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För att kunna livnära en växande population behöver avkastningen på skördar öka. En lösning på dettaär att optimera plantornas fotosyntes, vilket innefattar förbättrad koldioxidfixering. För att lyckas meddet krävs kunskap i hur reglering av nyckelproteiner i kloroplasten går till. Syftet med detta projekt är identifiera möjliga reglerande protein-metabolitinteraktioner i Arabidopsis thaliana. Målproteinerna ärde 11 enzymerna i Calvin-Benson-Basshamcykeln. Metaboliterna som testas är 3PGA, ATP, FBP, GAP, vilka är mellan produkter eller kofaktorer i cykeln; 2PG, som är en produkt av en konkurrerande reaktion i cykeln; och slutligen G6P, citrat och sackaros, vilka är centrala metaboliter i andra viktiga reaktioner i cellen.  Före experimenten med Arabidopsis testades protokollen med spenat.  Som ett första steg isolerades kloroplasterna från blad. När intakta kloroplaster verifierats extraherades proteinerna. Inter-aktioner mellan metaboliterna och proteinerna analyserades med en metod kallad limited proteolysis-small molecule mapping. Denna teknik, vilken kombinerar begränsad proteolys med masspektrometri, detekterade flertalet protein-metabolit interaktioner. I Arabidopsis uppvisade alla enzym förutom FB-Pase, PPE och TIM minst en interaktion. I spenat sågs interaktioner med FBA, GAPDH, PGK, PRK, RuBisCO, TIM och TK. Resultaten visar möjliga reglerande interaktioner, vilka skulle kunna användasför att identifiera flaskhalsar i kolfixeringen. Denna kunskap kan i sin tur utnyttjas för att öka flödet i Calvin-Benson-Basshamcykeln och därigenom förbättra växters koldioxidfixering. / In order to feed a growing population, the crop yield needs to be increased.  One way to do this is to optimise the photosynthetic activity in the plant, which includes improvement of carbon fixation. To succeed with this, knowledge of the regulation of key proteins in the chloroplast is required. The aim of this project is to identify possible regulatory protein-metabolite interactions in chloroplasts from Arabidopsis thaliana. The target proteins are the 11 enzymes of the Calvin-Benson-Bassham cycle. The metabolites of interest are 3PGA, ATP, FBP, GAP, which are intermediates or co-factors of the cycle;2PG, which is a product of a competing reaction in the cycle; and finally G6P, citrate and sucrose, which  are central metabolites in other vital reactions in the cell. Before the experiments with Arabidopsis, spinach was used as a test organism to evaluate the proposed protocols. First, chloroplasts were isolatedfrom leaves. When the integrity of the chloroplasts had been validated, the proteins were extracted. Metabolic interactions with the extracted proteins were analyzed with limited proteolysis-small molecule mapping. This method, which combines limited proteolysis with mass spectrometry, detected severalprotein-metabolite interactions. In Arabidopsis, all enzymes except for FBPase, PPE and TIM had atleast one interaction. In spinach, interactions were seen with FBA, GAPDH, PGK, PRK, RuBisCO,TIM and TK. The results highlight potential regulatory events, which could be used to target bottlenecks in carbon fixation. This could provide a pathway to increase the flux in the Calvin-Benson-Bassham cycle, and thereby improve carbon fixation in plants.

A large number of emerging infectious diseases (including influenza epidemics) has been identified during the last century. The emergence and re-emergence of infectious diseases have a negative impact on global health. Influenza epidemics alone cause between 3 and 5 million cases of severe illness annually, and between 250,000 and 500,000 deaths. In addition to the human suffering, influenza epidemics also impose heavy demands on the health care system. For example, hospitals and intensive care units have limited excess capacity during infectious diseases epidemics. Therefore, it is important that increased influenza activity is noticed early at local levels to allow time to adjust primary care and hospital resources that are already under pressure. Algorithms for the detection and prediction of influenza epidemics are essential components to achieve this. Although a large number of studies have reported algorithms for detection or prediction of influenza epidemics, outputs that fulfil standard criteria for operational readiness are seldom produced. Furthermore, in the light of the rapidly growing availability of “Big Data” from both diagnostic and prediagnostic (syndromic) data sources in health care and public health settings, a new generation of epidemiologic and statistical methods, using several data sources, is desired for reliable analyses and modeling. The rationale for this thesis was to inform the planning of local response measures and adjustments to health care capacity during influenza epidemics. The overall aim was to develop a method for detection and prediction of influenza epidemics. Before developing the method, three preparatory studies were performed. In the first of these studies, the associations (in terms of correlation) between diagnostic and pre-diagnostic data sources were examined, with the aim of investigating the potential of these sources for use in influenza surveillance systems. In the second study, a literature study of detection and prediction algorithms used in the field of influenza surveillance was performed. In the third study, the algorithms found in the previous study were compared in a prospective evaluation study. In the fourth study, a method for nowcasting of influenza activity was developed using electronically available data for real-time surveillance in local settings followed by retrospective application on the same data. This method includes three functions: detection of the start of the epidemic at the local level and predictions of the peak timing and the peak intensity. In the fifth and final study, the nowcasting method was evaluated by prospective application on authentic data from Östergötland County, Sweden. In the first study, correlations with large effect sizes between diagnostic and pre-diagnostic data were found, indicating that pre-diagnostic data sources have potential for use in influenza surveillance systems. However, it was concluded that further longitudinal research incorporating prospective evaluations is required before these sources can be used for this purpose. In the second study, a meta-narrative review approach was used in which two narratives for reporting prospective evaluation of influenza detection and prediction algorithms were identified: the biodefence informatics narrative and the health policy research narrative. As a result of the promising performances of one detection algorithm and one prediction algorithm in the third study, it was concluded that both further evaluation research and research on methods for nowcasting of influenza activity were warranted. In the fourth study, the performance of the nowcasting method was promising when applied on retrospective data but it was concluded that thorough prospective evaluations are necessary before recommending the method for broader use. In the fifth study, the performance of the nowcasting method was promising when prospectively applied on authentic data, implying that the method has potential for routine use. In future studies, the validity of the nowcasting method must be investigated by application and further evaluation in multiple local settings, including large urbanizations.

Recent advances in molecular biology has led to the discovery of many new potential drugs. However, difficulties with in situ analysis of ligand binding prevents quick advancement in clinical trials, which stresses the need for better direct methods. A relatively new methodology, called Cellular Thermal Shift Assay (CETSA), allows for detection of ligand binding in a cells natural environment and can be used in combination with Mass Spectrometry (MS) for readout. With help from the Pelago Bioscience team, I developed a pipeline for processing of CETSA MS data and a web based system for viewing the results. The system, called CETSA Analytics, also evaluates the results relevance and helps its users to locate information efficiently. CETSA Analytics is currently being tested by Pelago Bioscience AB as a tool for experimental data distribution.

Archetypes are used to standardize a way of creating, presenting and distributing health care data. In this master thesis project the open specifications of openEHR was followed. The objective of this master thesis project has been to develop a Java based API for creating and editing openEHR archetypes. The API is a programming toolbox that can be used when developing archetype editors. Another purpose has been to implement validation functionality for archetypes. An important aspect is that the functionality of the API is well documented, this is important to ease the understanding of the system for future developers. The result was a Java based API that is a platform for future archetype editors. The API-kernel has optional immutability so developed archetypes can be locked for modification by making them immutable. The API is compatible with the openEHR specifications 1.0.1, it can load and save archetypes in ADL (Archetype Definition Language) format. There is also a validation feature that verifies that the archetype follows the right structure with respect to predefined reference models. This master thesis report also presents a basic GUI proposal.

Cell functions are steered by complex interactions of gene products, like forming a temporary or stable complex, altering gene expression or catalyzing a reaction. Mapping these interactions is the key in understanding biological processes and therefore is the focus of numerous experiments and studies. Small-scale experiments deliver high quality data but lack coverage whereas high-throughput techniques cover thousands of interactions but can be error-prone. Unfortunately all of these approaches can only focus on one type of interaction at the time. This makes experimental mapping of the genome-wide network a cost and time intensive procedure. However, to overcome these problems, different computational approaches have been suggested that integrate multiple data sets and/or different evidence types. This widens the stringent definition of an interaction and introduces a more general term - functional association.  FunCoup is a database for genome-wide functional association networks of Homo sapiens and 16 model organisms. FunCoup distinguishes between five different functional associations: co-membership in a protein complex, physical interaction, participation in the same signaling cascade, participation in the same metabolic process and for prokaryotic species, co-occurrence in the same operon. For each class, FunCoup applies naive Bayesian integration of ten different evidence types of data, to predict novel interactions. It further uses orthologs to transfer interaction evidence between species. This considerably increases coverage, and allows inference of comprehensive networks even for not well studied organisms.  BinoX is a novel method for pathway analysis and determining the relation between gene sets, using functional association networks. Traditionally, pathway annotation has been done using gene overlap only, but these methods only get a small part of the whole picture. Placing the gene sets in context of a network provides additional evidence for pathway analysis, revealing a global picture based on the whole genome. PathwAX is a web server based on the BinoX algorithm. A user can input a gene set and get online network crosstalk based pathway annotation. PathwAX uses the FunCoup networks and 280 pre-defined pathways. Most runs take just a few seconds and the results are summarized in an interactive chart the user can manipulate to gain further insights of the gene set's pathway associations. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 2: Manuscript.</p>

Recently Whole Genome Sequencing (WGS) has become the new high-resolution tool used to trace the source of foodborne outbreaks. There are often only a few genetic differences that can distinguish closely related bacterial isolates, and variability in data quality between different laboratories may influence the results. In this project, a data set from ten laboratories where the same bacterial samples were sequenced using different library preparation kits and sequencing methods in an interlaboratory study, has been used. Factors that could be responsible for the different performance in terms of how well the raw WGS data from the different labs assembles were investigated. The raw data from the different labs assembled very differently. One lab showed adapter sequences in their reads and filtering them improved the assembly substantially. All labs utilizing the transposase-based library preparation kit Nextera, had base composition bias in the beginning of the reads. For many labs, as the coverage was increased, the number of contigs first increased and then decreased. This was due to low number of contaminating reads from other species. However, these contaminations were barely visible in the plots generated by Kraken/Krona. Filtering out contigs with very low coverage removed this problem. Two labs performed much worse than the others. Some of their reads showed quality drop towards the ends, whereas their data also had the longest read length. However, quality trimming the read ends did not improve the assembly. These two labs had higher GC content in their reads compared to the other labs, the reason for this needs further investigation.

Följande studie behandlar utvecklandet av automatiserade cellsegmenteringsmodeller med avsikt att identifiera gränser mellan celler i hjärtvävnad. Syftet är att möjliggöra analys av data genererad från multiplexed error-robust in situ hybridization (MERFISH). MERFISH är en spatial transcriptomics-teknik som till skillnad från exempelvis single-cell RNA sequencing (ScRNA-seq) och single molecule fluorescence in situ hybridization (smFISH), möjliggör profilering av hundratals RNA-sekvenser hos enskilda celler utan att förlora dess rumsliga kontext. I Kosuri laboratoriet på Salk Institute of Biological Studies i San Diego tillämpas MERFISH på mushjärtan. Syftet är att få en djupare insikt i hur celler är organiserade i friska hjärtan, och hur denna struktur ändras i och med åldring och sjukdom. Att extrahera meningsfull information från MERFISH medför dock en betydande utmaning - en exakt cellsegmentering. Studien bidrar följaktligen till utvecklandet av segmenteringsmodeller för att kringgå de utmaningar som står i vägen för all efterföljande analys. Då klassiska segmenteringsalgoritmer är otillräckliga för att segmentera den komplexa vävnad som hjärtat utgörs av, tillämpades några av dagens mest avancerade och framstående maskininlärningsalgoritmer inom fältet, kallade Cellpose och Omnipose. Givet den täta och heterogena hjärtvävnaden, som härstammar från en bred distribution av celltyper och geometrier, utvecklades två separata modeller; en för att täcka både mindre celler och kardiomyocyter skurna på tvärsnittet; och en för att enbart segmentera kardiomyocyter skurna i longitudinell riktning. Den förstnämnda modellen utvecklades och tränades i Cellpose, och uppnådde en träffsäkerhet på 91.2%. Modellen för longitudinella kardiomyocyter utvecklades istället både i Cellpose och Omnipose för att utvärdera vilket nätverk som är bäst lämpat för ändamålet. Ingen av nätverken lyckades uppnå en tillräckligt hög träffsäkerhet för att vara applicerbar, och är därmed i behov av fortsatt träning. Modellen genererad i Omnipose bedöms dock vara mest lovande, givet dess mer heltäckande segmentering. Ytterligare utvecklingsområden för framtiden innefattar segmentering av celler i fibros-täta regioner, samt att utveckla en 3D-segmentering av hela hjärtat för att uppnå en mer komplett MERFISH-analys. Sammanfattningsvis har de genererade segmenteringsmodellerna banat väg för möjliggörandet av en rigorös MERFISH-analys av hjärtat. Genom att avslöja några av de strukturella och funktionella orsakerna till hjärtsvikt på en cellulär nivå, kan vi således på sikt bidra till utvecklingen av mer effektiva terapeutiska strategier. / The following study delves into the development of automated cell segmentation models, with the intention of identifying boundaries between cells in the cardiac tissue for analysing spatial transcriptomics data. Addressing the limitations of alternative techniques like single-cell RNA sequencing (ScRNA-seq) and single molecule fluorescence in situ hybridization (smFISH), the study underscores the innovative use of multiplexed error-robust fluorescence in situ hybridization (MERFISH) deployed by the Kosuri Lab at Salk Institute for Biological Studies. This advanced imaging-based technique allows for a single-cell transcriptome profiling of hundreds of different transcripts while retaining the spatial context of the tissue. The technique can accordingly reveal how the organization of cells within a healthy heart is altered during disease. However, the extraction of meaningful data from MERFISH poses a significant challenge - accurate cell segmentation. This thesis therefore presents the development of a robust model for cell boundary identification within cardiac tissue, leveraging some of the advanced supervised machine learning algorithms in the field, named Cellpose and Omnipose. Due to the dense and highly heterogeneous tissue- stemming from a wide distribution of cell types and shapes- two separate models had to be developed; one that covers the smaller cells and the cross-sectioned cardiomyocytes, and correspondingly one to cover the longitudinal cardiomyocytes. The cross-section model was successfully developed to achieve an accuracy of 91.2%, whereas the longitudinal model still needs further improvements before being implemented. The thesis acknowledges potential areas for improvement, emphasizing the need to further improve the segmentation of longitudinal cardiomyocytes, tackle the challenges with segmenting cells within fibrotic regions of the diseased heart, as well as achieving a precise 3D cell segmentation. Nonetheless, the generated models have paved the way towards enabling efficient downstream MERFISH analysis to ultimately understand the structural and functional dynamics of heart failure at a cellular level, aiding the development of more effective therapeutic strategies.

The increase in demand for food has resulted in increased demand for tools that help streamline plant breeding process in order to create new varieties of crops. Identifying the underlying genetic mechanism of favourable characteristics is essential in order to make the best breeding decisions. In this project we have developed a modified autoencoder model which allows for lateral phenotype injection into the latent layer, in order to identify causal SNPs for phenotypes of interest in wheat. SNP and phenotype data for 500 samples of Lantmännen SW Seed provided by Lantmännen was used to train the network. Artificial phenotype created using a single SNP was used during training instead of real phenotype, since the relationship between the phenotype and SNP is already known. The modified training model with lateral phenotype injection showed significant increase in genotype concordance of the artificial phenotype when compared to the control model without phenotype injection. Causal SNP was successfully identified by using concordance terrain graph, where the difference in concordance of individual SNPs  between the modified modified model and control model was plotted against the genomic position of each SNP. The model requires further testing to elucidate its behaviour for phenotypes linked to multiple SNPs.

Cancer is a leading cause of mortality worldwide, responsible for nearly one in six deaths. Thus, there is a need for a greater understanding of cancer for the development of novel therapeutics. This master thesis project aims to compare the proteome signatures between dying and surviving cancer cells treated with diverse anticancer drugs. The first aim is to investigate if drug targets behave similarly and have the same sign (up- or down-regulation) in dying versus surviving cells. The second aim is to validate that combining the dying cancer cell’s proteome with the surviving cell’s can help improve drug target rankings for anticancer treatments. The third aim is to identify proteins and pathways involved in life and death decisions by comparing dying and surviving states in response to the anticancer drugs in different cell lines. First, we demonstrate that drug target behaviour in dying versus surviving cells is almost identical for nine diverse anticancer compounds with a correlation of 0.93. To identify drug targets, orthogonal partial least squares-discriminant analysis (OPLS-DA) modelling was performed to contrast the proteome signature of one anticancer drug against all other drugs and rank the proteins based on the magnitude of the model’s predictive component. There were occasions when the dying cells gave better rankings than the surviving ones. In some cases, the best target rankings were obtained when combining the data from both surviving and dying cells. To identify proteins and pathways involved in life and death decisions, OPLS-DA modelling contrasting the two states was performed, and heatmaps and scatterplots of dying and surviving log2 fold changes were made. As a result, several pathways involved in cell survival and cell death were identified. In addition, at least six proteins consistently differentially regulated between the surviving and dying cells were identified. Such proteins can be considered as putative survival (resistance) or sensitivity biomarkers and serve as potential drug targets for the development of novel anticancer agents.