Bioengineering

SURE: Bioengineering

Bioengineering

 

BIO-001: Integrated Nano/Bio Platform for Sensitive and High Throughput Detection of Biological Analytes

Professor Sara Mahshid

sara.mahshid [at] mcgill.ca
514-398-8964
http://mahshidlab.com/

Research Area

nanomaterials, nano/microfluidic devices, biosensors

Description

The project aims to develop portable biological sensor based on nanostructured nanoscale electrodes, which combines 3D nanoelectrodes with photo/electroactive 2D materials to enhance sensitivity, rapidity and quantitative detection of biological analytes using optical and electrochemical detection method for applications in health care and point-of-care diagnostics. This research is proposed based on the synergy of multifunctional nanomaterials embedded in nanoscale cavities with the relative size of the nano-sized biological analytes and integration of the opto-electrochemical detection platform with implemented fluidic sample delivery systems. The ordered nanoscale electrodes can provide adjustable light diffraction and plasmonic features to enhance optical detection of biological analytes while increasing sensitivity in catalytic electrochemical detection. The 3D hierarchical nanostructures enable adjustable spatial configuration and surface roughness while 2D integration of materials allows for modifiable surface chemistry and physical properties. The first objective of this research is to develop nanostructured nanoelectrodes by applying lithography technique to pattern nanoscale cavities and then apply electrochemical techniques to grow 3D nanomaterials (including gold (Au) and 2D materials (graphene) to modify their photoelectronic properties. The second objective is to implement and optimize the detection platform (which is integrated with a sample delivery system), for practical biosensing applications. Performance of the proposed platform would be measured in quantitative detection of pathogenic bacteria. It is expected that the proposed detection platform will be able to overcome the current limitations of point-of-care sensing platforms and successfully enhance the sensitivity, selectivity, rapidity and durability of the sensor.

Tasks

  • Literature review on the subject
  • Using microfabrication facility in the Department of Bioenigneering to develop the sensor platform.
  • Using the electrochemical workstation to fabricate the nanostructured sensor
  • Preparing solutions and reagents for the experiments.
  • Working with fluorescent microscopy and electroanalytical tools to measure the sensor parameters.

 

Deliverables

regular attendance in weekly group meetings and reports on the projects. submit biweekly progress report. draft a complete report including the literature review and the experimental results at the end of the project.

Number of positions

2

Academic Level

Year 3

BIO-002: Computational structural and systems biology: Design principles of protein structures and networks

Professor Yu Xia

brandon.xia [at] mcgill.ca
514-398-5026
http://bioinfo.lab.mcgill.ca

Research Area

Bioinformatics, Computational Biology

Description

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.

Tasks

  • 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

 

Deliverables

A final report summarizing the findings.

Number of positions

1

Academic Level

Year 3

BIO-003: Bionanocomposite plasmonic materials for biosensing

Professor Sebastian Wachsman-Hogiu

sebastian.wachsmannhogiu [at] mcgill.ca
4383502897

Research Area

Biosensors

Description

This project will explore mixtures of platinum nanoparticles with biological materials with the goal of using these materials for biosensing via optical and electrochemical methods.

Tasks

Perform literature review Synthesize nanoparticles Characterize nanoparticles Collect and analyze data

Deliverables

Literature review on platinum-based canocomposites Composite nanomaterials from platinum and biological materials

Number of positions

1

Academic Level

No preference

BIO-004: Nuclear mechanics and stem cell differentiation

Professor Allen Ehrlicher

allen.ehrlicher [at] mcgill.ca
514-714-8239

Research Area

Cell Mechanics

Description

This project will examine the role of nuclear deformation in the differentiation of stem cells, as a means to understand the role of contractility and substrate stiffness in stem cell mechanotransduction. We will explore various lamin constructs in conjunction with mechanical substrates and confined geometries, and the work will be performed in conjunction with two PhD candidates.

Tasks

Transfection; Traction Force Microscopy experiment preparation, performance and analysis; Confocal and fluorescence microscopy; micro-patterned substrate preparation; cell marker staining and analysis.

Deliverables

Daily/weekly deliverable will be based on research tasks. End of session deliverables include quantitative measurements of nuclear deformation as functions of lamin concentration and distribution, substrate stiffness, and contractile work.

Number of positions

2

Academic Level

Year 3

BIO-005: Investigation of dynamic functional connectivity using simulated and experimental multimodal neuroimaging data

Professor Georgios Mitsis

georgios.mitsis [at] mcgill.ca
5143984344

Research Area

Signal processing, functional neuroimaging

Description

The exceptional capacity of the brain to process complex stimuli arises largely from the presence of intricate interactions between different regions. Therefore, understanding connectivity holds one of the major keys for understanding brain function in health and disease. More recently, there has been much interest in dynamic functional connectivity, i.e. how connectivity patterns vary over time. In this context, the main objective of the present project is to use advanced signal and image processing to better understand the nature of dynamic functional connectivity using both simulated and experimental data. Specifically, we will use realistic simulated data generated from computational models as well as collect/analyze multimodal neuroimaging data (simultaneous EEG-fMRI, Doppler ultrasound and fNIRS) during resting-state conditions and motor task execution. We will specifically investigate the possible source of time-varying functional connectivity patterns, such as physiological factors (i.e. fluctuations in physiological signals such as heart rate, respiration and arterial CO2) and electrophysiological signatures, such as power in different frequency band of the EEG signal. To achieve this we will use nonstationary signal processing methods such as wavelets and time-varying multivariate autoregressive models that our lab has developed. Multimodal imaging methods are very promising to better elucidate the exploiting the excellent time resolution of EEG, MEG and the excellent spatial resolution of fMRI. Further validation of the motor connectivity measures to be identified will yield a set of robust and sensitive biomarkers of age-related motor decline, which may ultimately guide personalized treatment strategies using exercise or stimulation protocols (e.g. transcranial direct current stimulation).

Tasks

The student will help in the collection of experimental data at McGill’s Brain Imaging Center (BIC), as well as analyze the simulated/experimental data, using advanced methods. The aim will be to better understand how signals recorded with different modalities may be used to quantify dynamic connectivity patterns, e.g. how does EEG signal power in different frequency bands affect the slow fluctuations observed with the fMRI BOLD signal?

Deliverables

Deliverable 1: Processing pipeline for analyzing the multimodal experimental data. Deliverable 2: Technical report.

Number of positions

2

Academic Level

Year 3

BIO-006: Mathematical modeling of tumor growth and therapy effects in 3D cell culture and transgenic mouse cancer models.

Professor Georgios Mitsis

georgios.mitsis [at] mcgill.ca
5143984344

Research Area

Mathematical modeling, computational oncology

Description

Despite the unquestionable progress in basic cancer research, which has included the use of computational approaches, the widespread application of the latter to cancer therapy design in a clinical setting is still elusive. However, quantitative approaches can significantly contribute towards better understanding the underlying biological mechanisms as well as the long-held goal of designing patient-specific therapeutic strategies. Specifically, constructing mathematical models that can reliably predict tumor growth and its response to therapy according to a patient’s individual characteristics can be used to achieve the latter goal. In this context, the present project aims at building temporal/spatiotemporal mathematical models describing tumor growth and the effects of therapy and validating these models using state-of-the-art 3D bioprinting (cell culture) methods and animal model data. Specifically, we will use data generated from 3D bioprinting techniques developed in the research lab of Prof. M. Kinsella (Bioengineering), which are able produce in vitro models of tumor tissue capable of mimicking the mechanical, pathophysiological, and cellular heterogeneous state found in native tumors. We will also use data collected from transgenic mice of HPV+ skin cancer collected at the lab of Prof. K. Strati (University of Cyprus). The parameters of the developed models will be fine-tuned using the experimental data and the effect of varying several experimental parameters (e.g. initial position and concentrations of tumor cells and fibroblasts in the cultures) will be quantitatively assessed.

Tasks

The student will conduct a literature survey related to models describing tumor growth and therapy effects. Based on that, he/she will select the model structure that is most suitable for the available experimental data (3D cell cultures, transgenic mice). The selected model structures will be subsequently fitted to the data in order to provide better quantitative understanding of the underlying mechanisms and assess the effect of different experimental design parameters.

Deliverables

Deliverable 1: Matlab toolbox implementing computational models for tumor growth and therapy effects. Deliverable 2: Technical report.

Number of positions

1

Academic Level

Year 3

BIO-007: Bioengineering Better Carbon Fixation and Sequestration

Professor Allen Ehrlicher

allen.ehrlicher [at] mcgill.ca
514-714-8239

Research Area

Cell mechanics

Description

Virtually all life on Earth stems from the ability of photosynthetic organisms to use atmospheric carbon dioxide in producing usable biological materials in a process known as biosequestration. The complex reactions within the Calvin Cycle serve as variables by which biosequestration can be optimized for large-scale industrial carbon fixation. Research into the ideal conditions for each Calvin Cycle reaction, as well as the potential of modifying the enzymes and substrates involved, may lead to the design of a higher-rate carbon fixation system. This project endeavours to apply directed evolution approaches to iteratively select for fitness of microorganisms in biosequestration. Applications include applied bioengineering on an industrial scale to mitigate atmospheric carbon-dioxide levels.

Tasks

This is an ambitious project beyond the scope of casual summer research oriented around discovery of the feasibility of this approach, and exceptionally independent and self-motivated researchers will be considered. Research tasks include literature review and application of existing techniques, proactive collaborative discussion with local and regional experts for development. In the lab, the researcher will conduct the various biochemical reactions involved in biosequestration, manipulating existing biological molecules to work in different capacities, and manipulating enzymatic pathways to optimize reaction rate. Prior expertise in photosynthetic biochemistry, lab experience, and bioengineering are all highly valued.collaborative discussion with local and regional experts for development. In the lab, the researcher will conduct the various biochemical reactions involved in biosequestration, manipulating existing biological molecules to work in different capacities, and manipulating enzymatic pathways to optimize reaction rate. Prior expertise in photosynthetic biochemistry, lab experience, and bioengineering are all highly valued.

Deliverables

Demonstration of the feasibility of this approach by the selective improvement of biosequestration of microorganisms, likely through targeted directed evolution of the RuBisCO enzyme and related pathways.

Number of positions

2

Academic Level

Year 2

BIO-008: Regulation of motor proteins in intracellular transport and cell division

Professor Adam Hendricks

adam.hendricks [at] mcgill.ca
514.893.2343
https://hendricks.lab.mcgill.ca

Research Area

Bioengineering

Description

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 cell division, and how motor proteins are misregulated in neurodegenerative disease and cancer.

Tasks

Student 1: Use super-resolution fluorescence imaging to examine the number and organization of motor proteins on intracellular cargoes. Student 2: Develop optical trapping techniques to measure the forces exerted by motor proteins in living cells.

Deliverables

Student 1: Quantify the number and types of motor proteins associated with sorting, early, and late endosomes. Student 2: Measure the forces exerted by motor proteins as they transport cargoes in living cells.

Number of positions

2

Academic Level

Year 3

BIO-009: Biocomputation with ‘Smart’ Biological Agents

Professor Dan Nicolau

dan.nicolau [at] mcgill.ca
5147188261
http://www.pnas.org/content/113/10/2591.full.pdf

Research Area

Computational biomimetics

Description

Many mathematical and real-life problems cannot, or are very difficult to be solved by the present computers which process the information sequentially and with extreme precision. Among these problems one can mention travel and production scheduling, class time tables, and cryptography. Despite this difficulty, these problems are solved easily by individual biological agents, from microorganisms to humans, who do not process the information sequentially, but in parallel, and who trade precision for heuristic decision making. Alternatively, some mathematical and real-life problems that cannot be solved by the present computers are also difficult to solve by individuals, due to the limited capacity of an individual to process the information in parallel, but can be solved heuristically by groups of individuals operating together either explicitly or tacitly. Among these problems one can mention behaviour of groups in panic situations, solving complex traffic problems, hierarchical self-organisation of groups in conflictual situations. To this end, the project aims to assess the individual and collective ‘computational power’ of individual biological agents in optimally partitioning the available space and taking optimal decisions. The possible applications range from medical to new algorithms and computer paradigms. The project involves either experiments, such as observing the ‘intelligent’ behaviour of microorganisms facing space confinement via their movement in microfabricated networks; or the modelling and simulation of their behaviour; or a combination of both. The ‘smart’ biological agent of choice is a fungus, which has been demonstrated as using intelligent algorithms for searching labyrinths.

Tasks

The project can be approached, depending on the student’s strengths, either from an experimental, or a simulation perspective. Experimental tasks comprise the fabrication of simple microfluidics structures; growth of microorganisms in microfluidics structures; observation and recording of microorganisms behavior. Simulation tasks comprise the translation of microorganisms behavior in logic rules and simple algorithms; and the simulation of microorganisms behavior in complex structures.

Deliverables

Update the existing database of microorganisms behavior in confined spaces; or alternatively prepare a report on the optimality of microorganisms behavior. In both cases one conference paper is expected at the end of the project.

Number of positions

2

Academic Level

No preference

BIO-010: Information Storage on Molecular Surfaces of Biomolecules

Professor Dan Nicolau

dan.nicolau [at] mcgill.ca
5147188261
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0114042

Research Area

Biological information

Description

The spatial recognition of objects, from airplanes to human faces, is of ever-increasing interest in the present interconnected and crowded world. While this problem is tackled by humans by a myriad of image analysis and recognition algorithms implemented in dedicated software, a similar problem is seamlessly solved in Nature by the ‘image recognition’ between biomolecules – the cornerstone of all biological processes. However, and despite their theoretical similarity, presently only separate, specialised programs are used for image recognition for the macro-world, e.g., biometrics, and nano-world, e.g., drug discovery. The project will involve the use of existing in-house developed software for building images of biomolecules, followed by the development of an interface between structural databases, image building for the bio-objects present in these databases, and the archiving, classification and access to a database of molecular images.

Tasks

Upgrade of the existing simulation procedure for molecular surfaces; running simulation for a small set of proteins; search for commonality of characteristics between the mapped molecular surfaces.

Deliverables

Update the existing Biomolecular Adsorption Database (BAD); report regarding the property distribution on molecular surface. One conference paper is expected at the end of the project.

Number of positions

1

Academic Level

Year 3