The heart is a complex three-dimensional structure with mechanical properties that are inhomogeneous, non-linear, time-variant and anisotropic. These properties affect major physiological factors within the heart, such as the pumping performance of the ventricles, the oxygen demand in the tissue and the distribution of coronary blood flow. During the cardiac cycle the heart muscle tissue is deformed as a consequence of the active contraction of the muscle fibers and their relaxation respectively. A mapping of this deformation would give increased understanding of the mechanical properties of the heart. The deformation induces strain and stress in the tissue which are both mechanical properties and can be described with a mathematical tensor object. The aim of this master's thesis is to develop a visualization tool for the strain tensor objects that can aid a user to see and/or understand various differences between different hearts and spatial and temporal differences within the same heart. Preferably should the tool be general enough for use with different types of data.

Today, scientists in various biomedical fields rely on biological data sources in their research. Large amounts of information concerning, for instance, genes, proteins and diseases are publicly available on the internet, and are used daily for acquiring knowledge. Typically, biological data is spread across multiple sources, which has led to heterogeneity and redundancy. The current thesis suggests grouping as one way of computationally managing biological data. A conceptual model for this purpose is presented, which takes properties specific for biological data into account. The model defines sub-tasks and key issues where multiple solutions are possible, and describes what approaches for these that have been used in earlier work. Further, an implementation of this model is described, as well as test cases which show that the model is indeed useful. Since the use of ontologies is relatively new in the management of biological data, the main focus of the thesis is on how semantic similarity of ontological annotations can be used for grouping. The results of the test cases show for example that the implementation of the model, using Gene Ontology, is capable of producing groups of data entries with similar molecular functions.

This master thesis report describes signal processing parameters of electroencephalography (EEG) signals with a significant difference between the signals from the animal model of clinical depression and the non-depressed animal model. The signal from the depressed model had a weaker power in gamma (30 - 80 Hz) than the non-depressed model during awake and it had a stronger power in delta (1.5 - 4 Hz) during sleep. The report describes the process of using visualisation to understand the shape of the signal which helps with interpreting results and helps with the development of parameters. A generic tool for time-frequency analysis was improved to cope with the size of the weeklong EEG dataset. A method for evaluating the quality of how well the EEG parameters are able to separate the strains with as short recordings as possible was developed. This project shows that it is possible to separate an animal model of depression from an animal model of non-depression based on its EEG and that EEG-classifiers may work as indicative classifiers for depression. Not a lot of data is needed. Further studies are needed to verify that the results are not overly sensitive to recording setup and to study to what extent the results are translational. It might be some of the EEG parameters with significant differences described here are limited to describe the difference between the two strains FSL and SD. But the classifiers have reasonable biological explanations that makes them good candidates for being translational EEG-based classifiers for clinical depression.

The field of systems biology has become very popular as a means to deal with cancer as well as other complex biological issues. It enables scientists to gain an insight into difficult conditions through mathematical approaches that have been developed. Prostate cancer is the second leading cause of death among men after skin cancer and its heterogeneity makes it a complex disease. In this study we focus on three pathways known to play crucial roles in the formation of prostate cancer. By using a mathematical model that combines all of them we describe the interactions taking place during signal transduction in the prostate under normal and cancer conditions.

Networks are ubiquitous with several levels of complexity, configuration, hierarchy and function. Many micro- and macro-scale biological or non-biological interactions define complex systems. Our most sophisticated organ, the brain, accommodates the interaction of its billions of neurons through trillions of synapses and is a good example of a complex system. Network structure has been shown to be the key to determine network functions. For instance, communities or modules in the network explain functional segregation and modular interactions reveal functional integration. Moreover, the dynamics of cortical networks have been experimentally shown to be linked to the behavioral states of the animal. The level of rate and synchrony have been demonstrated to be related to sleep (inactive) and awake (active) states of animals. The structure of brain networks is not static. New synapses are formed and some existing synapses or neurons die due to neurodegenerative disease, environmental influences, development and learning, etc. Although there are many studies on the function of brain networks, the changes by neuronal and synaptic degeneration have not been so far in focus. In fact, there is no known mathematical model on the progressive pattern of synaptic pruning and neurodegeneration. The goal of this dissertation is to develop various models of progressive network degeneration and analyze their impact on structural and functional features of the networks. In order to expand the often chosen approach of the "random networks", the "small world" and "scale-free" network topologies are considered which have recently been proposed as alternatives. The effect of four progressive synaptic pruning strategies on the size of critical sites of brain networks and other complex networks is analyzed. Different measures are used to estimate the levels of population rate, regularity, synchrony and pair-wise correlation of neuronal networks. Our analysis reveals that the network degree, instead of network topology, highly affects the mean population activity. / <p>QC 20170906</p>

In this project, two different machine learning models were tested in an attempt at imputing missing genotype data from patients on two different panels. As the integrity of the patients had to be protected, initial training was done on data simulated from the 1000 Genomes Project. The first model consisted of two convolutional variational autoencoders and the latent representations of the networks were shuffled to force the networks to find the same patterns in the two datasets. This model was unfortunately unsuccessful at imputing the missing data. The second model was based on a UNet structure and was more successful at the task of imputation. This model had one encoder for each dataset, making each encoder specialized at finding patterns in its own data. Further improvements are required in order for the model to be fully capable at imputing the missing data.

With the increasing amounts of data generated in life science, methods for data storage and sharing are being developed and implemented. Online data repositories are more and more commonly used for data sharing. The national Swedish platform Science of Life Laboratory has decided to use an institutional data repository as a mean to address the increasing amounts of data generated at the platform. In this project, the system used for the institutional repository at SciLifeLab was studied and compared to implementations of the same system at other institutions to create user documentation for the repository. This documentation was created with the FAIR principles as a guidance. Feedback on the guidelines were then sought from users and based on the received feedback, the user documentation was improved. Using a FAIR evaluation tool called FAIR evaluation services, items published on the repository were evaluated. Investigation of these results and their correlation to the items record on the repository were carried out. Out of ten evaluated datasets all except one scored exactly the same on the FAIR evaluation services tests. This could indicate that the test used is not evaluating aspects needed to encounter the differences in these published items. Based on this, conclusions as to in what extent user documentation can increase the FAIRness of data cannot be drawn.

Many people - especially during their elderly - consume multiple drugs for the treatment of complex or co-existing diseases. Identifying side effects caused by polypharmacy is crucial for reducing mortality and morbidity of the patients which will lead to improvement in their quality of life. Since there is immense space for possible drug combinations, it is infeasible to examine them entirely in the lab. In silico models can offer a convenient solution, however, due to the lack of a sufficient amount of homogenous data it is difficult to develop both reliable and scalable models in its ability to accurately predict Polypharmacy Side Effect. Recent advancement in the field of representational learning has utilized the power of graph networks to harmonize information from the heterogeneous biological databases and interactomes. This thesis takes advantage of those techniques and incorporates them with the state-of-the-art Graph Neural Network algorithms to implement a Deep learning pipeline capable of predicting the Adverse Drug Reaction of any given paired drug combinations.

The Selection during Niche AdaPtation (SNAP) hypothesis aims to explain how the gene order in bacterial chromosomes can change as the result of bacteria adapting to a new environment. It starts with a duplication of a chromosomal segment that includes some genes providing a fitness advantage. The duplication of these genes is preserved by positive selection. However, the rest of the duplicated segment accumulates mutations, including deletions. This results in a rearranged gene order. In this work, we develop a method to identify SNAP in bacterial chromosomes. The method was tested in Salmonella and Bartonella genomes. First, each gene was assigned an orthologous group (OG). For each genus, single-copy panorthologs (SCPos), the OGs that were present in most of the genomes as one copy, were targeted. If these SCPos were present twice or more in a genome, they were used to build duplicated regions within said genome. The resulting regions were visualized and their possible compatibility with the SNAP hypothesis was discussed. Even though the method proved to be effective on Bartonella genomes, it was less efficient on Salmonella. In addition, no strong evidence of SNAP was detected in Salmonella genomes.

Between closely related species there are genomic regions with a higher level of differentiation compared to the rest of the genome. For a time it was believed that these regions harbored loci important for speciation but it has now been shown that these patterns can arise from other mechanisms, like recombination. The aim of this project was to estimate the recombination landscape for red-breasted flycatcher (Ficedula parva) and taiga flycatcher (F. albicilla) using patterns of linkage disequilibrium. For the analysis, 15 red-breasted and 65 taiga individuals were used. Scaffolds on autosomes were phased using fastPHASE and the population recombination rate was estimated using LDhelmet. To investigate the accuracy of the phasing, two re-phasings were done for one scaffold. The correlation between the rephases were weak on the fine-scale, and strong between means in 200 kb windows. 2,176 recombination hotspots were detected in red-breasted flycatcher and 2,187 in taiga flycatcher. Of those 175 hotspots were shared, more than what was expected by chance if the species were completely independent (31 hotspots). Both species showed a small increase in the rate at hotspots unique to the other species. The low number of shared hotspots might indicate that the recombination landscape is less conserved between red-breasted and taiga flycatchers than found between collared and pied flycatcher. However, the investigation of the phasing step indicate that the fine-scale estimation, on which hotspots are found, might not be reliable. For future analysis, it is important to use high-quality data and carefully chose methods.