, basics of computer vision, understanding of loss functions, and familiarity with hyperparameter optimization for training of deep learning models. Understanding of beam theory and crack propagation theory is a strong asset.

Reconstruct 3D images Create expertly-labeled training dataset (ground truth) Optimize convolutional neural network hyperparameters and loss function Train different CNNs and analyze their performance Segment experimental data (up to 50 scans) Construct digital models of scanned specimens

The project aims at the development of (bio)materials and systems with improved optical and electrical properties for broad applicability to biosensor development, lab on a chip systems, and detection avoidance.

The student will perform material synthesis and systems integration. The student will also perform experiments, collect and analyze data, prepare reports and presentations.

Mucosal barriers are key components of the innate immune system that influence disease transmission by interacting with and sequestering pathogens, and by influencing pathogen survival at the point of transmission. To date, our understanding of the biophysical mechanisms governing interactions between pathogens and mucin glycoproteins, the primary structural components of mucus that largely determine its mechanical and biochemical properties, remains incomplete. This hinders our ability to understand and model pathogen dynamics in-host, particularly during the initial stages of infection with respiratory viruses, where causative pathogenic agents must traverse mucosal barriers and overcome host innate immune responses. Here, we will address this important gap by studying interactions between mucins and a library of fluorescent virus-like particles (VLPs) engineered to display the surface proteins of relevant respiratory viruses in the context of the early stages of host-infection. We will characterize the motion of these VLPs in both native mucus and in gels reconstituted from purified native mucins. The goal is to assess the role of mucin molecules in the binding/sequestering of pathogens and their impact on infection of host cells.

Contribute to the development of the pseudovirion library with collaborating labs. Generate particle tracking data for the pseudovirions in the various gels. Apply and adapt existing particle tracking code to study the transport of the pseudovirions through the gels.

The transmission of SARS-CoV-2, the causative viral agent of Covid-19 disease, will persist for the foreseeable future. Although it is now over 2 years since the first cases of Covid-19 were documented, circulation of SARS-CoV-2 has not transitioned to endemic, and the emergence of novel variants continues to pose a substantial challenge. Ultimately, the ability to model and predict longer-term SARS-CoV-2 circulation in the face of variant emergence, waning immunity, seasonal transmission patterns, and non-pharmaceutical intervention (NPI) adoption will be critical for mitigate the impact of this virus on the functioning of society. Our team has developed a compartmental epidemiological model for SARS-CoV-2 transmission in Canada. The model is stratified by age, and includes different immune phenotypes (i.e. zero, partial, or complete immunity) as well as viral strains and vaccine status. This project will involve two main components. First, we will finalize calibrating the model to epidemiological data from Canadian provinces, including cross-validation will less commonly used data sources such as serological data. Second, we will use the calibrated model to make projections regarding the timing and burden of Covid-19 infections (and hospitalizations) under different scenarios related to seasonality in transmission, variant emergence, booster dose administration, and NPI adoption.

Adapt and use existing code for the underlying Covid-19 model. Perform model calibrations, and use the model to run the code under different scenarios. Comparison of results with epidemiological data will be critical, as will be suitable data visualization.

The cell is the fundamental unit of life, yet the inner workings of the cell are far more complex than we ever imagined. Without a good model of the cell, it is difficult to develop new drugs to repair diseased cells, or build new cells to produce much-needed chemicals and materials. A key step towards building a working model of the cell is to map the complex network of interactions between thousands of tiny molecular machines in the cell called proteins. This project will focus on computer modeling of protein structures and networks. Various experimental and computational datasets on protein structures and networks will be integrated and visualized. The resulting integrated protein structures and networks will then be annotated with evolutionary and disease properties, with the aim to understand how protein structures and networks evolve, and how disruptions in protein structures and networks lead to disease.

Literature review. Becoming familiar with publicly-available datasets on protein structures and networks. Becoming familiar with existing computational tools on modeling protein structures and networks. Computer programming.

The motor proteins kinesin and dynein move along microtubules to transport cargoes and organize microtubules in the cell. Our goal is to understand how multiple motor proteins operate in teams, and how they are regulated to perform complex functions like cell division and directed transport. Through extending single-molecule techniques to native organelles and living cells, we have developed advanced microscopy tools to measure the regulation, motility, and forces exerted by motor proteins with unprecedented resolution, and to manipulate the system by applying external forces to the cargoes through optical tweezers and controlling motor activity using optogenetics. We will image and manipulate ensembles of kinesin and dynein as they transport native cargoes in reconstituted systems and living cells to understand how kinesin and dynein motors interact, how they are controlled to direct intracellular trafficking, and how motor proteins are misregulated in neurodegenerative disease.

Student 1: Quantify the number and type of motor proteins associated with organelles using superresolution fluorescence microscopy (SIM and STORM). Student 2: Use the optogenetic inhibitors we developed to test the role of kinesin-1, -2, and -3 in the motility of endoplasmic reticulum-associated organelles.

Optical tweezers (or optical traps) use a tightly-focused laser beam to exert forces on micron-sized refractive objects. By attaching motor proteins to small latex beads, we can measure the forces exerted by single molecules. Our lab has also developed techniques to measure the forces exerted by motor proteins and characterize the viscoelastic environment in living cells. Here, we will modify our current optical trapping systems to develop a force-feedback optical trap that allows us to exert constant forces on motor proteins as they move along cytoskeletal filaments. The force is measured by collecting the light that passes through the bead onto a quadrant photodiode, and the position of the trap is controlled through an acousto-optic deflector. We'll then use this system to test how exerting constant forces along the microtubule alters the movement of cargoes transported by the motor proteins kinesin and dynein.

Student 1: (1) Develop optical tweezers capable of manipulating single molecules and measuring their nanometer-sized displacements and pN-level forces. (2) Program a simple feedback controller to maintain constant forces.