High mobility group box 1 (HMGB1) was first identified as a chromatin-associated protein and later discovered to initiate and regulate inflammation by inducing cytokine production, cell migration and cell differentiation. HMGB1 forms complexes with a variety of proteins (e.g. C1q, LPS, CXCL12, IL-1a, IL1b, Beclin-6, p53) that in turn play a role in different cellular mechanisms. However, most HMGB1-protein complexes identified are found in the extracellular space whereas intracellular HMGB1-protein complexes are far less defined.  Firstly, data of HMGB1 interactome was previously generated by Rebecka Heinbäck, Erlandsson Harris group at KI. The HMGB1 interactome was identified in resting and in LPS-stressed THP-1 cells using a method called BioID.  The objective was to explore possible intracellular HMGB1 protein-protein interactions during resting and inflammatory conditions. HMGB1 in complex with other proteins have been known to exhibit crucial functions, therefore our investigation can lead to important knowledge in developing promising future therapeutics targeting HMGB1 in addition to further knowledge on intracellular functions of HMGB1. In this project, we used a combination of different computational analysis tools to explore the roles of HMGB1 and its interactome. Thereafter, we selected proteins within the BioID dataset that were further investigated for direct protein-protein interactions with HMGB1 using computational modelling as well as laboratory techniques, such as co-immunoprecipitation.  Our data reveals functional and biological differences of HMGB1 in resting and LPS activated THP-1 cells. Within resting cells, the HMGB1 interactome is involved in transduction and transcription processes whereas under LPS-stressed conditions HMGB1 is indicated in apoptosis, HATs, and processes in antiviral mechanisms, mainly when localised in the cytosol. Additionally, we revealed potential direct interaction of HMGB1 to S100A6 and HCLS1, in which both can induce different functionalities. Finally, we have further explored the interaction possibilities of HMGB1:S100A6 complex to RAGE, where we found interesting, preliminary results that should be further explored.  To conclude, this thesis suggests new direct, intracellular interaction partners to HMGB1 and indicates a shift in the HMGB1 interactome following LPS stress.

Human motion analysis (HMA) can play a crucial role in sports and healthcare by providing unique insights on movement mechanics in the form of objective measurements and quantitative data. Traditional, state of the art, marker-based techniques, despite their accuracy, come with financial and logistical barriers, and are restricted to laboratory settings. Markerless systems offer much improved affordability and portability, and can potentially be used outside of laboratories. However, these advantages come with a significant cost in accuracy. This thesis attempts to address the challenge of democratizing HMA by leveraging recent advances in smartphone technology and machine learning.\newline\newlineThis thesis evaluates two modalities of performing markerless HMA: Single smartphone using Apple Arkit, and multiple smartphone setup using OpenCap, and compares both to a state of the art multiple-camera marker-based system from Vicon. Additionally, this thesis presents and evaluates two approaches to improving the single smartphone modality: Employing a Gaussian Process Model (GPR), and a Long-short-term-memory (LSTM) neural network to refine the single smartphone data to align with the marker-based result. Specific movements were recorded simultaneously with all three modalities on 13 subjects to build a dataset. From this, GPR and LSTM models were trained and applied to refine the single camera modality data. Lower limb joint angles, and joint centers were evaluated across the different modalities, and analyzed for potential use in real-world applications. While the findings of this thesis are promising, as both the GPR and LSTM models improve the accuracy of Apple Arkit, and OpenCap providing accurate and consistent results. It is important to acknowledge limitations regarding demographic diversity and how real-world environmental factors may influence its application. This thesis contributes to the efforts in narrowing the gap between marker-based HMA methods, and more accessible solutions. / Rörelseanalys av människokroppen (HMA) kan spela en betydelsefull roll i både idrott och hälso- och sjukvården. Genom objektiv och kvantitativ data ger den unik insikt i mekaniken bakom rörelser. Traditionella, toppmoderna, markör-baserade tekniker är mycket precisa, men medför finansiella och logistikbaserade barriärer, och finns endast tillgängliga i laboratorier. Markör-fria system erbjuder mycket bättre pris, portabilitet och kan potentiellt användas utanför laboratorier. Dessa fördelar går dock hand i hand med en betydande minskning av nogrannhet. Denna avhandling försöker ta itu med utmaningen att demokratisera HMA genom att utnyttja de senaste framstegen inom smartphoneteknik och maskininlärning. Denna avhandling utvärderar två sätt att utföra markör-fri HMA: Genom att använda en smartphone som kör Apple Arkit, och en uppsättning med flera smartphones som kör OpenCap. Båda modaliteter jämförs med ett markör-baserat system som använder flera kameror, från Vicon. Dessutom presenteras och utvärderas två metoder för att förbättra modaliteten med endast en smartphone: Användning av en Gaussisk Process modell för Regression (GPR) och ett Long-short-term-memory (LSTM) neuronnät för att förbättra data från en smartphone modalititeten, så att det bättre överenstämmer med det markör-baserade resultatet. Specifika rörelser spelades in samtidigt med alla tre modaliteter på 13 försökspersoner för att bygga upp ett dataset. Utifrån detta tränades GPR- och LSTM-modeller och användas för att förbättra data från en kamera modaliteten (Apple Arkit). Ledvinklar och ledcentra för de nedre extremiteterna utvärderades i de olika modaliteterna och analyserades för potentiell använding i verkliga tillämpningar. Även om resultaten av denna avhandling är lovande, då både GPR- och LSTM-modellerna förbättrar nogrannheten hos Apple Arkit, och OpenCap ger korrekta och konsekventa resultat, så är det viktigt att erkänna begränsningarna när det gäller demografisk mångfald och hur miljöfaktorer i verkligheten kan påverka tillämpningen.

Greater understanding of how we can use protein simulations and statistical characteristics of biomolecular interfaces as proxies for biological function will make manifest major advances in protein engineering. Here we show how to use calculated change in binding affinity and coevolutionary scores to predict the functional effect of mutations in the interface between a Histidine Kinase and a Response Regulator. These proteins participate in the Two-Component Regulatory system, a system for intracellular signalling found in bacteria. We find that both scores work as proxies for functional mutants and demonstrate a ~30 fold improvement in initial positive predictive value compared with choosing randomly from a sequence space of 160 000 variants in the top 20 mutants. We also demonstrate qualitative differences in the predictions of the two scores, primarily a tendency for the coevolutionary score to miss out on one class of functional mutants with enriched frequency of the amino acid threonine in one position.

In situ sekvensering är en metod som kan användas för att lokalisera differentiellt uttryck av mRNA direkt i vävnadssnitt, vilket kan ge viktiga ledtrådar om många sjukdomstillstånd. Idag förloras många av sekvenserna från in situ sekvensering på grund av det kvalitetsmått man använder för att säkerställa att sekvenser är korrekta. Det finns troligtvis möjlighet att förbättra prestandan av den nuvarande base calling-metoden eftersom att metoden är i ett tidigt utvecklingsskede. Vi har genomfört explorativ dataanalys för att undersöka förekomst av systematiska fel och korrigerat för dessa med hjälp av statistiska metoder. Vi har framförallt undersökt tre metoder för att korrigera för systematiska fel: I) Korrektion av överblödning som sker på grund avöverlappande emissionsspektra mellan fluorescenta prober. II) En sannolikhetsbaserad tolkningav intensitetsdata som resulterar i ett nytt kvalitetsmått och en alternativ klassificerare baseradpå övervakad inlärning. III) En utredning om förekomst av cykelberoende effekter, exempelvisofullständig dehybridisering av fluorescenta prober. Vi föreslår att man gör följande saker: Implementerar och utvärderar det sannolikhetsbaserade kvalitetsmåttet Utvecklar och implementerar den föreslagna klassificeraren Genomför ytterligare experiment för att påvisa eller bestrida förekomst av ofullständigdehybridisering / In situ sequencing is a method that can be used to localize differential expression of mRNA directly in tissue sections, something that can give valuable insights to many statest of disease. Today, many of the registered sequences from in situ sequencing are lost due to a conservative quality measure used to filter out incorrect sequencing reads. There is room for improvement in the performance of the current method for base calling since the technology is in an early stage of development. We have performed exploratory data analysis to investigate occurrence of systematic errors, and corrected for these by using various statistical methods. The primary methods that have been investigated are the following: I) Correction of emission spectra overlap resulting in spillover between channels. II) A probability-based interpretation of intensity data, resulting in a novel quality measure and an alternative classifier based on supervised learning. III) Analysis of occurrence of cycle dependent effects, e.g. incomplete dehybridization of fluorescent probes. We suggest the following: Implementation and evaluation of the probability-based quality measure Development and implementation of the proposed classifier Additional experiments to investigate the possible occurrence of incomplete dehybridization

Mapping of the human genome has been instrumental in understanding diseasescaused by changes in single genes. However, disease mechanisms involvingmultiple genes have proven to be much more elusive. Their complexityemerges from interactions of intracellular molecules and makes them immuneto the traditional reductionist approach. Only by modelling this complexinteraction pattern using networks is it possible to understand the emergentproperties that give rise to diseases.The overarching term used to describe both physical and indirect interactionsinvolved in the same functions is functional association. FunCoup is oneof the most comprehensive networks of functional association. It uses a naïveBayesian approach to integrate high-throughput experimental evidence of intracellularinteractions in humans and multiple model organisms. In the firstupdate, both the coverage and the quality of the interactions, were increasedand a feature for comparing interactions across species was added. The latestupdate involved a complete overhaul of all data sources, including a refinementof the training data and addition of new class and sources of interactionsas well as six new species.Disease-specific changes in genes can be identified using high-throughputgenome-wide studies of patients and healthy individuals. To understand theunderlying mechanisms that produce these changes, they can be mapped tocollections of genes with known functions, such as pathways. BinoX wasdeveloped to map altered genes to pathways using the topology of FunCoup.This approach combined with a new random model for comparison enables BinoXto outperform traditional gene-overlap-based methods and other networkbasedtechniques.Results from high-throughput experiments are challenged by noise and biases,resulting in many false positives. Statistical attempts to correct for thesechallenges have led to a reduction in coverage. Both limitations can be remediedusing prioritisation tools such as MaxLink, which ranks genes using guiltby association in the context of a functional association network. MaxLink’salgorithm was generalised to work with any disease phenotype and its statisticalfoundation was strengthened. MaxLink’s predictions were validatedexperimentally using FRET.The availability of prioritisation tools without an appropriate way to comparethem makes it difficult to select the correct tool for a problem domain.A benchmark to assess performance of prioritisation tools in terms of theirability to generalise to new data was developed. FunCoup was used for prioritisationwhile testing was done using cross-validation of terms derived fromGene Ontology. This resulted in a robust and unbiased benchmark for evaluationof current and future prioritisation tools. Surprisingly, previously superiortools based on global network structure were shown to be inferior to a localnetwork-based tool when performance was analysed on the most relevant partof the output, i.e. the top ranked genes.This thesis demonstrates how a network that models the intricate biologyof the cell can contribute with valuable insights for researchers that study diseaseswith complex genetic origins. The developed tools will help the researchcommunity to understand the underlying causes of such diseases and discovernew treatment targets. The robust way to benchmark such tools will help researchersto select the proper tool for their problem domain. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 5: Manuscript. Paper 6: Manuscript.</p>

Methods for estimating past demographic events of populations are powerful tools in order to get insights of otherwise hidden pasts. The genetic data of people is a valuable resource for these purposes as patterns of variation can inform of the past evolutionary forces and historical events that generated them. There is, however, a lack of methods within the field that uses this information to its full extent. That is why this project has looked at developing a set of new alternatives for estimating demographic events. The work done has been based on modifying the purely sequence based method TTo (Two-Two-outgroup) for estimating divergence times of two populations. The modifications consisted of using beta distributions to model the polymorphic diversity of the ancestral population in order to increase the max sample size possible. The finished project resulted in two implemented methods: TT-beta and a partial variant of MM. TT-beta was able to produce estimations in the same region as TTo and showed that the usage of beta distributions had real potential. For MM there only was a partial implementation able to be done, but this one also showed promise and the ability to use varying sample sizes to estimate demographic values.

Only 40 years ago, just three individuals made the journey from Finland/Russia to found the current wolf population in the southwest of Sweden. This population, that to this date descends from less than 10 founders, has a substantial increased extinction risk due to inbreeding. Several previous studies have used SNPs to monitor the level of inbreeding and homozygosity in the population, as well as measure immigration and the inflow of new genetic material. This study uses both short- and long-read data to discover structural variants (SVs) and small indels in the population, so that they may be used to extend the analyses and provide more insight into the current state of the Scandinavian wolf population. After the calling of the SVs, strict filtering and manual curation were applied to the data, thereby removing many false positive calls and increasing confidence in the remaining SVs. Short-read and long-read SV-callers found 31,800 and 57,821 SVs respectively, with relatively little overlap between the two sets. By far, the most common SV-types were deletions and insertions, at about 30,000 each with varying length ranging from a 50 base pairs to several tens of Mbp. Analyses on the data, such as PCAs and parent-offspring trio analyses, reveal high-confidence calls and consistent results between SV-types and SV-callers, as well as a low estimated genotyping error rate. PCAs performed on the SVs resembled those performed on SNPs, which strengthens the credibility of the identified variants. Finally, this study suggests several alternative steps for possible improvement to the dataset, along with some proposals for subsequent research topics that may use the variants discovered in this study.