Statistical analysis of saturating transposon mutagenesis in fission yeast

Fission yeast or S. pombe is a popular model organism, allowing the investigation of many cellular processes of eukaryotic cells. A transposon screen has been conducted with the yeast genome, where transposons (2k basepairs) are inserted artificially at random into the yeast genome by transposase hermes. An insertion generally results in the disruption of a gene and hence it’s knockout. Saturation transposon mutagenesis describes the saturation or transposon insertion in the yeast genome and accompanied growth of cells. Cells with an insertion in an essential region for growth and fitness are most likely to die, resulting in transposon depleted regions. Therefore previously non annotated regions can be discovered if such regions fall within less well a priory investigated regions. Due to the arbitrary length of depleted regions, under sampling and insertion biases a Hidden Markov Model is employed to classify regions as essential and non essential.

Report

• Date: September 2016

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microRNA expression profiles in human brain autopsy tissues

MicroRNAs were recently discovered to be important in regulating gene expression, adding an additional control layer. MicroRNAs are well expressed in the brain, suggesting a link between the brain complexity and usage of additional control of protein regulation. Their length of 19 to 22 nucleotides is su cient to target specific mRNAs for degradation. Importantly miRNAs often target multiple mRNAs and each mRNA is in turn regulated by multiple microRNAs, making the system quite complex and requiring mathematical modelling. To analyse the expression levels in di↵erent brain regions several mathematical overlap functions were implemented into an R package, called divo.R, allowing the user to group co-expressed microRNAs that may be functionally related to one another. Divo.R had been implemented in R and was then transfered to Python with a R wrapper for improved speed. Still unable large, next generation sequencing, data sets, a third version is implemented here in C with a R wrapper, promising a significant decrease in computing time.

Report

• Date: May 2016

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4D/2D MRI flow and CFD comparison in a coarctation patient.

Congenital coarctation of the Aorta (CoA) is an abnormal narrowing of the aorta from birth. It is usually associated with high blood pressure and the risk of developing further vascular diseases. Patients suffering from CoA have reduced live expectancy, highly depending on the degree of coarctation. An intervention is usually performed when a pressure drop or gradient in excess of 20mmHg exists along the coarctation. The pressure drop is measured with an expensive, invasive catheterisation method. An alternative to measuring the pressure drop with a catheter is to use computational fluid dynamics (CFD) to compute the flow and pressure inside of an aorta. This technique is relatively new and first has to be validated against measured data before being put into practice. The report aims at obtaining such a validation, by comparing measured data prior to the intervention and after the intervention with simulated data in ANSYS fluent, a CFD solver. Setting up of an ANSYS fluent simulation requires several steps which are outlined in this report and follows the following rough guideline. Segmentation extracts the 3D geometry of the aorta as a surface. The surface is loaded into fluent after further processing. Flow information from MRI is extracted to be used as conditions in the simulation. The CFD solver is set up and the results are analysed and compared with the measured values. CFD simulations have the great potential to accurately predict the flow at every point within the aorta, with future applications in stent placement optimisation and aiding in choosing the appropriate type of stent.

Report

• Date: March 2016

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Photoacoustic sensing of lipids

Photoacoustic imaging uses an amplitude modulated signal to drive a laser in the near infrared range. Due to the absorption of light in the medium, the sample is heated. The alteration in heating causes the tissue to contract and expand and different rates, building up thermal stress. The thermal stress results in a pressure wave that can be detected with a transducer. The aim of this report is find such a signal from biological tissues particularly fat and a fibre coating material.

Report

• Date: January 2016

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A model of thermotherapy treatment for bladder cancer

Abstract. In this work, we investigate chemo- thermotherapy, a recently clinically-approved post-surgery treatment of non muscle invasive urothelial bladder carcinoma. We developed a mathematical model and numerically sim- ulated the physical processes related to this treatment. The model is based on the conductive Maxwell’s equations used to simulate the therapy admin- istration and Convection-Diffusion equation for incompressible fluid to study heat propagation through the bladder tissue. The model parameters corre- spond to the data provided by the thermotherapy device manufacturer. We base our computational domain on a CT image of a human bladder. Our nu- merical simulations can be applied to further research on the effects of chemo- thermotherapy on bladder and surrounding tissues and for treatment person- alization in order to maximize the effect of the therapy while avoiding burning of the bladder.

Paper

• Date: December 2016

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Modeling of Alzheimer's Disease (AD) inducing Protein Amyloid-beta (Aβ) in 3D

Alzheimer's disease is a form of dementia affecting mainly the elderly. Symptoms are loss of short term memory in the early stages up to shut down of basic bodily functions leading to death in the final stages.There is currently no treatment for the prevention or delay of AD,with medication manly focusing on suppressing AD symptoms.The disease is caused by the protein fragment Amyloid-beta,clustering together with other fragments inside of the brain and forming a Plaque at Neurons,leading to their cell death! The main structure of Amyloid-beta is known and can be further investigated using Molecular dynamics (MD) simulations. MD uses classical mechanics to numerically analise protein structures , which allows one to investigate structural changes to the protein.

Poster

• Date: July 2014

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