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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Escherichia coli (E. coli) is among the gram-negative bacteria that can cause several infections including sepsis. Confirmed sepsis patients must show a sequential organ failure assessment (SOFA) score of ≥2 with verified infection. Understanding the genotypic characteristics of E. coliclinical isolates from sepsis patients can help directing treatment strategies, tracking antibiotic resistance, and monitoring acquired virulence factors that can contribute to the severity of sepsis. The isolates included in this thesis were collected from confirmed sepsis patients (SOFA 2-3)during a prospective observational study that was conducted in Sweden. The aim was to study the biodiversity in E. coli clinical isolates using whole genome sequencing (WGS) paired-end reads that were produced by the next-generation sequencer Illumina. To perform the WGS-based analysis, two bioinformatics pipelines were used. The first is the in-house developed pipeline and the second is the 1928Diagnostics E. coli pipeline. The obtained in silico results were compared with the phenotypic findings for species identification and the in vitro predictions of antibiotic resistance. Species identification by the bioinformatics pipelines matched the phenotypic method, except for three isolates that were highly contaminated with other species. Both pipelines predicted the exact multi locus sequence types, which revealed that the most common sequence types (STs) were ST73(17%), ST95(9%), and ST131(6%). The phenotype of the isolates resulted in 5% resistant to at least one of the assessed antibiotics. The 1928Diagnostics predicted 28% of the isolates were resistant to at least one class of the tested antibiotic classes, while the in-house pipeline predicted 33% of the isolates to be resistant. Out of the predicted resistant isolates, 52% coded for multi-drug resistance. The in-house pipeline reported virulence genes. The common reported genes were coding for iron reuptake, adhesins, cell outer membrane and increased serum survival. It was observed that the isolates that belonged to ST73 and ST95 showed a more susceptible antibiotic profile than isolates that belonged to ST131, those harbored the highest mean of virulence genes. In conclusion, the present study provided an evidence of the usefulness of the WGS-based analysis to study the biodiversity in E. coli. The obtained results are valuable for surveillances, tracking antibiotic resistance and identifying virulence factors, but with a limited use in clinical settings.

Rheumatoid arthritis (RA) is a systematic autoimmune disorder characterized by a persistent joint inflammation. A subset of HLA-DRB1 alleles known as shared epitope (SE) are the strongest genetic risk factors to develop anti-citrullinated protein antibody positive (ACPA-positive) RA. A strong enrichment of interactions exists between ACPA-positive RA-associated genetic variants and HLA-DRB1 SE alleles in disease development. Pathway analysis was performed to investigate how the interactions between risk variants (SNPs) with HLA-DRB1 from a previous study related to ACPA-positive RA. Gene-gene interactions analysis was performed between non-HLA risk variants and HLA-DRB1 SE alleles in SRQ biobank (SRQb) case-control cohort. We also evaluated whether the reported gene-gene interactions from a previous study relate to methotrexate (MTX) response for RA patients, at three and six months of follow-up in EIRA study. Interaction analysis based on an additive model was performed to understand the combined effect of two risk factors in the disease and treatment response. Two out of three genes from pathway analysis that were RXRA and NR3C1, pointed to ACPA-positive RA related important pathways including vitamin D receptor (VDR) pathway and adipocytokine signaling pathway. The replication analysis in SRQ-case-control study showed 2.627% of the evaluated SNPs insignificant additive interaction with HLA-DRB1 SE alleles. No interactions were significant in relation to the response to MTX monotherapy after 3 and 6 months follow-up. This project provides new insights into the gene-gene interactions in the study of ACPA-positive RA and suggests candidate genes for future functional studies.

The microbiome has become a focus of research, particularly in the field of human health and precision medicine, due to its role in human development, immunity, and nutrition. Microbiome profiling studies have become more tractable and advanced in large part thanks to advancements in metagenomics. One such study is the Black Women’s Health Study (BWHS), which aims to better understand health risks and disease development specific to Black women, who are more susceptible to certain health conditions. However, a major obstacle for reproducibility of microbiome research is the high sensitivity of microbial compositions to external factors and batch-to-batch technical variability, resulting in batch effects that often hinder analysis of factors of interest. While batch effect adjustment methods have been developed for other biomedical data, they do not appropriately account for two unique features of microbiome data: 1) its compositional nature, and 2) extreme overdispersion and zero-inflation. My dissertation addresses these challenges by evaluating and improving batch effect correction methods for microbiome data and then applies these approaches to data from BWHS. First, I evaluated ComBat-Seq, along with existing microbiome-specific tools, in removing batch effects from both simulated 16S rRNA and real-world shotgun metagenomic sequencing data while preserving effects belonging to biological factors of interest. Second, I applied ComBat-Seq in an epidemiological study in which I identified several oral health-related genera among adult Black women to be associated with the host’s geographic location in the US. Finally, I introduced an extension to ComBat-Seq that improves its performance in batch effect correction on rare taxa with outliers via imputation. I demonstrated that, by replacing zeroes with predicted non-zero read counts that follow the observed compositional structure of the data, imputation effectively reduced the number of problematic cases in which outliers were intensified after batch effect correction. Collectively, my thesis demonstrates that 1) when the specific features of microbiome data are accounted for, batch effect correction methods offer a promising solution to address batch effect in microbiome data and improve microbiome profiling studies and 2) it is important to consider social/environmental factors associated with the host’s physical location when studying the oral microbiome.