91ÉçÇø

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.

Academic Level: Year 3

Description
Bioprocess development involves upstream, and downstream processing; and analytical process technology optimization and integration. A significant amount of monitoring and control data are generated at all steps of the bioprocess. These data are derived from different types of equipment: bioreactors, purification units and analytical units. The aim of the project is to design data bases and streamline data acquisition and integration in historical display for supervision and control of advanced bioprocesses..

Tasks:
Develop good knowledge of bioprocess steps - Access communication protocols from equipment manufacturers - Design a network for streamlining process data acquisition - Develop drivers for communication data base/equipment units

Deliverables:
Data base integrating process operation - Drivers for equipment unit - User manual

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:
Report on the optimality of microorganisms behavior. In both cases one conference paper is expected at the end of the project.

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.

Description
A single bout of exercise, when performed immediately after motor skill practice, improves motor memory consolidation. This is thought to be associated with increase in corticospinal excitability (CSE) during memory consolidation, which was shown to strongly correlate to gains in motor memory induced by exercise. These results suggest a brain network involving M1 and underlying the effects of acute exercise on motor memory. This project aims to: 1) identify short-term changes in resting state network (RSN) connectivity and diffusional MRI assessed mean diffusivity (MD) after a single bout of cardiovascular exercise in brain areas involved in motor memory consolidation and 2) determine whether these changes are associated with positive effects of exercise on motor memory. It is hypothesized that: 1) an acute bout of cardiovascular exercise performed immediately after motor practice will increase connectivity and decrease MD in brain areas involved in motor memory consolidation and 2) changes in connectivity and MD within these areas will be associated with motor memory improvements. Subjects were randomly assigned to the exercise or control group. On visit 2, the subject underwent a baseline MRI session before practicing the motor task outside the scanner. Exercise group subjects then performed a 15 minute bout of cardiovascular exercise while control subjects rested for that time. A second MRI session was performed after the exercise or rest period. A retention test of the motor skill in both groups was measured 8 and 24 hours after the practice of the motor task. This is a joint project with Prof. MH Boudrias, School of Physical and Occupational Therapy..

Tasks:
The data has already been acquired, hence the student will not be involved in data collection. The student will be expected to complete a literature search on the effects of exercise on the brain and learn the state-of the art methods to analysis the data (FSL & SPM). His/her main focus will be to analyze the MRI data using these state-of-the-art methods, compare the results obtained between groups and interpret the results. The student should have basic skills in programming.

Deliverables:
Deliverable 1: Technical report on the results obtained.

Description
Development of a point of care diagnostic device that can sensitively detect different biomolecular targets is highly desirable for decreasing the delay between disease diagnosis and treatment thus increasing the survival rate. Such an ideal diagnostic device must display clinically relevant sensitivity and specificity, and must ideally be rapid, selective and quantitative enough to work in unprocessed body fluids, and able to diagnose multiple diseases simultaneously. As no such diagnostic method have achieved all of the above in one single device, this project aims at innovating novel lab-on-chip (LOC) technologies via combining nanostructured materials with an electrical/fluidic sample delivery system for rapid, quantitative and high throughput molecular detection at the point of care. Nano/microfluidic devices can provide means for sample concentration, on-chip sample isolation, sample preparation and multiplex diagnosis. On the other hand, nanostructured biosensors have shown high biochemical sensitivity and selectivity by significant enhancement of the detection sites. Therefore, it is anticipated that the interface of nanostructures with nano/microfluidic devices will lead to an ideal high throughput detection of multiple target molecules in one portable device..

Tasks:
1- Electric field and fluid flow profile in lab-chip-device using COMSOL simulation 2- 3D fabrication of the integrated nano/microfluidic channels 3- functionality of gold/carbon nanostructures in immunoassay modification

Deliverables:
1- numerical model studying two physics 2- CAD design and fabrication protocol 3- running regular experiments using wet lab protocols

Description
Proteins are evolved molecular machines capable of self-assembly and reliable functioning in fluctuating environments. Understanding the physical and evolutionary principles underlying these remarkable properties of proteins is a central challenge in biomolecular engineering. This project will focus on computer modeling of protein structure and evolution. Homology modeling will be used to construct three-dimensional structural models of various proteins. Next, structural, biophysical and evolutionary properties of these proteins will be investigated, with the aim to understand how biophysical properties of proteins affect their evolutionary properties at the residue level. The focus will be on soluble and membrane proteins..

Tasks:
Literature review; becoming familiar with existing publicly-available datasets on protein sequence and structure; becoming familiar with computational tools on modeling protein structure and evolution; computer programming.

Deliverables:
A final report summarizing the findings.

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 networks. Various publicly-available datasets on protein networks will be integrated and visualized. The resulting integrated protein networks will then be annotated with evolutionary and disease properties, with the aim to understand how protein networks evolve, and how disruptions in protein networks lead to disease. The focus will be on protein networks in yeast and human..

Tasks:
Literature review; becoming familiar with publicly-available datasets on protein networks; becoming familiar with existing computational tools on modeling protein networks; computer programming.

Deliverables:
A final report summarizing the findings.

Description
Develop novel nanocomposite materials for biosensing applications..

Tasks:
1. Prepare metallic nanoparticles 2. Prepare composite material with nanostructured biosilica 3. Perform experimental measurements of spectroscopic signals

Deliverables:
1. Colloidal plasmonic nanoparticles 2. Composite materials on flexible substrates