Programs > Brochure
IIP-University of Queensland, Institute of Molecular Bioscience (IMB)
Queensland, Australia (Outgoing Program)
|Partner Institution/Organization Homepage:||Click to visit|
|Restrictions:||Princeton applicants only|
|Dept Offering Program:||International Internship Program (IIP)||Program Type:||Internship|
|Language Prerequisite:||No||Program Features:||Lab Based Work, Research|
|Degree Level:||1st year u/g students, 2nd year u/g students, 3rd year u/g students||Time Away:||Summer|
|Housing options:||Student Responsibilty with support from IIP and/or Host Organization||Program Group:||International Internship Program|
About: The Institute for Molecular Bioscience's (IMB) mission is to decipher the information contained in the genes, proteins, and molecules of humans, animals, and plants. Since its establishment in 2000, IMB has earned a reputation as one of the Asia Pacific region's leading research institutes. By understanding the development process and aspects that go awry in complex diseases, IMB aims to develop pharmaceutical and cellular therapies, technologies, and diagnostics to prevent or treat such diseases.
- Intern Responsibilities: IIP interns will work on one or more of the following projects:
- Project 1: School of Chemistry and Chemical Engineering)
- As a feedstock, glycerol has great potential for use in several chemical conversion routes to generate high value products. The upgradation of glycerol from processes such as biodiesel production could potentially increase the economic viability of the industry. Photocatalysis presents an alternative method to current approaches which is environmentally benign and capable of operating under favorable ambient conditions. In this instance, photons of light are used to induce glycerol oxidation to products such as 2-dihydroxyacetone leading to the reduction of protons to form H2 over a semi-conductor. This project will investigate both the synthesis of photo-active materials via traditional wet impregnation routes and mechano-chemical processes (ball-milling) and the evaluation of these materials in a novel propeller fluidized photo reactor under UV irradiation from high power light emitting diodes.
- One intern responsibility will be to optimize the loading of metal co-catalysts (e.g. Pt, Pd, Ni) onto photo-active semiconductors such as TiO2 via different synthesis routes. The intern will then evaluate the above materials for glycerol conversion which will include the design and construction of a LED irradiation array that can be coupled to a photovoltaics panel to create a solar based unit.
- Project 2:
- Photocatalysis is a technology that has been extensively studied for over four decades. Photocatalytic technology shows excellent potential for water treatment and has extended its feasibility for water treatment application as a result of these important factors; ambient operating conditions, low operating costs and complete mineralization of parent and any intermediate compounds without secondary pollution. In the UK, one of the most frequent organic substances identified in drinking water and groundwater is the chlorophenoxy acid herbicide 4-chloro-2-methylphenoxyacetic acid (MCPA). Northern Ireland, in particular, has been faced with significant challenges in the removal of herbicides; a total of 153 events reported over a 3 year period where MCPA exceeded the 0.1µg/L limit (EC Drinking Water Directive maximum allowable concentration). This project will focus on the development and optimization of photocatalytic technology for the degradation of MCPA along with its reaction intermediates. A variety of different reaction conditions will be investigated including light sources with a view towards light emitting diodes and solar power, pH and addition of potential reaction promoters (H2O2).
- The intern will be responsible for working alongside PhD students in the design and construction of a large scale immobilized photocatalytic unit for the treatment of contaminated water from various sources around Northern Ireland.
- Project 3: Making sense of cancer data via deep learning (School of Electronics, Electrical Engineering and Computer Science)
- This project aims to use computers to make sense of a large set of medical data relating to types of cancer. A wealth of “big data” has been compiled in databases organized by NIH, and 20000+ genes and expression of these genes have been documented from patients with all types of cancer (Breast, Prostate, Pancreatic, Leukemia and 20-30 more). Each cancer subtype has data of at least 1000 patient samples, and the patient-matched gene expression data of the normal tissue that the tumor was extracted from is also documented. Using this information, which genes are expressed abnormally in the tumor sample of a patient may be discovered. As well as this, mutations, copy number, survival, etc. data is available for almost all the patients. Although much effort has been put into compiling this data, the researchers or scientists can search manually for only one gene at a time, which is highly inefficient. It is also impossible to explain the cause of cancer based on merely handpicking genes of interest. When it comes to the clinic, doctors diagnose cancer patients based on immunohistochemistry and instinct and then prescribe the same course of therapy for all patients depending on the grade of cancer: surgery, followed by radiation therapy and chemotherapy. The therapy inevitably fails in 99.99% of patients. Even if the doctor does consider the gene expression data of a cancer patient, it is impossible for the human mind to make sense of such a volume of existing data and attempt to match the new patient’s data to what is available. For this reason, all of the aforementioned data turns out to be useless. Ultimately, we want to provide a better and more complete method than this inefficient handpicking method by using modern machine learning such as deep learning.
- With appropriate guidance of lab members in Queen’s University, the intern could work on the following:
- Building an algorithm in which data is fed into an automatic learning process that could separate for each cancer subtype. Feeding the experimental dataset of a new cancer patient’s gene expression would provide us with optimal treatment options and more realistic survival predictions by matching that data with previously treated patients who had similar gene expression patterns.
- Generating mRNA data in a high-throughput fashion that would allow for gene expressions to be discovered and documented, and then;
- Modifying the biochemistry map showing interconnectivity of such genes using each patient’s data, in order to help scientists and researchers work more accurately and efficiently.
- Project 1: School of Chemistry and Chemical Engineering)
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