Browsing by master's degree program "Magisterprogrammet i informatik inom livsvetenskaperna"

Integrated population models (IPMs) are a promising approach to assess and manage wildlife populations in dynamic and uncertain conditions. By combining multiple data sources into a single, unified model, they enable the parametrization of versatile, mechanistic population models that can predict population dynamics in novel circumstances. This is in contrast to traditional approaches where independent empirical estimates for demographic parameters are typically incorporated into a population projection matrix such as a Leslie matrix. A major limitation of conventional methods is their inability to fully utilize all available information, as the synergies between different data sources are not exploited. The Baltic ringed seal (Pusa hispida botnica) presents an example illustrating the limitation of conventional monitoring approaches. Despite the availability of long-term monitoring data, population assessment is hindered by dynamic environmental conditions, varying reproductive rates, and the recently re-introduced hunting, thus limiting the quality of information available to managers regarding, for example, hunting quotas. In particular, population counts of ringed seals from aerial surveys have exhibited unexpected trends and large fluctuations during the last decade, making it impossible to obtain reliable estimates of population growth from survey data alone. This thesis presents a Bayesian IPM for the ringed seal population inhabiting the Bothnian Bay in the Baltic Sea. The central aim of this work is to outline an approach that can overcome some of the challenges that have crippled Baltic ringed seal monitoring efforts during the last decade, and support science-based management decisions. The thesis broadly consists of three parts. First, a state-space model is presented for the Bothnian Bay ringed seal population. Demographic processes are described through a stochastic age and sex structured population model that includes both hunting mortality and the hypothesized effects of environmental variables such as pollution and sea ice cover on demographic parameters and seal behaviour. Next, the model is fit to census and various demographic and reproductive data, as well as hunting statistics, from 1988 to 2023 under a Bayesian framework where posterior samples of model parameters are obtained using Markov Chain Monte Carlo methods. Finally, posterior estimates of model parameters are used to construct a Leslie matrix, and model behavior is analyzed using methods developed for matrix projection models. Future population dynamics are also simulated under alternative management scenarios to inform ringed seal management decisions. In general, this thesis demonstrates the value of mechanistic IPMs for monitoring and managing natural populations under changing environments, and supporting science-based management decisions.

Cancer consists of heterogeneous cell populations that repeatedly undergo natural selection. These cell populations contest with each other for space and nutrients and try to generate phenotypes that maximize their ecological fitness. For achieving this, they evolve evolutionarily stable strategies. When an oncologist starts to treat cancer, another game emerges. While affected by the cellular evolution processes, modeling of this game owes to the results of the classical game theory. This thesis investigates the theoretical foundations of adaptive cancer treatment. It draws from two game theoretical approaches, evolutionary game theory and Stackelberg leader-follower game. The underlying hypothesis of adaptive regimen is that the patient's cancer burden can be administered by leveraging the resource competition between treatment-sensitive and treatment-resistant cells. The intercellular competition is mathematically modelled as an evolutionary game using the G function approach. The properties of the evolutionary stability, such as ESS, the ESS maximum principle, and convergence stability, that are relevant to tumorigenesis and intra-tumoral dynamics, are elaborated. To mitigate the patient's cancer burden, it is necessary to find an optimal modulation and frequency of treatment doses. The Stackelberg leader-follower game, adopted from the economic studies of duopoly, provides a promising framework to model the interplay between a rationally playing oncologist as a leader and the evolutionary evolving tumor as a follower. The two game types applied simultaneously to cancer therapy strategisizing can nourish each other and improve the planning of adaptive regimen. Hence, the characteristics of the Stackelberg game are mathematically studied and a preliminary dose-optimization function is presented. The applicability of the combination of the two games in the planning of cancer therapy strategies is tested with a theoretical case. The results are critically discussed from three perspectives: the biological veracity of the eco-evolutionary model, the applicability of the Stackelberg game, and the clinical relevance of the combination. The current limitations of the model are considered to invite further research on the subject.

Progressive Multifocal Leukoencephalopathy (PML) is a rare but often fatal central nervous system demyelination disease caused by the reactivation of persistent JC polyomavirus (JCPyV) in immunosuppressed individuals. JCPyV infects oligodendrocytes in the brain, causing lysis of the glial cells, which leads to progressive demyelination and destruction of neurons seen as lesions in the white matter. The cause of JCPyV reactivation and how it reaches the brain are not well understood. MicroRNAs (miRNAs) are short non-coding RNAs which negatively regulate gene expression by marking mRNAs for destruction or by preventing translation. A Single miRNA can have multiple mRNA targets and multiple miRNAs can target the same mRNA, making the miRNA induced gene regulation a complex process affecting multiple different signaling pathways and cellular processes. The focus of the thesis is to study miRNA differential expression of PML patients compared to healthy individuals to find miRNAs and their target genes affected by JCPyV, while showing expertise in the data handling and data analysis of a miRNA sequencing experiment. The study was conducted by collecting miRNA samples from 8 PML patients and two controls and using Next-gen sequencing and the QuickMIRSeq analysis tool to collect miRNA counts for differential expression analysis. The analysis identified twelve miRNAs upregulated in the PML brain and multiple target genes interacting with two or more of the found miRNAs. The miRNAs were found to have connections to JCPyV replication, PML and important cellular processes such as neuroinflammation and BBB integrity.

In inductive inference phenomena from the past are modeled in order to make predictions of the future. The mathematical concept of exchangeability for random sequences provides a mathematical justification for the assumption that observations are independently and identically distributed given some underlying parameters estimable from the empirical distribution of the observations. The theory of exchangeability contains basic elements for inductive inference, such as the de Finetti representation theorem for the probability of a general exchangeable sequence, prior probability distributions for the parameters in the representation theorem, as well as the predictive probabilities, or rule of succession, for new observations from the random sequence under consideration. However, entirely unanticipated observations pose a problem for inductive inference. How can one assign a probability for an event that has never been seen before? This is called the sampling of species problem. Under exchangeability, the number of possible different events t has to be known before-hand to be able to assign an equal prior probability 1/t for each event. In the sampling of species problem an assumption of infinite possible events has to be made, leading to the prior probability 1/∞ for each event, which is impossible. Exchangeability is thus inadequate to handle probability distributions for infinite possible events. It turns out that a solution to the sampling of species problem arises from partition exchangeability. Exchangeable random sequences have the same probability of occurring, if the observations in the sequence have identical frequencies. Under partition exchangeability, the sequences have the same probability of occurring when they share identical frequencies of frequencies. In this thesis, partition exchangeability is introduced as a framework of inductive inference by juxtaposing it with the more familiar type of exchangeability for random sequences. Partition exchangeability has parallel elements to exchangeability, in the Kingman representation theorem, the Poisson-Dirichlet distribution for the prior probability distribution, and a corresponding rule of succession. The rules of succession are required in the problem of supervised classification to provide product predictive probabilities to be maximized by assigning the test data into pre-defined classes based on training data. A Bayesian construction of supervised classification is discussed in this thesis. In theory, the best classification performance is gained when assigning the class labels to the test data simultaneously, but because of computational complexity, an assumption is often made where the test data points are i.i.d. with regards to each other. In the case of a known set of possible events these simultaneous and marginal classifiers converge in their test data predictive probabilities as the amount of training data tends to infinity, justifying the use of the simpler marginal classifier with enough training data. These two classifiers are implemented in this thesis under partition exchangeability, and it is shown in theory and in practice with a simulation study that the same asymptotic convergence between the simultaneous and marginal classifiers applies with partition exchangeable data as well. Finally, a small application in single cell RNA expression is explored.

Ovarian cancer is the most lethal gynecological cancer and high-grade serous ovarian cancer (HGSOC) is the most common type of it. HGSOC is often diagnosed in advanced stages and most patients will relapse after optimal first-line treatment. One reason for the lack of successful treatment in HGSOC is high tumor heterogeneity including differences across the tumors in distinct patients, and even within each tumor. This heterogeneity is the result of genetic and non genetic factors. Phenotypical variabilty exists also within cancer cells that have the same genetic background. This is due to the fact that a cell can exist in more than one stable state where its genome is in a specific configuration and it expresses certain genes. Diverse cell states and transitions between them initially offer a path for tumor development, and later enable essential tumor behavior, such as metastasis and survival in variable environmental pressures, such as those posed by anti-cancer therapies. Generally, phenotypic heterogeneity is acquired from the cell of origin for a tumor. This thesis studies cell states in HGSOC cancer cells and their normal counterparts, fallopian tube epithelial cells. Exploration of cell states is based on gene expression data of individual cells. Gene expression data was analyzed with state-of-the-art tools and computational methods. Gene modules representing cell states were constructed using genes found in differential gene expression analysis of cancer cells, normal cells and tumor microenvironment. Differentially expressed gene (DEG) groups of cancer, normal FTE and shared epithelial genes were grouped separately into gene modules based on gene-gene associations and community detection. Potential dynamical relationships between cell states were addressed by pseudo-temporal ordering using RNA velocity modeling approach. We are able to capture biologically meaningful cell states which are relevant in the development of HGSOC with chosen research strategy. Found cell states represent processes such as epithelial-mesenchymal transition, inflammation and stress response which are known to have a role in cancer development. The transition patterns showed consistent tendencies across the samples, and the trajectories for normal samples presented more directionality than those of cancer specimens. The results indicate existence of shared epithelial states which stay in fixed positions in the developmental trajectory of normal and cancer cells. For example, both epithelial stem cells and stem-like cancer cells seem to utilize oxidative phosphorylation (OXPHOS) for their metabolic needs. On the other hand, cell states that are more terminal showed higher activities of tumor necrosis factor alpha and Wnt/beta-catenin pathways that were both mutually exclusive with OXPHOS. Overall, this thesis presents a novel approach to study cell states the characterization of which is essential in understanding tumorigenesis and cancer cell plasticity.

Unsupervised learning techniques can detect clinically relevant structure in population cohort data of human gut microbiota. While the gut microbiota composition is influenced by individual factors such as diet, medication, and development of the immune system during early childhood, it is proposed that individuals maintain a relatively stable microbiota ecosystem throughout adulthood. This stability allows to distinguish individuals into subgroups based on their gut microbiota characteristics, which define the key features of microbiota community types within the population. For this, I compared three probabilistic unsupervised learning techniques, optimization-based Non-negative Matrix Factorization, and Bayesian modelling techniques, Dirichlet Multinomial Mixtures and Latent Dirichlet Allocation, with a naive benchmark clustering based on dominant taxa. I used an all-cause mortality association strength as a quantitative metrics to distinguish biologically relevant structure in a large Finnish population cohort with almost 18 years follow-up. The techniques defined microbiota assemblages as either discrete enterotypes, which assigned each sample to a single community type, or continuous enterosignatures, which identified patterns of co-occurrence of microbiota community types within each sample. I found five rather robust community types, characterized by Bacteroides, Alistipes, Agathobacter, Escherichia, and Prevotella bacterial genera. Latent Dirichlet Allocation detected the strongest early mortality signal using Cox regression, outperforming all other techniques. The replicability of Latent Dirichlet Allocation was assessed using cross validation. The predicted community types uncovered similar ecological landscape on the data with the community types obtained using the entire data, confirming the clinical relevance, robustness, and scalability of the technique.

This work examines how neural networks can be used to qualitatively analyze systems of differential equations depicting population dynamics. We present a novel numerical method derived from physics informed learning, capable of extracting equilibria and bifurcations from population dynamics models. The potential of the framework is showcased three different example problems, a logistic model with outside inference, the Rosenzweig-MacArthur model and one model from a recent population dynamics paper. The key idea behind the method is having a neural network learn the dynamics of a free parameter ODE system, and then using the derivatives of the neural network to find equilibria and bifurcations. We, a bit clunkily, refer to these networks as physics informed neural networks with free parameters and variable initial conditions. In addition to these examples, we also survey how and where these neural networks could be further utilized in the context of population dynamics. To answer the how, we document our experiences choosing good hyperparameters for these networks, even venturing into previously unexplored territory. For the where, we suggest potentially useful neural network frameworks to answer questions from an external survey concerning contemporary open questions in population dynamics. The research of the work is preceded by a short dive on qualitative population dynamics, where we ponder what are the problems we want to solve and what are the tools we have available for that. Special attention is paid to parameter sensitivity analysis of ordinary differential equation systems through bifurcation theory. We also provide a beginner friendly introduction to deep learning, so that the research can be understood even by someone not previously familiar with the field. The work was written, and all included contents were selected, with the goal of establishing a basis for future research.

Background/Objectives: Various studies have shown the advantage when incorporating polygenic risk scores (PRSs) in models with classic risk factors. However, systematic comparisons of PRSs with non-genetic factors are lacking. In particular, many studies on PRSs do not even report the predictive performance of the confounders, such as age and sex, included in the model, which are already very predictive for most diseases. We looked at the ability of PRSs to predict the onset of 18 diseases in FinnGen R8 (N=342,499) and compared PRSs with the known non-genetic risk factors, age, sex, Education, and Charlson Comorbidity Index (CCI). Methods: We set up individual studies for the 18 diseases. A single study consisted of an exposure (1999-2009), a washout (2009-2011), and an observation period (2011-2019). Eligible individuals could not have the selected disease of interest inside the disease-free period, which ranged from birth until the beginning of the observation period. We then defined the case and control status based on the diagnoses in the observation period and calculated the phenotypic scores during the exposure period. The PRSs were calculated using MegaPRS and the latest publicly available genome-wide association study summary statistics. We then fitted separate Cox proportional hazards models for each disease to predict disease onset during the observation period. Results: In FinnGen, the model’s predictive ability (c-index) with all predictors ranged from 0.565 (95%CI: 0.552-0.576) for Acute Appendicitis to 0.838 (95% CI: 0.834-0.841) for Atrial Fibrillation. The PRSs outperformed the phenotypic predictors, CCI, and Education, for 6/18 diseases and still significantly enhance onset prediction for 13/18 diseases when added to a model with only non-genetic predictors. Conclusion: Overall, we showed that for many diseases PRSs add predictive power over commonly used predictors - such as age, sex, CCI, and Education. However, many important challenges must be addressed before implementing PRSs in clinical practice. Notably, we will need disease-specific cost- benefit analyses and studies to assess the direct impact of including PRSs in clinical use. Nonetheless, as more research is being conducted, PRSs could play an increasingly valuable role in identifying individuals at higher risk for certain diseases and enabling targeted interventions to improve health outcomes.

Essential thrombocythemia (ET) is a clonal hematopoietic disease characterized by an abnormal increase of platelets in the circulation, with increased risk of thrombosis and hemorrhage. Despite megakaryocytes having a central role in the disease, few studies have investigated their gene expression in ET. The aim of this study is to characterize the gene expression profiles of megakaryocytes from ET patients harboring different driver mutations, and increase the knowledge of the molecular mechanisms underlying the pathophysiology of the disease. In this study, samples were obtained from healthy donors and ET patients with JAK2 V617F, CALR Type I, CALR Type II driver mutations and triple-negative patients. Following megakaryocyte culture from peripheral blood and RNA sequencing, the data was pre-processed and analyzed using differential gene expression analysis. The downstream analysis was conducted using pathway enrichment analysis tools. The analysis revealed that all mutants shared common deregulated genes related to processes involving platelets and coagulation. However, it was shown that CALR and JAK2 V617F mutants also have distinct patterns of gene expression. CALR Type I mutants had a unique gene expression signature consisting of genes related to immune response, as well as metabolic, regulatory, proliferative, and inflammatory pathways, while CALR Type II mutants had unique genes related to ribosomes. The CALR mutants also shared a common anti-inflammatory response signature which set them apart from JAK2 V617F mutants. In conclusion, this study shows that the gene expression profiles of ET mutants are heterogeneous. Moreover, the results provide new insights into the gene expression profiles of CALR mutants that distinguish them from the other mutants. Further experiments using single-cell RNA sequencing methods could build upon these findings and uncover the observed gene expression discrepancies between CALR and JAK2 mutants with increased accuracy.

Cancer cells accumulate somatic mutations in their DNA throughout their lifetime. The advances in cancer prevention and treatment methods call for a deeper understanding of carcinogenesis on the genetic sequence level. Mutational signatures present a novel and promising way to capture somatic mutation patterns and define their causes, allowing to summarize the mutational landscape of cancer as a combination of distinct mutagenic processes acting with different levels of strength. While the majority of previous studies assume an additive relationship between the mutational processes, this Master’s thesis provides tentative evidence that contemporary methods with additivity constraints, e.g. non-negative matrix factorization (NMF), are not sufficient to comprehensively explain the observed mutations in cancer genomes and the observed deviations are not random. To quantify these residues, two metrics are defined – additive and multiplicative residues – and hierarchical clustering algorithms are used to identify cancer subsets with similar residual profiles. It is shown that in certain cancer sample subsets there is a systematic mutational burden overestimation that can only be solved by a multiplicatively acting process, as well as non-random underestimation, requiring additional mutational signatures. Here an extension to the additive mutational signature model is proposed – a probabilistic model that incorporates a selectively active modulatory mutational process that is able to act in a multiplicative manner together with the known mutational signatures, reducing systematic variability.