2024-2025 projects will be posted below
Please note that the deadline for students choosing projects is September 16th, and听the deadline for faculty submitting proposals is September 5th.
Supervisors: if you note an error in your posting, please email allen.ehrlicher [at] mcgill.ca
The Capstone Design Project Proposals listed below are available to undergraduate students registered in the Department of Bioengineering. Students interested in a particular project are welcome express their interest by sending an email to the supervisor offering the project. Note that each project has been created for teams of 1 to 4 bioengineering students.
- Roles and Responsibilities of Project Supervisors
- Industrial Sponsorship of Undergraduate Project Courses in Bioengineering: Information for Students, Staff and Industrial Sponsor
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Project 1 Title: Development of a Parallelized Light-Induced Protein Micropatterning Technique for High-throughput Patterned-Based Contractility Screening (PaCs) Applications
Supervisor: Prof Ehrlicher allen.ehrlicher [at] mcgill.ca,
PhD candidate Clayton Molter clayton.molter [at] mcgill.ca
Preferred Team size:听础苍测
Background and Objectives:
Cells in our body鈥檚 tissue exchange mechanical forces with their external environment which consists of their extracellular matrices or other cells. Aberrant changes in these mechanics can have profound consequences on our health and can even lead to diseases such as cancer. In cancer metastasis, for example, changes in cell-substrate forces may induce cells change their migration behaviour, leading to tissue invasion. These changes may be related to other biological processes, such as changes in protein expression and localization. Thus, there is a need to develop methods to quantify and elucidate how changes in cell contractility correlate with protein localization. However, techniques enabling these analyses are non-trivial and involve the combination of biophysical and biological assays which have historically been distinct. To date, relating contractile forces to endogenous expression and localization of protein with single-cell resolution remains to be a challenge. This is because localization studies of endogenous protein activity typically require immunofluorescence assays, which require the immobilization of cells. This practice is at odds with conventional methodologies used to quantify cell contractility, such as traction force microscopy (TFM) which require a cell-free reference image.
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The Ehrlicher lab has recently developed a method for the high throughput, reference-free quantification of single cell contractile work based on the cell-induced deformations of adhesive protein micropatterns, called Pattern-based Contractility Screening (PACS). Because no cell-free image is required, this technique has the potential to be used in combination with immunofluorescent assays, potential facilitating paired contractility/localization analyses. However, the existing platform used to prepare PaCs micropatterns requires time- and reagent-intensive laser photo-etching of individual patterns in microscopic areas, hence providing a barrier to scaling this technique up for larger samples. The Ehrlicher Lab aims to develop a separate method which employs photomasks and a single non-laser light source to enable simultaneous parallel printing of patterns across a large surface.
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The student design team will be responsible for developing and optimizing the parallel micropatterning protocol to enable a unified PaCs/immunofluorescence experiment protocol and analysis pipeline to enable the quantitative correlation of protein localization and cell contractility.
Description of Design Component: The design components are below.
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- Develop a photomask-based parallel printing method using widefield UV illumination to micropattern adhesive protein patterns on soft silicone substrates of various stiffnesses.
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- Optimize a high-throughput, parallelized PACs-workflow with a reliable method for re-identification of immobilized single cells after initial PACs image acquisition during immunofluorescence imaging.
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- Develop an analysis scheme capable of identifying and pairing separately acquired PACs and immunofluorescent single cell images in a high throughput manner.
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Given that all instruments, reagents, and fundamental protocols are available, it is expected that all components (a), (b), and (c) will be completed by the end of the project.
Skills to have or develop: Experience in microfabrication and microscopy will be an asset for the project. Programming and image analysis will be an asset for developing the data analysis scheme.
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Project 2 Title: Defining the role of adhesion proteins in cell migration
Supervisor: Prof Anmar Kadra anmar.khadra [at] mcgill.ca
Preferred Team size: 3
Background and Objectives: Cell migration plays a fundamental role in many (patho)physiological systems. Several key proteins are involved in regulating this process, especially those associated with cell polarity (consisting of a protruding front and a retracting back) and directionality. Although the underlying dynamics of these two features have been extensively studied, it remains unclear how these proteins interact collectively to generate different patterns of cell migration. To determine how such proteins interact within an integrated network, we plan in this project to use computational modeling approaches by developing an extended spatiotemporal model of not only the three key proteins: Rac/Rho/paxillin involved in defining cellular polarity and adhesion dynamics, but also other adhesion proteins that have been implicated in cell motility (including FAK, PAK, PIX, GIT, talin and vinculin). The many positive and negative feedback loops between these proteins are expected to produce complex dynamics that can exhibit multistability. The model will be implemented in a stochastic fashion using the Cellular Potts Model (CPM), a phenomenological cell-motility model that has a time dependent domain which protrudes and retracts in response to local concentrations of Rac and Rho, respectively. The CPM will be combined with the Stochastic Simulation Algorithm (SSA) and parameterized using experimental data from the Dr. Brown Lab at 不良研究所 (e.g., cell protrusion rate, circularity, perimeter, etc.). Model validation against the data will be performed using four different metrics, including cell speed, directionality ratio, protrusion rate and alpha-value. This virtual cell model will then be investigated numerically to unravel how mutations in key adhesion proteins can alter migration patterns.
I lead a lab in the Department of Physiology, consisting of 10 graduate students and 3 undergraduate students. Our lab specializes in computational modeling with applications in biology and medicine.
Description of Design Component: the candidates will expand on our earlier work modeling spatiotemporal dynamics of protein-protein interactions involved in cell motility. This will be done by taking advantage of the expertise currently available in the lab combining CPM with SSA. After finalizing the model, the candidates will parameterize the model based on migration data obtained from the Brown Lab using four different metrics for comparisons. They will then study the dynamics of the model in different parameter regimes representing certain mutations.
Skills to have or develop: Background in computational modeling and nonlinear dynamics will be very helpful.
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Project 3 Title: Development of a pipeline for analyzing neurophysiology data recorded from the brain
Supervisor: Prof. Amir Shmuel, amir.shmuel [at] mcgill.ca
Preferred Team size: Any
Background and Objectives: Neurophysiology is a method commonly used to record the brain鈥檚 activity. Previously, neurophysiology was performed using one contact electrode, yielding time-courses of activity recorded from a single brain site. With the advent of neurophysiology, we can now record activity simultaneously from several tens of brain sites. This increased spatial sampling makes it possible to study how different brain regions interact. The project aims to develop scripts for analyzing high throughput neurophysiology data of the brain.
Description of Design Component: The students will review material to learn the basics of brain activity and neurophysiology. They will receive data and develop analysis pipelines using available scripts from software packages. They will create an analysis package for multi-channel neurophysiological recordings of brain activity. They will document the methods and the results. They will write a detailed report. Novel methods have the potential for publication.
Skills to have or develop: The ideal candidates will have knowledge and skills in signal and/or image processing, statistics, and coding. The students will gain knowledge and experience in the basics of the brain鈥檚 organization, neurophysiology, preparing analysis pipelines based on existing software packages, and how to write a journal paper.
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Project 4 Title: Development of a pipeline for analyzing high-resolution functional MRI data of the brain
Supervisor: Prof. Amir Shmuel, amir.shmuel [at] mcgill.ca
Preferred Team size: Any
Background and Objectives: Functional MRI is a non-invasive imaging method commonly used to image the brain鈥檚 activity in health and disease. Previously, functional MRI was performed using 1.5 Tesla and 3 Tesla MRI scanners. With the advent of MRI hardware, there are now over one hundred 7 Tesla scanners worldwide. The Neuro's Brain Imaging Centre is home to one of these cutting-edge 7 Tesla scanners. 7 Tesla MRI boosts the signal-to-noise ratio of the images, thus making it possible to perform sub-millimeter functional MRI. This increased resolution makes it possible to probe brain activity at the mesoscopic scale of the brain's columns and layers, the building blocks of the functional organization of the brain. The project aims to develop scripts for analyzing high-resolution functional MRI data of the brain.
Description of Design Component: The students will review material to learn the basics of functional MRI and high-resolution functional MRI. They will receive data and develop analysis pipelines using available scripts from software packages. They will create an analysis package for high-resolution functional MRI of the brain. They will document the methods and the results. They will write a detailed report. Novel methods have the potential for publication.
Skills to have or develop: The ideal candidates will have knowledge and skills in signal and/or image processing, statistics, and coding. The students will gain knowledge and experience in the basics of the brain鈥檚 organization, neuroimaging, functional MRI, preparing analysis pipelines based on existing software packages, and how to write a journal paper.
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Project 5 Title: Causes, prevention and treatment of gas embolism
Supervisor: Prof Dan Nicolau, dan.nicolau [at] mcgill.ca
Preferred Team size: 3
Background and Objectives:
Background: One of the important causes of 鈥渁ccidental鈥� death during, or occasioned by, surgery is air embolism, with obscure causes. 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, tentatively, 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.
[1] Makary & Daniel. Medical error鈥攖he third leading cause of death in the US. BMJ 2016;353.
[2] Berlot et al., Uncommon Occurrences of Air Embolism: Description of Cases and Review of the Literature. Case Rep Crit Care. 2018; 2018: 5808390.
Present status of the work (published): Mardanpour, Mohammad Mahdi, et al. Investigation of air bubble behavior after gas embolism events induced in a microfluidic network mimicking microvasculature. Lab on a Chip, 2024. ().
Description of Design Component: The first major task of the project is to choose the right materials, from the mechanical properties, interface with biological fluids, and fabrication point of view. The second major task of the project is to design the microfluidics structures which would mimic blood vessels to an extent that will allow in vitro experimentation. Finally, the third design task is to optimise 鈥渂lood vessels on a chip鈥� devices, on which several surgery protocols can be attempted with a focus on avoiding gas embolism.
Skills to have or develop: Modelling and simulation, microfabrication, microscopy/imaging, diagnostic devices
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Project 6 Title: Designing universal molecular surfaces
Supervisor: Prof Dan Nicolau, dan.nicolau [at] mcgill.ca
Preferred Team size: 3
Background and Objectives:
Background: The shape of, and the physico-chemical properties on the protein molecular surfaces govern the specific molecular interactions in protein-ligand complexes. Therefore, studies as diverse as those on protein folding, protein conformational stability [3], inter- and intra- protein interactions, molecular recognition and docking; as well as applications-orientated ones, such as drug design, protein and peptide solubility, crystal packing, and enzyme catalysis, benefit from an accurate and precise representation of the molecular surfaces. Furthermore, for large, intricate protein complexes, such as ion-channels, mechano-sensitive channels, or molecular chaperones, where the biomolecular functionality occurs on the inner molecular surface of the complex, makes the precision of the representation of molecular surfaces even more imperative.
Objectives: Despite the urgent and important need for precision hydrophobicity, which is critical to biomolecular processes is rarely represented with precision, i.e., at atom-level, but often at aminoacid level. Perhaps more importantly, hydrophobicity is never universal, e.g., applicable to proteins, DNA/RNA, lipids and sugars, despite biomolecular processes involving all. To this end, the project aims to design a set of universal atomic hydrophobicity databases, and associated software to be used primarily in drug discovery, molecular biology studies and synthetic biology.
Present status of the work: Cho et al. 2024
Description of Design Component: The first major task of the project is to design a set of atomic hydrophobicities either from empirical data, or better from combinatorial molecular dynamics simulations. The second major task of the project is to design (or upgrade an existing) software program able to represent and quantify properties on any biomolecular surface. A third, possible design task is to demonstrate the superior advantage of this approach for a case study, e.g., Covid19 treatment, mRNA technology.
Skills to have or develop: computer science, biochemistry, drug design
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Project 7 Title: Bio-Computation using biological agents
Supervisor: Prof Dan Nicolau, dan.nicolau [at] mcgill.ca
Preferred Team size: 3
Background and Objectives: Background: Many mathematical and real-life problems, e.g., 鈥渢ravel salesman problem鈥� (TSP), protein structure, cryptography, cannot be solved, if reasonably large, by the present computers, which process the information sequentially (albeit with extreme precision and speed). These mathematical problems can be solved if (i) they are translated into a graph; (ii) this graph is translated into a design of a microfluidic network; and (iii) the fabricated microfluidic structure is explored by individual biological agents, e.g., microorganisms, which act as simple CPUs.
Objectives: The project aims to assess the individual and collective 鈥榗omputational power鈥� of individual biological agents in optimally partitioning the available space and taking optimal decisions. The project involves, tentatively, the following modules: (i) translate the problem of interest in a graph; (ii) fabrication of the network equivalent to the graph encoding the mathematical problem; (iii) exploration of the microfluidic network by agents, e.g., bacteria; (iv) readout of the bio-computed solutions. An alternative to the proposed research path consists in the use of digital microfluidics to accelerate the DNA computation, but many other variations of the concepts are possible.
Nicolau Jr. et al. Parallel computation with molecular-motor-propelled agents in nanofabricated networks. Proc. Natl. Acad. Sci. U.S.A., 113 (10), 2591-2596, 2016
van Delft, et al. Something has to give: Scaling combinatorial computing by biological agents... Royal Society of UK Interface Focus, 8 (6), 2018.
Full lecture on biocomputation and biosimulation:
Description of Design Component: The first major task of the project is to design a microfluidics network which will encode a mathematical problem, such as Travel Salesman鈥檚 Problem, with full consideration to fabrication, materials and scaling problems. The second major task of the project is to design the operation of such a computer, as tailored to various biological elements, e.g., bacteria, Euglena, paramecium, etc. A third, possible design task is to construct a system including automated imaging and fluidic inputs.
Skills to have or develop: microfluidics, computer science, microfabrication, microscopy/imaging, microbiology
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Project 8 Title: Rational device design for regulated drainage in glaucoma filtration surgery
Supervisor: Prof Caroline Wagner, caroline.wagner [at] mcgill.ca
Preferred Team size: 4
Background and Objectives: Surgical procedures increasing aqueous humor (AH) outflow, such as trabeculectomy and glaucoma drainage device (GDD) implantation, are performed to reduce intraocular pressure (IOP) and prevent further optic nerve damage in patients with refractory glaucoma. Among many factors, long-term surgical success has been attributed to the filtering capability of the formed bleb, a blister-like fluid collection. While studies have investigated key bleb parameters and morphologies associated with ideal functionality, its transient formation process remains poorly understood. The goal of this project is to develop a novel device for regulated AH flow in glaucoma management, which includes bleb formation and fluid mechanics at the core of its design.
At the early stages of the postoperative period while the bleb remains immature, the patient is prone to over-drainage of AH, which can lead to a dangerous drop in IOP. Clinicians have attempted to regulate the outflow of AH in both trabeculectomy and GDD with inconsistent success. As a result, an internally conducted survey of 75 licensed ophthalmologists shows that approximately 98% of clinicians seek a device capable of regulating AH through the GDD in the initial weeks after surgery, without the need for a mechanical valve or ligation step. In addition, long-term studies on filtration surgeries only show a success rate of 71.2% in the GDD group and 53.1% in the trabeculectomy group. As a result, this interdisciplinary project has the potential to generate new biomedical devices for improved and effective glaucoma management.
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Description of Design Component:
The project will consist of three phases:
Phase 1: Development of a mathematical / CFD model for bleb formation to investigate the effects of dynamic pressure phases on long-term surgical success. Through a combined mathematical and CFD approach, the team will develop a method to characterize the initial effects of filtration surgery on AH flow mechanics, bleb formation, and IOP. The model will be created by using postoperative anterior segment optical coherence tomography (AS-OCT) images to mimic bleb morphology in-silico through a clinically validated approach.
Phase 2: Exploration of rational designs to best regulate drainage of AH throughout the surgical postoperative phases. Using the validated model, associated constraints, and parameters, the team will explore various iterations for ideal flow management throughout the postoperative period. A novel device that allows regulated AH egress from the anterior chamber will be designed, with significant focus being placed on the desired formation of the bleb. Fluid mechanic experiments in-vitro will validate the design鈥檚 outflow capability.
Phase 3: Validation of design in enucleated eyes. The device tested in vitro will subsequently be evaluated by performing experiments using freshly enucleated eyes obtained from the Anatomy Laboratory Facilities at 不良研究所.
Skills to have or develop: Development and validation of CFD/mathematical models, Iteration and selection of design prototype, Prototype testing and validation, Material selection and manufacturing optimization, Manipulation of enucleated eyes.
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Project 9 Title: Deep Generative Models for Representing and Synthesizing Multiple Sclerosis Patient MRI
Supervisor: Prof Tal Arbel, arbel [at] cim.mcgill.ca
Preferred Team size: 3
Background and Objectives: Developing deep learning techniques that can accurately predict patient-specific disease trajectories across multiple treatments would enable the most effective therapy to be identified quickly. This would represent a significant step forward in patient care and AI-based personalized medicine. The overarching goals of this project is to explore how modern generative modeling techniques, developed in the context of 2D natural images, can be used to effectively compress high dimensional medical images over time, permitting the prediction of patient-specific temporal trajectories, as well as to synthesize the associated Magnetic Resonance Images (MRI) over time.
The student design team will work closely with Brennan Nichyporuk, a Research Scientist at Mila, as well as graduate students in Prof. Arbel鈥檚 lab, and will assist with (1) the development of generative modeling techniques (e.g. Diffusion Models) with the aim of building deep learning representations for personalized medicine in multiple sclerosis, and (2) with supporting infrastructure.
New deep generative models devised for medical image analysis over time have the potential to lead to concrete improvements in patient care and more trustworthy model predictions. In addition, the results of this work could lead to new understanding of individualized disease evolution and treatment response.
Brennan Nichyporuk is a Research Scientist at Mila. He is an affiliate faculty member in the Electrical and Computer Engineering department at 不良研究所, where he develops deep learning techniques for medical image analysis in association with the Probabilistic Vision Group, a part of the Center for Intelligent Machines.
Prof. Arbel leads an interdisciplinary research program that lies at the intersection of machine learning, computer vision and medical imaging for real-world applications in healthcare, particularly in the context of neurological diseases such as multiple sclerosis (MS). In recent years, her team has pioneered new scientific advances in medical imaging for personalized medicine and trustworthy and responsible AI, including explainability and generative modeling, uncertainty estimation, and increasing fairness by overcoming biases and improving generalizability. In addition to new scientific advances devised to overcome open challenges in developing deep learning models for medical applications, the work has shown great potential for concrete impact in patient care, particularly for complex , chronic and heterogeneous neurological diseases such as MS.
Description of Design Component: The students will assist in the development of several 3D generative models (e.g. StyleGAN, Diffusion) models for representing high dimensional MRI acquired from MS patients during clinical trials. The students will analyze and compare the results of different models. The students will examine the workings of the models for several temporal models developed in Prof. Arbel鈥檚 lab.
Experiments will be performed on a proprietary dataset available in Prof. Arbel鈥檚, consisting of MS patient images (and clinical data) acquired during different clinical trials, leading to over 70,000 patient images over time, along with lesion labels, treatment codes and progression outcomes. Results will also be examined on public datasets for other clinical contexts (e.g. ADNI Alzheimer鈥檚 Dataset).
Skills to have or develop: Candidates should have background in the development of deep learning models. The students will gain knowledge in developing and adapting modern generative models (e.g. GANs, Diffusion) in the context of real-world medical imaging applications. They will gain experience in preparing software pipelines to analyze the performance of the models. There is a strong chance that the students could join graduate students in writing a conference paper on the results.
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Project 10 Title: Multimodal AI for prediction of bladder cancer treatment response
Supervisor: Prof James Tsui, james.tsui [at] mcgill.ca
Preferred Team size: Any
Background and Objectives: Multimodal AI for prediction of response and survival in bladder cancer patients treated with organ preservation approaches.
The proposed project has the potential to enhance patient outcomes and quality of life by enabling more tailored and effective treatment strategies for bladder cancer. By enabling better patient selection, the tool developed in this project could lead to increased survival rates and a reduced disease burden.
This project will be led by Dr. James Tsui, Radiation Oncologist and Associate Member of the Biomedical Engineering Department. He is a physician and a machine learning data scientist. The students will also work closely with wet bench scientists at the Research Institute of 不良研究所.
Description of Design Component: 1) Develop a deep learning model using convolutional neural networks to identify the presence and assess the maturity of Tertiary Lymphoid Structures in histopathology of muscle-invasive bladder cancer tumor biopsies.
2) Apply advanced data representation and feature learning techniques to histopathology slides to enhance model accuracy and robustness.
3) Construct a multimodal AI-based classification model to predict patient response to concurrent chemoradiation therapy for bladder cancer.
Skills to have or develop: Machine learning skill.
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Project 11 Title: Cell-free production of template DNA to support mRNA manufacturing
Supervisor: Prof Amine Kamen, amine.kamen [at] mcgill.ca
Preferred Team size: 4
Background and Objectives: The proposed project aims to develop an in vitro enzymatic method for producing plasmids for mRNA cell-free production. This approach could overcome the limitations of standard PCR amplification, which becomes inefficient at large scales and replace current E.coli fermentations requiring large investments and extensive processing to produce high quality plasmid DNA.
This research has the potential to significantly advance plasmid production for various biotechnology applications, including cell and gene therapy applications and speed up mRNA vaccine manufacturing in the context of Global Pandemic Preparedness Plan.
National Research Council, Montreal site
and Viral Vectors and Vaccines Bioprocessing lab,
Description of Design Component: Key challenges of enzymatic production of plasmid include the high cost of additives like dNTPs and understanding why the reaction stops鈥攚hether due to complete dNTP consumption (less likely) or the need to regenerate other components such as ATP or co-factors (more likely). The first step of the project involves a literature review and digital twinning to develop potential solutions, which will then be tested in the lab. An innovative aspect of this project is the potential use of oligos that are assembled and then multiplied in the reaction, which could further reduce production timelines and manufacturing costs.
The deliverables of the project include 1) Identification of the critical process parameters for template DNA production, 2) modelling of the process to maintain the critical quality attributes of the template DNA end product and 3) design of the manufacturing process and 4) economical analysis to demonstrate potential industrialization of the synthetic manufacturing process.
Skills to have or develop: Molecular Biology, Synthetic biology, process design, modelling and digitalization
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Project 12 Title: Machine learning for identifying the brain鈥檚 cortical neurons and blood vessels
Supervisor: Prof Amir Shmuel, amir.shmuel [at] mcgill.ca
Preferred Team size: Any
Background and Objectives: Tissue clearing and light-sheet microscopy are cutting-edge methods for 3D imaging of tissue components, e.g., neurons and capillaries. With the advent of these methods, we can now quantify and model the 3D brain's organization at a high resolution (voxels with 1-2 micron-long edges).
The project aims to develop machine-learning scripts for analyzing high throughput cleared tissue with fluorescence emitted from neurons and cortical blood vessels.
The project will advance our understanding of the brain鈥檚 organization at the microscopic scale in the quest for understanding the function of the healthy brain and the pathophysiology of neurological diseases.
Description of Design Component: The students will review material to learn the basics of the brain鈥檚 organization and activity. They will receive data and develop analysis pipelines using available scripts from software packages. They will create an analysis package for identifying and segmenting neurons, blood vessels, and capillaries in the cerebral cortex. They will document the methods and the results. They will write a detailed report. Novel methods have the potential for publication.
Skills to have or develop: The ideal candidates will have knowledge and skills in signal and/or image processing, statistics, and coding.
The students will gain knowledge and experience in the basics of the brain鈥檚 organization, preparing machine-learning analysis pipelines based on existing software packages, and how to write a journal paper.
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Project 14 Title: Flux Balance Analysis of S. cerevisiae for the in silico optimization of metabolic fluxes in the mevalonate pathway
Supervisor: Profs Mario Jardon and Codruta Ignea, jardoncontla [at] mcgill.ca // codruta.ignea [at] mcgill.ca
Preferred Team size: 3
Background and Objectives: Isoprenoids play crucial roles in various biological processes and structures and are key precursors for the synthesis of valuable chemicals. They are produced in microbial systems and higher organisms via the mevalonate (MVA) pathway. Understanding and manipulating this pathway can enable the development of therapeutic interventions for certain metabolic disorders and benefit numerous biotechnological applications.
The overall objective of this project is to establish a platform from existing metabolic models that can enable to predict and test in silico the effect of interventions on the MVA pathway. In order to achieve this overall objective, the proposed specific objectives are:
1. Incorporate into the COBRA Toolbox () an existing metabolic model of Sacharomyces cerevisiae from established databases for Flux Balance Analysis (FBA) or Metabolic Flux Analysis (MFA) applications.
2. Interrogate in silico the effect of interventions on key nodes of the MVP, such as the stabilization of HMGR, a rate-limiting enzyme of the MVA pathway.
3. Since the MVA pathway is the source of high added value chemicals, a FBA/MFA will aim at identify promising targets in the MVA pathway for overexpression or downregulation, with the purpose of maximizing the output of a model metabolite, such as squalene.
4. Depending on the findings and the progress of the project, the most promising results can be validated in vivo in yeast cells.
Overall, isoprenoids are vital for a wide range of physiological functions and have significant applications in health, agriculture, and industry.
The research of the Ignea research group () aims to engineer biosynthetic systems for the production of high value compounds with a wide range of applications in medicine, industry and agriculture, using Synthetic Biology and Metabolic Engineering approaches. We develop innovative bioengineering tools and strategies, drawing inspiration from natural systems to redesign the metabolism of production hosts to improve performance of heterologous pathways and enzymes, regulate pathways, and optimize targeted production.
Description of Design Component: The proposed project corresponds to the first step in the DBTL (Design-Build-Test-Learn) cycle that constitutes the main paradigm of Metabolic Engineering and Synthetic Biology. It will establish the foundation for a systematic interrogation of the metabolic pathways under study that can be used as a solid foundation for the next steps in the DBTL cycle.
Skills to have or develop: The students participating in this project will become familiar with MATLAB and the COBRA Toolbox (COnstraint-Based Reconstruction and Analysis Toolbox), a Matlab-based environment widely used for Flux Balance Analysis (FBA) and Metabolic Flux Analysis (MFA). Previous experience with Matlab will be beneficial. Students who have experience with other platforms for FBA/MFA are welcome to apply.
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Project 15 Title: Engineering Substrate Selectivity and Catalytic Activity of Taxadiene 5伪-hydroxylase Involved in Early Steps of Taxol Biosynthesis
Supervisor: Profs Codruta Ignea, Brandon Xia, codruta.ignea [at] mcgill.ca; brandon.xia [at] mcgill.ca
Preferred Team size: 4
Background and Objectives: Project description (Background and objectives)
Paclitaxel (trademarked as Taxol), initially obtained from the stem bark of the Pacific yew tree, Taxus brevifolia, is a widely used chemotherapeutic agent known for its significant anticancer activity. However, the traditional production methods of taxol are highly inefficient and unsustainable standing on plant cell cultures or plant extraction combined with organic synthesis. Synthetic biology has emerged as a promising approach for the cost-effective and sustainable production of high value compounds, such as taxol, using in yeast (Saccharomyces cerevisiae) or other microbial platforms. However, the synthetic pathway requires substantial optimization to achieve industrial-scale production. A key challenge lies in the low conversion efficiency of taxadiene-5 alpha-hydroxylase (T5aH) in the early stages of taxol biosynthesis, which limits the supply of precursors for subsequent steps, thereby hindering the introduction of the taxol biosynthetic pathway in yeast.
In this project, students will: (i) review published data on the enzyme's structure and function and propose mechanisms for its redesign with the aim of enhancing productivity; (ii) employing enzymology and protein engineering knowledge, develop simulation pipelines for identifying candidate positions for enzyme mutagenesis; and (iii) rationally design enzyme variants and provide in silico analysis to evaluate their designs. The proposed mutants will then be tested in vivo within an established system in the Ignea lab.
With a current demand of 2.6 tons of taxol annually and prediction of expanded uses, the industrial sector will benefit from production of taxol at lower cost using microbial fermentation. A strong societal impact is anticipated as lowering the cost of drug production will translate into more available treatments per patient.
Description of Design Component: Students will define the desired outcome in the enzyme function, acquire structural information of the enzyme, and perform structural analysis such as active site analysis, binding pocket identification, and stability analysis. Subsequently, students will set up Molecular Dynamics simulations and analyze the trajectory data from MD simulations to identify key interactions, conformational changes, and regions of the enzyme that are critical for its function. Based on the MD simulation results and biochemistry knowledge, students will identify residues that could be mutated to achieve the desired functional change and perform in silico screening to predict and evaluate the effect of mutations on enzyme structure, stability, and activity. Finally, students will test the most promising enzyme variants in vivo to validate the predicted improvements in function.
Skills to have or develop: computer science, biochemistry, molecular biology and microbiology laboratory techniques
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Project 16 Title: Computer controlled gas mixing system for probing neural circuits of respiratory function
Supervisor: Profs Georgios Mitsis, Richard Hoge, georgios.mitsis [at] mcgill.ca, rick.hoge [at] mcgill.ca
Preferred Team size: 4
Background and Objectives: Background: Changes to blood levels of oxygen and carbon dioxide trigger several neural responses that are essential to homeostatic regulation. These responses can be affected by pharmacological interventions or disease, and are also associated with a person's subjective experience of stimuli such as exercise, changing environmental conditions, or impaired lung function. Much of our understanding of respiratory control mechanisms in health and disease has come from physiological studies (including neuroimaging studies) in which arterial blood gases have been manipulated accordingly.
Objectives: In this context, the main objective of the current project will be to improve a prototype system for the computerized control of the amount of O2 and CO2 inspired by human research participants breathing through a face mask. The main specific objectives are: (i) optimize the mechanical properties of the prototype system (tubing diameter, installation of pressure sensors and auxiliary pumps) to ensure adequate gas flow (ii) expand its capabilities to allow for closed-loop control of blood gases and the generation of alarms depending on the expired gas levels, (iii) desig face masks which will be compatible with the geometry of the coil of the 7T magnetic resonance system at the Montreal Neurological Institute.
A prototype system has already been developed. We anticipate that it will be used by several groups within 不良研究所's research environment and that it will provide significant insights into respiratory dysfunction as well as probing cerebrovascular health using advanced Neuroimaging techniques (fMR).
Description of Design Component: The design component entails three main elements:
1) Hardware and software for controlled mixing of inspired gas concentrations
2) Hardware and software for recording of expired gas concentrations
3) Breathing mask and tubing for delivery of inspired gas and sampling of expired gas
These components must be able to operate in an integrated fashion, during programmed sequences in which the inspired gas mixture is automatically changed under computer control according to a predetermined schedule. Some applications, such as magnetic resonance imaging (MRI) scanning during a gas manipulation, may require the mixing and recording hardware to be kept several meters away from the breathing mask and gas sampling ports. The breathing mask geometry must be compatible with other equipment that may be used during these experiments, such as an MRI head coil.
Certain components of the system, such as voltage controlled gas mixers and transducers for measuring expired blood gases, will be provided in the form of commercially available "off the shelf" modules. The main design work will be related to the computer interfacing, physical packaging, and system integration aspects.
The group will have opportunities to discuss the various design objectives, and consider the relative merits of different "off the shelf" options for system modules. Some fabrication work, such as light machining, 3D printing, and electronics assembly/soldering will be required.
Skills to have or develop: Requested:
- some programming experience is essential
- experience with computer-hardware-sensor interfacing, ADC/DAC systems, is an asset
- experience in basic fabrication methods, such as CNC machining and/or 3D printing is an asset
- understanding of basic fluid dynamic concepts
- basic understanding of human physiology (respiratory, nervous system) is an asset
To be developed:
- understanding of respiratory physiology, including blood gas transport mechanisms and chemoreceptor systems
- experience with medical gas practices and standards
- system integration skills, related to the various hardware and software components of the system
- experience in the safety and ethical considerations of instrumentation used with human research participants
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Project 17 Title: Development of machine learning algorithms for the classification of libraries collected from nanoplatforms with optical readouts to advance the detection of biomarkers of interest
Supervisor: Prof Sara Mahshid, sara.mahshid [at] mcgill.ca
Preferred Team size: 1
Background and Objectives: 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 conducts 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 detecting pathogens like bacteria and viruses, and spectral libraries derived from glioblastoma multiforme and medulloblastoma cancers.
Machine Learning-Assisted Raman Spectroscopy and SERS can be used as a universal platform for diagnostic applications which is nowadays a huge market.
Description of Design Component: This project involves the development of custom-designed 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).
Skills to have or develop: 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
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Project 18 Title: Design and fabrication of skin-interfaced microfluidics for biosensing
Supervisor: Prof Sara Mahshid, sara.mahshid [at] mcgill.ca
Preferred Team size: 1
Background and Objectives: Skin-derived biofluids are a rich bank of biomarkers that are indicative of a person鈥檚 health status. These biomarkers range from small molecules to proteins to nucleic acids. However, tapping into this biomarker information requires performing complex bioassays on the body. Microfluidics interfaced with the skin is a great tool for sample collection and handling on the skin. The microfluidic interface enables the incorporation of unit operations like mixing and valving on small amounts of fluid. Downstream, the sample is delivered to a nanostructured transducer that enables detection of the target of interest. The facile integration of skin-interfaced microfluidics with high-performing sensing modules paves the way for personalized medicine. To this end, the project aims to explore novel mechanisms of fluid handling in skin-interfaced microfluidics for biosensing.
Skin-interfaced microfluidics and sample delivery systems are the main parts of wearable point-of-care systems for health monitoring platforms which are promising approaches for improving personalized medicine and diagnostic applications which are nowadays a huge markets.
Description of Design Component: The design components of the project are, (i) developing and fabricating microfluidic circuits (with unit operations) for skin-derived fluids, (ii) computational simulations for fluidic and mechanical characterization, (iii) testing of microfluidic integration with a sensing platform.
Skills to have or develop: Experience with microfabrication and 3D printing is desired. The team will develop skills on
microfluidic design and nanomaterials
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Project 19 Title: Continuous Wearable Bladder State Detection Technology Through Microwave Sensing For Urinary Incontinence
Supervisor: Prof Emily Porter, emily.porter [at] mcgill.ca
Preferred Team size: 4
Background and Objectives: Urinary incontinence (UI) affects more than 200 million individuals worldwide, and is particularly prevalent in women, the elderly, those with spinal cord injuries, and children and young adults with intellectual or developmental disabilities. UI can have a significant negative impact on an individual鈥檚 quality of life, independence, and dignity. A wearable device that provides an alert as the bladder is approaching full could be very valuable in allowing users to void on time. A support tool for those with UI should be safe for 24/7 use, non-invasive, low-cost, and discreet. This project will focus on the design of a wearable bladder monitoring for detecting when the bladder is full of urine.
Electromagnetic (EM)-based solutions with applications in diagnostic, therapeutic, supportive or assistive medical technologies are promising due to their low-cost, minimally invasive nature, ability to provide highly frequent scan data, and the potential for combined diagnosis and treatment (i.e., theranostics), or combined monitoring and proactive intervention or correction. These characteristics are highly valuable in the context of UI as it requires a continuous monitoring approach that is non invasive. The primary objective of the project is to determine the optimal flexible antenna array configuration for accurately and robustly detecting bladder fullness. Using simulations and experimental testing on a custom-designed phantom, the team will work on translating this configuration into a user-friendly prototype that meets practical usability requirements.
Wearable, cost-effective, and continuous monitoring devices for bladder fullness have immense potential for delivering improved POC services to patients diagnosed with urinary incontinence. Knowing when the bladder is full can significantly reduce the risk of accidents, particularly for elderly patients and individuals with spinal cord injuries, who may struggle with timely recognition. This not only has a positive impact on the patient's psychological well-being by preserving their dignity but also alleviates the burden on caregivers and nurses, saving time and energy. As current commercial methods for bladder fullness detection rely on expensive and bulky equipment primarily available in hospital settings, this device offers a quick, reliable, and accessible solution. The resulting benefits extend to both patients and healthcare providers, while also contributing to the rapidly growing medtech field.
In the Electromagnetic Medical Technologies (EMT) Lab, we are interested in electromagnetic (EM)-based solutions with applications in diagnostic, therapeutic, supportive or assistive medical technologies. Primarily based in the radio-frequency and microwave frequency ranges, these EM-based technologies are promising due to their low-cost, minimally invasive nature, ability to provide highly frequent scan data, and the potential for combined diagnosis and treatment (i.e., theranostics), or combined monitoring and proactive intervention or correction.
Description of Design Component: The project involves exploring various microwave sensing configurations for bladder monitoring to identify the most effective design for continuous detection. Students will simulate different array configurations within the pelvic region and develop a dynamic phantom that mimics shape changes as the bladder fills. Experiments will be conducted to compare simulation data with the phantom's performance. The collected data will undergo analysis and post-processing to create intuitive visualizations. Ultimately, the project will culminate in the development of a prototype that prioritizes wearability and user-friendliness, adhering to design constraints.
Skills to have or develop: Tissue Simulation, Electromagnetics Simulation, Experimental Design and Data Collection, Microwave sensing, Phantoms, Data Analysis, Post Processing, Data Visualization
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Project 20 Title: Developing a Bioinformatic Pipeline for the Elucidation of Specialized Biosynthetic Pathways using Genomics and Transcriptomic Data
Supervisor: Prof Codruta Ignea, codruta.ignea [at] mcgill.ca
Preferred Team size: 4
Background and Objectives: Plant and marine secondary metabolites are a rich source of bioactive compounds with applications in medicine, food, cosmetics, agriculture, and even biofuels. Unfortunately, these compounds are produced in very small amounts in their native organisms and are often extremely difficult to synthesize chemically due to their complex structures. However, if the biosynthetic pathway of a metabolite of interest is known, it can be reconstructed in yeast with synthetic biology. The microorganism can then be cultivated via precision fermentation to produce the compound at larger scale and more sustainably. One way to identify genes that are involved in a biosynthetic pathway is to collect the transcriptomes of the native organism under different conditions and compare gene expression levels. This approach will inform which genes or transcripts correlate with production of the compound. At the genome level, sequence information helps in the identification of biosynthetic gene clusters that may facilitate pathway discovery and regulation by characterization of genes clustered together. There are various bioinformatic tools that are used to align, annotate, and analyse transcriptomic and genomic data. Current methods have specific functions to interrogate target transcriptomic and genomic data. However, most of these tools require programming knowledge to harness their full potential, which raises the barrier of entry into the field. This project aims to facilitate the search for novel genes in biosynthetic pathways by programming an automated, modular, and user-friendly bioinformatic pipeline that streamlines the process of analysing transcriptomic and/or genomic data.
This project will facilitate elucidation of specialized biosynthetic pathways for engineering production of valuable compounds in microbial cell factories. If scaled-up for precision fermentation in large bioreactors, these microbial systems will enable sustainable and cost effective production of important chemicals.
Description of Design Component: The students will be expanding on a previous project that built the basic steps and structure of the pipeline. They will:
1. Develop and implement a strategy to identify and extract pertinent pathway genes from transcriptomic data using methods like differential expression analysis, co-expression analysis, and functional annotation. They may also apply genomic strategies, such as searching for biosynthetic gene clusters.
2. Adapt the pipeline to run on cloud computing platforms like Compute Canada.
3. Streamline the code to ensure user-friendliness, working around technical challenges like sequence data management, package management, operating system compatibility, and storage allocation.
Skills to have or develop: For this project, a background in programming is required, preferably with knowledge in Python, R, and how to use the command line. An understanding of synthetic biology and basic molecular biology is also essential. Students will learn about the field of bioinformatics and the strategies used to analyse large amounts of DNA and RNA-seq data. They will gain experience working with various transcriptomic analysis tools, Python and R scripts, and the cloud computing platform Compute Canada.
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Project 21 Title: EMG-Driven Neuromusculoskeletal Modelling for Predicting Voluntary Torque at the Human Ankle
Supervisor: Prof. Robert E. Kearney, robert.kearney [at] mcgill.ca
Preferred Team size: Any
Background and Objectives: The estimation of voluntary muscle force, or its resulting joint torque, is essential for understanding human movement. This knowledge contributes to various fields, including biomechanics, sports science, rehabilitation medicine, and neurology. Voluntary contraction torque, the torque generated around a joint by voluntary muscle contraction, is a key indicator of the functional capacity and health of the musculoskeletal system. It also plays a critical role in neuromechanical modeling of human joints, such as dynamic joint stiffness, where it has been shown that voluntary torque modulates stiffness.
Direct measurement of muscle force and its resulting torque is invasive, requiring needle insertions, which can lead to tissue damage and discomfort. Additionally, the total torque measured by sensors includes multiple components鈥攑assive, voluntary, intrinsic, and reflex torques鈥攎aking it difficult to isolate voluntary torque from the total without affecting other torque components. Consequently, researchers have turned to non-invasive methods, with surface Electromyography (sEMG) emerging as a promising tool for estimating muscle force and voluntary torque. Modeling the relationship between the sEMG signals from muscles acting around a joint and the resulting voluntary torque has practical applications in powered prosthetics, active orthoses, and monitoring neuromuscular recovery after injury.
Three main approaches have been explored for EMG-Torque modeling: System Identification (SysId), Machine Learning (ML), and Neuromusculoskeletal (NMS) modeling. In REKLab, we have predominantly employed SysId for EMG-Torque modeling. This project aims to:
1. Develop EMG-driven NMS models to predict ankle torque across eight different ankle positions, using EMG data from the gastrocnemius medial (GM), gastrocnemius lateral (GL), soleus, and tibialis anterior (TA) muscles. The models should be capable of estimating joint torque in novel or previously unseen tasks (i.e., tasks or trials not used in model training or calibration).
2. Compare the performance of the EMG-driven NMS models with SysId methods developed at REKLab, focusing on prediction accuracy and reliability.
The development of EMG-driven NMS models has potential for optimizing rehabilitation exercises and improving outcomes in fields such as prosthetics, orthotics, and neuromuscular recovery in sports and neuromuscular patients. By advancing non-invasive methods to estimate voluntary torque, this project could contribute to the creation of more accessible and efficient healthcare solutions, ultimately benefiting patients with mobility impairments. Additionally, the tools and insights gained from this research could foster innovation in wearable medical devices, enhancing quality of life and promoting advancements in personalized healthcare.
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Description of Design Component: The student(s) will gain foundational knowledge in EMG-driven NMS modeling, utilizing software tools such as OpenSim and CEINMS for model development and analysis. CEINMS is an open-source model developed by Dr. Guillaume Durandau, Dept. of Mechanical Engineering, 不良研究所. The students will be provided with experimental data from REKLab and tasked with developing comprehensive data analysis pipelines. This will involve using models from OpenSim and CEINMS, integrating them with experimental data, and constructing a pipeline to build and calibrate the EMG-driven NMS models. Students will then execute these models using EMG data (as input) recorded from novel or previously unseen trials. The predicted torque will be compared against experimentally acquired torque as well as against torque predicted by SysId models, allowing for model performance evaluation.
The student(s) will document the methods and the results, present their work in progress meetings, and finally write a detailed report. If the results are significant, there may be potential for the work to be published.
Skills to have or develop: The student team should possess or be willing to develop the following skills: (1) Basic knowledge of biomechanics and NMS models. (2) Experience with or willingness to learn NMS modeling software tools such as OpenSim and CEINMS. (3) Proficiency in data processing and developing pipelines to analyze experimental data, as well as building and calibrating NMS models. (4) Programming skills in languages such as MATLAB or Python, which will be used to handle data analysis and model execution. (5) Ability to work with and handle experimental data files for muscle EMGs and ankle position and torque recordings. (6) Scientific writing and documentation skills including clear and effective documentation and presentation of methods and results, as well as the capacity to write comprehensive reports on project findings.
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Project 22 Title: Development of a porous microneedle patch for transdermal therapeutics
Supervisor: Prof. David Juncker david.juncker [at] mcgill.ca
M.Sc. student Justin de Vries justin.devries [at] mail.mcgill.ca
Preferred team size: Any
Background and objectives: Microneedles have attracted significant attention in recent years as both a transdermal sampling and delivery medium. Compared to conventional hypodermic needles, their microscale size (25-2500 碌m in height) allows for near pain-free skin insertion, as they do not penetrate deep enough to stimulate the nerve fibers in the dermal layer. Various types of microneedles exist, with solid, coated, hollow, and porous structures, each with specific applications. Porous microneedles possess an interconnected pore network that allows for diffusion of both exogenous and endogenous molecules into and from interstitial fluid. Hydrogels are an attractive material for porous microneedles, owing to their hydrophilicity, biocompatibility, and tunable mechanical properties. Their intrinsic porosity allows for high liquid loading by swelling several times their dry weight, providing an optimal microenvironment for the retention of biotherapeutic agents like cells and extracellular vesicles. However, hydrogel microneedles still require optimization for issues regarding their inherently soft and brittle natures which impede facile and consistent skin penetration, as well as issues regarding their drying and long-term storage. The Juncker Lab aims to develop a porous microneedle patch that can effectively penetrate human skin and deliver a therapeutic transdermally.
Description of design component: Students will participate in the material design of a microneedle patch, including but not limited to polymer networks, biological additives, and surface coatings. Various fabrication techniques may be explored, including 3D printing and replica moulding. Students will need to characterize the mechanical properties, loading capacity, and biodegradability of the microneedles. An in vitro skin model can then be developed using a target therapeutic (e.g. drug or small molecule) to assess the penetration ability and release rate characteristics of the designed patch.
Economic and societal impact: Pre-loaded and dried microneedle patches can advance the amenability of therapeutic treatments at the point-of-care. The patch porosity and degree of crosslinking allow for precise tuning of timed drug release, which can then be initiated simply by pressing the patch into the skin with minimal user intervention. The avoidance of trained healthcare personnel and specialized equipment can thus help to reduce the cost of treatment access.
Skills to have or develop: Material design, CAD, microfabrication, microscopy/imaging
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Project 23: Title: Development of an Integrated Radiofrequency Coil and Shim Array for Ultra-High Field MRI of the Brain
Supervisor: Prof David Rudko david.rudko [at] mcgill.ca
Preferred Team size: Any
Background and Objectives:
Ultra-High field (UHF) brain imaging offers significant potential to visualize the structure and function of the human brain at high resolution. Nonetheless, properly leveraging UHF MRI requires addressing several critical challenges. First, the decreased radiofrequency (RF) field wavelength associated with UHF MRI requires consistent mapping of the transmit RF field in the brain. Secondly, UHF MRI is associated with increased static magnetic field inhomogeneities. This project will focus on designing a human head coil for dynamic shimming of the human brain with concurrent RF signal reception. Such a hardware design has the potential to improve UHF imaging by mitigating some of the impact of local magnetic field inhomogeneities.
Description of Design Component: Under the direction of Dr. Rudko and a Postdoctoral Fellow in the lab, students will be involved in amplifier circuit design, catered EM simulations using finite element methods and radiofrequency receive coil array construction for a 7T human head coil. The coil will be tested at intermediate stages to evaluate main magnetic field shimming capability. The performance of the array for imaging the human brain will be quantified using accepted MRI performance measures.
Skills to have or develop: Experience with MRI coil design, circuit design, electromagnetic field modeling and brain imaging
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Project Title: Development of a Standardized Machine for Multi-Tissue Extracellular Matrix Decellularization
Supervisor: Prof. Matt Kinsella
Preferred Team Size: 3
Background and Objectives:
ECM decellularization is a process that removes cells from a tissue, leaving behind just the ECM scaffold. The scaffold can then be used for tissue regeneration and artificial organ development, among other applications. This process involves the use of physical, chemical, and enzymatic treatments to remove cells from tissue while preserving its ECM鈥檚 structure and biochemical properties. Current methods of decellularization require a manual multistep approach that is laborious, time-consuming, and often produces inconsistent results. Recent developments in decellularization technology, focused on automating this process, have been shown to drastically reduce human intervention and mitigate these issues. However, these designs still require manual ECM pretreatment and post-processing steps, resulting in processes that are not fully continuous. This project aims to develop an improved device for automating the decellularization process by improving on existing technologies and methodologies, with particular focus on eliminating the pre and post processing steps, and potentially allowing on-board analysis.
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Description of Design Components:
(1) Design and fabricate a bioreactor with parts that can be autoclaved.
(2) Optimize existing protocols and processes for decellularization for use in automated setting, with reagent circuits/sequence/programming, and possibly remove manual pre and post treatments (tissue preparation, hydrogel preparation).
(3) Develop a quantifiable analysis technique (preferably automated) to assess the quality of decellularized ECM, compared to manual method. Skills to have or develop: Bioreactor design and fabrication, Automation, Programming, Decellularization analysis (dsDNA concentration, proteomic content, histology)
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Please also see the MedTech sponsored projects