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.

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.

In this project, we will use the optical trapping methods our lab has developed to map the viscoelastic properties of phase-separated condensates relevant to viral infection and biological materials.

- complete lab and laser safety training
- test and optimize optical trapping techniques on a simple system of phase-separated droplets using BSA and PEG.
- apply these techniques to understand how biological polymers and adhesive plaques are assembled from phase-separated precursor proteins.

Specialized metabolism is a process of remarkable genetic and biochemical plasticity leading to astonishing diversity of chemical structures. These natural products have provided humanity with a wealth of compounds for broad applications. Yet, accessing novel candidates with potent activities for drug discovery is extraordinarily difficult. The first revolution in natural product research has occurred with introduction of genomics and transcriptomics studies leading to discovery of several biosynthetic pathways of compounds of interest. Despite recent achievements, this process still stands on mechanistic knowledge and require laborious work. We will develop ACE2D, a disruptive Synthetic Biology approach that harnesses the power of selection to self-assemble biosynthetic pathways across kingdoms by mimicking the natural plant defense. Thus, we will engineer a modular approach in yeast that include an entry pressure component (M1), a randomized DNA library containing genes related to specialized metabolism under different selection markers (M2), and an exit response component against the induced pressure (M3). Undergoing rounds of adaptation, only cells acquiring the correct combination of genes in M2 for biosynthesis of compounds with M3 activity will be able to survive at increasing pressure in M1, thus enabling pathway self discovery, reconstruction and optimization. Increasing complexity of M2 to whole transcriptome or metatranscriptome level will enable production of a myriad of novel functional compounds. We will perform evolutionary projections of natural or artificial pathways, foresee recombineering rate and predict genetic targets to fine-tuning system performance. As observed in nature, the produced bioactive compounds may display combined functionalities. Thus, M3 products will be subjected to high throughput screening to discover activities beyond M1 pressure, such as antioxidant, antibacterial, antiinflammatory or cytotoxic.

Student 1. Establishing an in vivo pressure component in response to a specified functionality

Student 2. Constructing randomized DNA library from known DNA parts to reconstruct a known biosynthetic pathway as proof-of-concept

Student 3. Establishing a high-throughput system in response to the pressure component

This project addresses pressing issues in contemporary biotechnology: the challenges of producing highly complex structures required for potent bioactivity, and the urgent need to replace harmful synthetic chemistry methods with cheap and green technologies. It aims to reshape the microbial production of high value compounds by combining synthetic biology and computational science for rapid discovery of bioactive small molecules as potential drug leads.
The yeast Saccharomyces cerevisiae has been used since ancient times in fermentation processes for the production of food and beverages. Following advances in genetic engineering, yeast has become a workhorse of modern biotechnology, preferred by industry due to its robustness and versatility. Despite recent advances (artemisinin, cannabinoids, opioids, etc.), the economic gain from these efforts has been limited. Efficient production of structurally complex chemicals requires the development of advanced biosynthetic systems. Extensive efforts have been made to understand and engineer yeast metabolism for the generation of efficient cell factories. However, less attention has been placed on studying yeast membrane properties and their contribution to biotechnological applications. Several cellular and molecular mechanisms are greatly related to membranes and could be limiting the performance of yeast cell factories. Such processes include general yeast fitness, the expression of functional membrane proteins, and the export of molecules or proteins.
We hypothesize that differences in the properties between the membranes of yeast as host cell factory and other species as natural producers (e.g. plants) has a profound effect in the activity of heterologous membrane-bound enzymes and transport of substrates and products. We envision that by modifying the host membrane composition to mimic that of producer organisms or by engineering non-natural yeast membranes will improve performance of membrane-related bioprocesses reconstructed in yeast.

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.

Messenger RNA is a powerful technology that has shown great success for vaccination and therapeutic applications. This molecule is produced enzymatically through an in-vitro reaction using an RNA polymerase to synthesize RNA from a DNA template, named in-vitro transcription. This step of the RNA manufacturing process uses costly reagents, and the optimal parameters might differ for RNA polymerases of different origins. In this project, we will evaluate parameters for in-vitro transcription such as temperature, buffer composition, concentration of each reagent and incubation time. Once different conditions have been compared, we will select the best protocol for each RNA polymerase used, ultimately aiming to achieve higher RNA yield and quality.

Screen parameters for in-vitro transcription, quantify and analyze the resulting RNA with fluorescent assays and gel electrophoresis.

During the production of viral particles for the uses as vaccines, cell and gene therapies, robust quality control is required to ensure safety and efficacy. However, quantification of critical quality attributes (CQAs), like virus titer, require elaborate and costly assays that can take a whole day. This delay impedes rapid decision-making for effective control and optimization of the production process. Raman spectroscopy, renowned for its ability to detect, is emerging as a promosing technique for bioprocess monitoring. It is capable of quantifying multiple biocomponents via chemometric analysis, without the need of extensive sampling preparation. In this project, we aim to use develop the methods of using Raman spectroscopy to quantify CQAs in virus production. We will gather samples throughout the virus production process and quantify multiple CQAs using established methodologies. We will refine sample preparation methods and capture Raman spectra. We will develop robust chemometric models that can quantitatively corelate the signals with the traditional assays results. The proposal work can yield a novel process analytical technologies and it is the preliminary work of using Raman spectroscopy us in-line monitoring techniques.

The student will conduct viral production in shake flasks to collect samples. They need to quantify virus titer via ddPCR/TCID 50 as the reference. They are expected to develop sample preparation methods and chemometric models to analysis Raman spectrum, as well as operate the spectroscope to gather signals.

Biofluids are a rick bank of biomarkers that are indicative of person’s health status. These biomarkers range from small molecules to proteins to nucleic acids. However, tapping into this biomarker information requires performing complex bioassays at the point of need. Microfluidics enable integration of multi-step complex assays with portability, automation, and high throughput. Additive manufacturing or 3D printing is a promising and emerging technology with numerous applications in the development of novel materials and microfluidic devices for a wide range of biosensing applications. Mahshid Lab is currently developing microfluidic platforms for high-throughput and rapid diagnostics. Furthermore, Mahshid Lab combines bottom-up and top-down fabrication techniques for fabrication of advanced nanostructured platforms and their integration with fluidic devices for optical and electrochemical sensing. The facile integration of advanced additively manufactured microfluidics with high-performing sensing modules paves way for personalized medicine.

The aim of this project is to advance the development of 3D printed multiplexed microfluidic platforms for point-of-care (POC) diagnostic devices. Research tasks majorly involve, (i) developing and fabricating high-throughput microfluidic circuits (with unit operations) using additive manufacturing, including but not limited to stereolithography (SLA), digital light processing (DLP) techniques, (ii) integration and validation of nanomaterial based optical and electrochemical sensing platforms into a point of care format, (iii) computational simulations for fluidic and mechanical characterisations.

Machine learning and deep learning models are being integrated into biosensing projects for their data processing and analysis capabilities. Mahshid labs develops translational bio-nano-medical devices for point-of-care diagnostics and therapeutics applications. The lab is conducting translational research in nanomaterials-based sensing, point-of-care microfluidic devices, and integration approaches. We combined nanostructured platforms with optical readouts like bright field microscopes and surface-enhanced Raman spectroscopy (SERS) to generate various libraries. We have available image libraries from colorimetric assays for the detection of pathogens like bacteria and viruses, and spectral libraries derived from glioblastoma multiforme and medulloblastoma cancers. This project involves the development of custom-design data processing tools and optimization algorithms of our current data analysis algorithms (convolutional neural networks and support vector machines) to analyze the libraries and generate a prediction on the biomarker status. Prior experience with machine learning analysis and coding (required).

1. Design, implement, validate, and optimize add-on strategies (like feature recognition and data augmentation) to machine learning algorithms for effective data processing of our available libraries.

2. Optimize the algorithms currently in place for data analysis.

3. Integration of the code into a friendly readout system

To produce electricity from solar energy is of global importance to address the energy crisis and achieve sustainable development. Although cyanobacteria have always been the expert utilizing and converting solar energy, they barely have an effective circuit to export the generated electrons to form electricity on the electrode. No successful attempts of building artificial circuits in cyanobacteria have yet been reported. Recently, a syntrophic system consisting of cyanobacteria and electro-genic bacteria was demonstrated to be efficient in power generation (135~ 150 mW·m−2), which is quite inspiring. However, compared to electro-genic bacteria, which is vulnerable to growth conditions, yeast is considered more safe and quite robust under a wide range of growth conditions. Moreover, its mature genetic manipulation and broad substrate spectrum make yeast good candidate for exploring the engineered production of electricity. Therefore, in this project, we will build a consortium system, where cyanobacteria were engineered to produce lactate in high-efficiency, which could be further used by membrane-engineered yeast to produce electricity. Thereby, the charging (active electrons generation) and discharging (electrons export to out electrode) sector are assigned to photosynthetic and electro-genic microorganisms, respectively. Furthermore, nanomaterials would be explored to improve the innate electro-generation/transportation capacities of the yeast. This project will provide an alternative biological strategy to construct solar cells/panels. Once this technology and equipment achieve practical efficiency. They can significantly reduce the cost of electricity used in industrial production, and eventually benefit everyone. The knowledge developed in this project will foster innovations in yeast engineering and will advance the frontiers of advanced microbial production.

Relying on genetic engineering strategies, cyanobacteria will be engineered to produce lactate in high-efficiency. Afterwards, an extracellular electron transport EET circuit will be built in yeast. Then, these two engineered microorganisms will be co-cultured in a particular designed device, where nanomaterials will be used to on the surface of the electrode to serve as the electron transport highway (in nature, pili of exoelectrogenic bacteria plays similar role) and endow the whole system reliable and steady electricity-generation capability.

With the fast-growing world population and the negative impact of climate change on agriculture, food security is under a significant threat. Plant pests and pathogens (bacterial, fungal or viral) pose further risks on crop production and countermeasures make heavy use of synthetic pesticides often harmful to human health and environment. Moreover, fertilization practices are associated with increased greenhouse gases emissions. Microbial plant biostimulants have been used to improve plant ability to use soil nutrients and tolerance to abiotic stress, or more broadly to enhance soil fertility, promote recovery of degraded soil or facilitate yield preservation. However, their application as all-round products simultaneously acting as biopesticides and biofertilizers that can reduce cost and field interventions are yet to be developed.
We will develop novel biofertilizers as microbial consortia of crop-specific nitrogen-fixing bacteria, allelopathic-engineered fungi, and other natural microorganisms capable of fixation of atmospheric N2 and CO2. Using an interdisciplinary approach that combines synthetic biology, nanotechnology, microfluidics, biocomputation and artificial intelligence approaches, we will engineer tailored bacterial-fungal co-cultures with crop beneficial properties followed by scaled-up bioprocesses for high yield production.
Furthermore, we will introduce artificial intelligence for 1) prediction of pathway and strain optimization, bioprocess engineering and quantitative structure-activity relationship predictions and 2) screening and identifying beneficial microbes for a desired plant phenotype and establishing microbe-microbe or microbe-host interactions and predicting the behavior of the engineered microbial consortia.

We aim to: a) engineer crop-specific root colonizing bacteria for fixation of atmospheric N2 and CO2; b) developing synthetic soil microbiomes with probiotics abilities; c) screening microbial consortia, which can sustain fluctuating environmental conditions, robust colonization, prevalence throughout plant development and crop-specific beneficial traits.

This oscillating device is a physiotherapy appliance for correction of bruxism (involuntary clenching of teeth). This biomedical device lowers the muscular tone of the craniofacial complex by applying mechanical vibrations in the range 100-200 Hz. Such vibrations induce relief in habitual muscular tone and thus alleviate posteriorly displaced (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, 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.

Testing of the device, refinement of the application protocol, standardization of read-out metrics related to the head and neck posture, and the dental occlusion.

Imagine if you could explore rare historical and biomedical artifacts, up close, from anywhere in the world. What if you could examine them from all angles, from the outside and the inside, without risk of damaging these precious items? Our project aims to make this possible by creating a virtual reality (VR) museum. We are using cutting-edge technology to create digital 3D replicas of unique artifacts from two renowned museums at McGill University - the Maude Abbott Medical Museum and the Redpath Museum. These digital replicas will offer an unprecedented level of detail, allowing us to highlight and explain specific features of each object. These objects will then be placed in a VR environment, creating an interactive, immersive museum experience accessible to anyone with internet access. Furthermore, it can be an invaluable resource for education and research, allowing detailed study and comparison of these artifacts in a way that was previously impossible. This project will not only revolutionize the way we interact with museum collections, but it will also democratize access to these unique cultural and historical resources.

3D digital image processing and annotation. The images will be displayed in a cascade manner with an embedded possibility to zoom in (Google Earth style) with ever-increasing magnifications. The specimens and their components (e.g., individual bones or teeth) will be segmented, annotated and hyperlinked with other specimens for comparative studies. In total, we aim to accrue several dozens of 3D digital replicas of natural history and anatomy artifacts to be incorporated into the VR museum.

The primary role of bones in terrestrial animals is to support the organism and to enable locomotion. The bone response to loading has been refined by many millions of years of evolution, and it follows intricate adaptive pathways that can be empirically analyzed. Horse racing sits at the intersection of science, art, and business. On occasion, public enthusiasm and financial incentives drive racehorse training strategies beyond the natural physiologic capacity of bones to adapt to strenuous loading. As a result of reckless and/or unscrupulous training regimes, leg trauma may ensue in elite racehorses, often with fatal consequences. In this collaborative project with the Faculty of Veterinary Medicine of University of Montreal, we will analyse the inner architecture of the third metacarpal bone (aka cannon bone). Samples have been procured from 15 racehorses with different severity of bony lesions and functional impairment resulting from strenuous training. The bones have been imaged in 3D using microcomputed tomography. Using advanced image processing methodology involving deep learning-aided image segmentation, multidimensional anisotropy and texture analysis, principal component analysis and finite element analysis, the covert structural features of bone will be placed into the functional context of walking and racing. The aim of this project is to refine our understanding of adaptive capacity in equine bone and to identify safe and humane limits for racehorse training and competition intensity.

Analysis of X-ray tomographic images of bones

Sustainable functionalization and modification of materials is an important direction in material chemistry and bioengineering research fields. In particular, sustainable materials derived from natural sources such as plants and microorganisms can help address an increasing economic demand for eco-friendly products. Cellulose is a polymer of glucose subunits and is present abundantly in nature. Biocompatibility, bioactivity, and biodegradability make cellulose-based materials excellent low-cost platforms with a wide range of potential applications. Bacterial cellulose originating as a bioproduct of Kombucha SCOBY fermentation will be functionalized in situ with a variety of nanomaterials such as diatoms, carbon nanotubes, Ag nanoparticles, and PEDOT:PSS, for improved optical, mechanical, and electrical properties that can be used in biosensing applications.

1. Maintain SCOBY culture
2. Extract and process bacterial cellulose
3. Perform functionalization
4. Material characterization

The dominant transmission modes respiratory viruses, i.e. by droplets, aerosols, or direct contact, strongly determines the effectiveness of different non-pharmaceutical interventions that may be put in place in both pandemic and epidemic contexts. Yet, very little is known about the biophysics of the processes of droplet generation and viral entrainment during transmission events between hosts, and, in particular, about how the presence of viruses themselves may impact these phenomena. Here, we propose studying this using a simplified system of nanoparticles (NPs) with tunable surface biochemistries, and gels of various biological polymers including mucins, the primary solid component of saliva and respiratory mucus. Specifically, our project includes experimentally investigating the impact of NPs on the rheology and interfacial properties of biological gels, visualizing and recording the effect of NPs on the fragmentation of these gels following spraying events, and developing theory to relate the rheological and fragmentation results.

The student will conduct rheological experiments and use the fragmentation chamber and a high speed camera to collect data on droplet size distributions and mechanical properties for different fluid solutions and NPs. Situation permitting, the student may also be involved in preliminary studies of this setup using wildtype pathogen systems with collaborators.

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 how fluorescent virus-like particles (VLPs) engineered to display the surface proteins of relevant respiratory viruses move through mucin gels and transduce cells. The goal is to assess the role of mucin molecules in the binding/sequestering of pathogens and their impact on infection of host cells.

Support a Masters student in the generation of experimental data (particle tracks for the VLPs in the various gels and transduction studies) as well as the establishment of the cell transduction setup including growing and culturing cells. Apply and adapt existing code to study the transport of VLPs and the transduction of the cells.

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.

Paclitaxel (trademark Taxol) derived from the stem bark of the Pacific yew tree, Taxus brevifolia, is a widely used chemotherapeutic agent possessing significant anticancer activity. Recently, the development of synthetic biology has allowed for the biomanufacturing of several plant-based terpenoids in the Saccharomyces cerevisiae, with the most recent breakthrough achieved by production of the anticancer drug vinblastine. Highly intricate, taxol biosynthesis was recently reconstructed in tobacco plants. However, several steps were not functional in a microbial system, such as yeast. In this project, a multi-disciplinary approach spanning computational structural biology, enzymology and synthetic biology, will be applied to optimize the catalytic activity and product specificity of rate limiting enzymes involved in early steps of taxol biosynthesis for efficient reconstruction of these steps in yeast. The following objective will be pursued: 1. Machine learning-assisted directed evolution of taxol enzymes. 2. Homology modelling or molecular docking for rational and semi-rational mutagenesis to identify stabilized candidate variants. 3. Engineering variants and evaluated their activity in a yeast available platform.

In this project, the students will rationally design a mutagenesis strategy to improve product specificity of different rate limiting enzymes involved in taxol biosynthesis. This strategy include design of: (1) Machine learning-assisted directed evolution; (2) Alanine Scanning to examine the effect of residues at specific sites on protein function, (3) Rational mutagenesis by identifying residuals that are in proximity to or in the active site of target enzyme, (4) Semi-rational mutagenesis to engineer stable variants.