Gyros Protein Technologies AB produce instruments designed to perform automated immunoassaying on plastic CDs with microstructures. While generally being a very robust process, the company had noticed that some runs on the instruments encountered problems. They hypothesised it had to do with the chamber on the CD in which the sample is added to. It was believed that the chamber was not being filled properly, leaving it completely empty or contained with a small amount of air, rather than liquid. This project aimed to investigate this hypothesis and to develop an image analysis solution that could reliably detect these occurrences. An image analysis script was developed which mainly utilised template matching and canny edge detection to assess the presence of air. The analysis had great success in detecting empty chambers and large bubbles of air, while it had some trouble with discerning small bubbles from dirt on top of the CD. Evaluating the analysis on a test set of 1305 images annotated by two people, the analysis managed to score an accuracy of 96.8 % and 99.5 % respectively.

Indiana University-Purdue University Indianapolis (IUPUI) / Malaria infects more than 240 million people every year, causing more than 640,000 deaths in 2021 alone. The complex interactions between the Plasmodium parasites that cause malaria and host immune system have made it difficult to identify specific mechanisms of vaccine-induced and naturally acquired immunity. After more than half a century of research into potential immunization methods, reliable immune correlates of malaria protection still have yet to be identified, and questions underlying the reduced protective efficacy of malaria vaccines in field studies of endemic populations relative to non-endemic populations still remain. In this thesis, I use computational methods to identify biological determinants of whole-parasite vaccine-induced immunity and immune correlates of protection from clinical malaria. Our systems analysis of a PfSPZ Vaccine clinical trial revealed that innate signatures were predictive of increased antibody response but also a decrease in the cytotoxic response required for sterilizing immunity. Conversely, these myeloid signatures predicted protection against parasitemia for subjects receiving a saline placebo, suggesting a role for myeloid-lineage cells in clearing pre-erythrocytic parasite stages. Based on these findings, I created a structural equation model to examine the interactions between cellular, humoral, and transcriptomic responses and the effects these have on protection outcome. This revealed a direct positive effect of CD11+ monocyte-derived cells on parasitemia outcome post-vaccination that was mediated by the presence of P. falciparum-specific antibodies at pre-vaccination baseline. Additionally, this model illustrates an indirect role of CD14+ monocyte activation in restricting immune priming by the PfSPZ Vaccine. Together, this data supports our hypothesis that innate immune activation and antigen presentation are uncoupled from cytotoxic cell-dependent immunity from the PfSPZ Vaccine and that this effect may be antibody-dependent.

Here we present a similarity-based pairing method for generating compound pairs to train a Siamese Neural Network. In comparison with the conventional exhaustive pairing of N2/2 pairs (N being the sizeof the training set), this method results in N-1 pairs, significantly reducing the training time. It exhibits a better prediction performance consistently on the three physicochemical property datasets, using a multilayer perceptron with the ECFP4 fingerprint. We further include into the Siamese Neural Network the pre-trained Chemformer which extracts task-specific chemical features from the input SMILES strings. With the n-shot learning, we propose a means to measure the prediction uncertainty. Our results demonstrate that the higher accuracy is indeed associated with the lower prediction uncertainty. In addition, we discuss implications of the similarity principle in machine learning.

The study explores the integration of mathematical modelling and machine learning to understand tumour-macrophage interactions in the tumour microenvironment. It details mathematical models based on biochemistry and physics for predicting tumour dynamics, highlighting the role of macrophages. Machine learning, particularly unsupervised and supervised techniques like K-means clustering, logistic regression, and support vector machines, are implemented to analyse simulation data. The thesis's integration of K-means clustering reveals distinct tumour behaviour patterns through the classification of tumour cells based on their microenvironmental interactions. This segmentation is crucial for understanding tumour heterogeneity and its implications for treatment. Additionally, the application of logistic regression provides insights into the probability of macrophage polarization states in the tumour microenvironment. This statistical model underscores the significant factors influencing macrophage behaviour and their consequent impact on tumour progression. These analytical approaches enhance the understanding of the complex dynamics within the tumour microenvironment, contributing to more effective tumour study strategies. The study presents a comprehensive analysis of tumour growth, macrophage polarization, and their impact on cancer treatment and prognosis. Ethical considerations and future directions focus on enhancing model accuracy and integrating experimental data for improved cancer diagnosis and treatment strategies. The thesis concludes with the potential of this hybrid approach in advancing cancer biology and therapeutic approaches. / <p>Det finns övrigt digitalt material (t.ex. film-, bild- eller ljudfiler) eller modeller/artefakter tillhörande examensarbetet som ska skickas till arkivet.</p><p>There are other digital material (eg film, image or audio files) or models/artifacts that belongs to the thesis and need to be archived.</p>

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Large scale genomic sequencing efforts have resulted in a massive inflow of raw sequence data. This raw data, when appropriately processed and analyzed, can provide insight to a trained biologist and aid in hypothesis-driven research. Given the time and resource requirements necessary for biological experiments, computational predictions of gene functions can aid in reducing a large list of candidate genes to a few promising targets. Various computational solutions have been proposed and developed for gene function prediction. These solutions utilize various forms of data, such as DNA/RNA/protein sequences, protein structures, interaction networks, literature mining, and a combination of these data sources. However, these methods do not always produce precise results as the underlying data sets used for training or modeling are quite sparse. We developed and used a massive sequence similarity network build over 108 million known protein sequences to aid in protein function prediction. Predictions are made through the alignment of query sequences to representative sequences for a given cluster derived from the massive sequence similarity network. Derived clusters aggregate information (particularly that from the Gene Ontology) from respective members, which we then consolidate through a novel weighted path method. We evaluate our method on four holdout datasets using CAFA evaluation metrics. Our results suggest that clustering significantly reduces the time and memory requirements, with a marginal impact on predictive power. At lower sequence similarity thresholds, our method outperforms other gold standard methods. / Master of Science / We often think of a protein as a nutritional requirement. However, proteins are far more than just food, they play countless and unappreciated roles in facilitating life. From transporting nutrients in the body, synthesis of hormones, functioning as enzymes to expediting chemical reactions, serving as the scaffold for cells and tissues, to protecting the body against foreign pathogens. On a molecular level, each protein is made up of chains of 20 different amino acids, just like a chain of beads, that are then folded to create a 3-dimensional structure. The variations in the ordering of amino acids result in different types of proteins. There are millions of genes across known life, and they perform different functions when translated into proteins. Nature has given us many proteins with interesting properties, and the low cost of sequencing their precursors (DNA) has resulted in large amounts of sequence data that is not yet associated with a function. Biological experiments to determine the function of a protein can be time consuming and expensive. We built a massive network encompassing 108 million protein sequences based on sequence similarity. This ensures that we make use of as much data as possible to make better predictions. Specifically, our work focuses on utilizing this information of similar proteins to aid in predicting the functions of a protein given its sequences. It is based on the idea of guilt by association, such that if two proteins are similar in sequences, they perform similar functions. We show that using computationally efficient methods and large datasets, one can achieve fast and highly precise predictions.

Machine learning has gained much attention and extended to the field of drug discovery. However, due to the uncertainties of the dataset, predictions should be quantitatively analyzed. Conformal prediction is a powerful method for quantifying these uncertainties, generating a predefined confidence level and a corresponding interval within which the true target is anticipated to fall. This paper aims to explore the effects of different chemical representations of SMILES structures for training (chemical descriptors, Morgan fingerprints), machine learning algorithms (k-nearest neighbor, support vector machine, random forest, extreme gradient boosting, and artificial neural network), and different normalization methods (k-nearest neighbor, Mondrian regression) in influencing the conformal prediction results. We find that Morgan fingerprint outperforms chemical descriptors, Mondrian regression outperforms knearest neighbor for one or several values of coverage, and the mean, median, and standard deviation of the output interval. None of the investigated machine learning methods extremely outperforms the other methods. Conformal predictive system, an alternative form of conformal prediction was also investigated to explore its usefulness in drug discovery.

It is well known that genetic variation and ability to adapt is crucial for the survival of anypopulation. Whether it be about a natural population’s ability to respond to changes in itsenvironment, or a population of livestock’s ability to produce more milk, genetic variation is akey element. Effectively small populations have an increased risk of extinction caused byreduced ability to adapt or respond to selection. Small populations are also more affected bygenetic drift, which can cause deleterious mutations to fixate, reducing the populations’ fitnesspossibly to the point where it is unable to survive. Models describing changes in allelefrequencies in a population under selection can be used to study a population’s response toselection. A limitation to such models is they often assume infinite population size and neglectthe effects of genetic drift, making them unable to implement when working with effectivelysmall populations.Here, an individual-based model of a quantitative trait affected by selection, mutation andgenetic drift is used to study the adaptive potential of effectively small populations. In a series ofsimulations, changes in the trait are explored under directional selection and stabilizingselection with adaptation to one, and several repeated shifts in optimum. Results of simulationinclude that populations under strong directional selection, such as breeding, potentially risklosing all adaptive potential. Results also suggest that effects of strong directional selectionmight be irreversible, even if the strong selective pressure is removed.

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published_or_final_version / Microbiology / Doctoral / Doctor of Philosophy