Extracellular vesicles are emitted by almost all cell types. They play an important role in cell-to-cell communication by passing biomolecules such as mRNAs to other cells by endocytosis. It is crucial to isolate and purify them from complex body fluids for studying exosomes. Various techniques, including ultracentrifugation, ultrafiltration, precipitation kits, and immunoprecipitation, are used to isolate exosomes (Bu et al., 2019). Each of these techniques has a significant impact on the properties and purity of the EVs obtained. This project aims to understand the effects of different isolation methods on exosome content by comparing the methods of UC and SEC, with three objectives. The first objective was to compare UC and SEC samples and identify DEGs from native EVs. The second objective was to analyze DEG functional annotation between isolation methods to understand their impact on EV content in biological processes and cellular components. The last objective was to investigate the protein-protien interactions (PPI) between the differentially expressed genes. To investigate the effects of EVs isolation methods at the transcriptional level, RNA-seq data were analyzed from a dataset of three different cell lines, including human lung epithelial cells (HTB-177), umbilical vein endothelial cells (HUVEC), and cardiac progenitor cells (CPC). RNA-seq analysis used an available transcriptomic dataset of EV samples isolated by UC and SEC methods. It identified 10, 15509, and 8995 DEGs from HTB, HUVEC, and CPC, respectively, and mapped them to pathways using EnrichR software. The study found that isolation methods and cell line sources affect analysis results. EnrichR analysis revealed the isolation method's impact on exosomal RNA content and regulation of biological processes.

Given the need for modern researchers to produce open, reproducible scientific output, the lack of standards and best practices for sharing data and workflows used to produce and analyze molecular dynamics (MD) simulations has become an important issue in the field. There are now multiple well-established packages to perform molecular dynamics simulations, often highly tuned for exploiting specific classes of hardware, each with strong communities surrounding them, but with very limited interoperability/transferability options. Thus, the choice of the software package often dictates the workflow for both simulation production and analysis. The level of detail in documenting the workflows and analysis code varies greatly in published work, hindering reproducibility of the reported results and the ability for other researchers to build on these studies. An increasing number of researchers are motivated to make their data available, but many challenges remain in order to effectively share and reuse simulation data. To discuss these and other issues related to best practices in the field in general, we organized a workshop in November 2018 (https://bioexcel.eu/events/workshop-on-sharing-data-from-molecular-simulations/). Here, we present a brief overview of this workshop and topics discussed. We hope this effort will spark further conversation in the MD community to pave the way toward more open, interoperable, and reproducible outputs coming from research studies using MD simulations.

There is great need for coordination around standards and best practices in neuroscience to support efforts to make neuroscience a data-centric discipline. Major brain initiatives launched around the world are poised to generate huge stores of neuroscience data. At the same time, neuroscience, like many domains in biomedicine, is confronting the issues of transparency, rigor, and reproducibility. Widely used, validated standards and best practices are key to addressing the challenges in both big and small data science, as they are essential for integrating diverse data and for developing a robust, effective, and sustainable infrastructure to support open and reproducible neuroscience. However, developing community standards and gaining their adoption is difficult. The current landscape is characterized both by a lack of robust, validated standards and a plethora of overlapping, underdeveloped, untested and underutilized standards and best practices. The International Neuroinformatics Coordinating Facility (INCF), an independent organization dedicated to promoting data sharing through the coordination of infrastructure and standards, has recently implemented a formal procedure for evaluating and endorsing community standards and best practices in support of the FAIR principles. By formally serving as a standards organization dedicated to open and FAIR neuroscience, INCF helps evaluate, promulgate, and coordinate standards and best practices across neuroscience. Here, we provide an overview of the process and discuss how neuroscience can benefit from having a dedicated standards body.

Alternative splicing (AS) enables differential inclusion of exons from a given transcript, thereby contributing to the transcriptome and proteome diversity. Aberrant AS patterns play major roles in the development of different pathologies, including breast cancer. N⁶-methyladenosine (m⁶A), the most abundant internal modification of eukaryotic mRNA, influences tumor progression and metastasis of breast cancer, and it has been recently linked to AS regulation. Here, we identify a specific AS signature associated with breast tumorigenesis in vitro. We characterize for the first time the role of METTL3 in modulating breast cancer-associated AS programs, expanding the role of the m⁶A-methyltransferase in tumorigenesis. Specifically, we find that both m⁶A deposition in splice site boundaries and in splicing and transcription factor transcripts, such as MYC, direct AS switches of specific breast cancer-associated transcripts. Finally, we show that five of the AS events validated in vitro are associated with a poor overall survival rate for patients with breast cancer, suggesting the use of these AS events as a novel potential prognostic biomarker.

When microbes compete for limited resources, they often engage in chemical warfare using bacterial toxins. This competition can be understood in terms of evolutionary game theory (EGT). We study the predictions of EGT for the bacterial "suicide bomber" game in terms of the phase portraits of population dynamics, for parameter combinations that cover all interesting games for two-players, and seven of the 38 possible phase portraits of the three-player game. We compare these predictions to simulations of these competitions in finite well-mixed populations, but also allowing for probabilistic rather than pure strategies, as well as Darwinian adaptation over tens of thousands of generations. We find that Darwinian evolution of probabilistic strategies stabilizes games of the rock-paper-scissors type that emerge for parameters describing realistic bacterial populations, and point to ways in which the population fixed point can be selected by changing those parameters. © 2012 American Physical Society.

The dimensionality of biological data is often very high. Feature selection can be used to tackle the problem of high dimensionality. However, majority of the work in feature selection consists of supervised feature selection methods which require class labels. The problem further escalates when the data is time-series gene expression measurements that measure the effect of external stimuli on biological system. In this paper we propose an unsupervised method for gene selection from time-series gene expression data founded on statistical significance testing and swap randomization. We perform experiments with a publicly available mouse gene expression dataset and also a human gene expression dataset describing the exposure to asbestos. The results in both datasets show a considerable decrease in number of genes.

The complete Pomatoschistus minutus mitochondrion genome. The voucher NRM:NRM69326 is stored at The Swedish Museum of Natural History, Stockholm, Sweden. The Genbank Accession number of the annotated sequence MW092827.

Low rates of allergy are observed in children growing up on a farm, but the factors that contribute to this protective effect are unclear. This study aimed to investigate if living on a farm alters the infant gut microbiota and thereby reduces the risk of developing allergic diseases in later childhood. This study was based on the prospective Farmflora cohort, which included 28 children living on a farm and 35 control children living in a rural area, who were followed from birth to 8 years of age. The gut microbiota was analyzed from 122 fecal samples collected from 48 children during the first 6 months after birth, using quantitative microbiome profiling. This novel method integrates 16S rRNA gene sequencing data with total bacterial load to obtain absolute counts of bacterial abundance. A significant increase in microbial diversity was seen in the gut microbiota of all the infants in the cohort over the first 6 months after birth. Similar alpha and beta diversity levels were observed in the farm and the control children. However, Sutterella, Megasphaera, and Dorea were more abundant in the gut microbiota of farm children. It has previously been observed that the farm environment was associated with low rates of allergy in children at 3 years. Taxa Akkermansia was more abundant in the gut microbiota of infants who were evaluated with allergy at 3 years. In addition, children who were healthy at 8 years had a higher abundance of Bifidobacterium in their gut microbiota at 6 months of age. However, the abundance of Bifidobacterium could not be linked to farm residence in this study. The findings were consistent with previous studies which link the higher abundance of Sutterella, Dorea, and Megasphaera with protection against allergic diseases. In conclusion, the study observed differences in the gut microbiota of children growing up on a farm, who have low rates of allergy, and showed Bifidobacterium may be protective against allergy development.

Forensic statistics is a well-established scientific field whose purpose is to statistically analyze evidence in order to support legal decisions. It traditionally relies on methods that assume small numbers of independent variables and multiple samples. Unfortunately, such methods are less applicable when dealing with highly correlated multivariate data sets such as those generated by emerging high throughput analytical technologies. Chemometrics is a field that has a wealth of methods for the analysis of such complex data sets, so it would be desirable to combine the two fields in order to identify best practices for forensic statistics in the future. This paper provides a brief introduction to forensic statistics and describes how chemometrics could be integrated with its established methods to improve the evaluation of evidence in court.

The paper describes how statistics and chemometrics can be integrated, by analyzing a previous know forensic data set composed of bacterial communities from fingerprints. The presented strategy can be applied in cases where chemical and biological threat agents have been illegally disposed.

The project highlights Salmonella enterica subspecies enterica serovar Typhimurium (S. Tm)'s ability to metabolize simple sugars released from dead commensal bacteria, by using the p_BAD (araBAD promoter) system as a reporter of L-arabinose availability. Using bioinformatics and homology of conserved L-arabinose transporter genes shared in Escherichia coli K12 (E. coli) and S. Tm, we aimed to create a S. Tm mutant strain unable to obtain L-arabinose from it environment. During the projects course of time it was discovered that L-arabinose transporters are not a shared gene trait between E. coli and S. Tm, and that putative L-arabinose transporter orthologues may exists in the S. Tm genome.

Sepsis is a dangerous and potentially fatal condition that has a mysterious origin, underscoring the significance of prompt and accurate diagnosis and treatment. Bacterial whole-genome sequencing, which is widely used in clinical microbiology, stands at the forefront of sequencing technologies, particularly to combat sepsis. The aim of this thesis is to improve sepsis treatment by examining the genetic characteristics and drug resistance patterns of the common sepsis-causing bacteria Pseudomonas and Proteus spp., by analyzing the whole-genome sequencing data of bacterial isolates using an in-house-developed pipeline. The result was compared with a commercial cloud-based platform from 1928 Diagnostic (Gothenburg, Sweden), as well as the results from a clinical laboratory. Using Illumina HiSeq X next-generation sequencing technology, whole-genome data from 88 isolates of Pseudomonas and Proteus spp. was obtained. The isolates were obtained during a prospective observational study of community-onset severe sepsis and septic shock in adults at Skaraborg Hospital in Sweden's western region. The collected isolates were characterized using approved laboratory techniques, such as phenotypic antibiotic susceptibility testing (AST) in accordance with EUCAST guidelines and species identification by MALDI-TOF MS analysis. The species identification result matched the phenotypic method, with the exception of two isolates from Pseudomonas samples and four isolates from Proteus samples. When benchmarking the in-house pipeline and 1928 platform for Pseudomonas spp., predicted 97% of the isolates were resistant to at least one class of the tested antibiotics, of which 94% shows multi-drug resistance. In phenotypes, 88% of the isolates had at least one antibiotic resistance future, of which 68% shows multi-drug resistance. The most prevalent sequence types (STs) identified were ST 3285 and ST111 (9.3%) and ST564 and ST17 (6.98%) each, and both pipelines accurately predicted the number of multilocus types. The in-house pipeline reported 9820 Pseudomonas virulence genes, with PhzB1, a metabolic factor, being the most common gene. It was discovered that there was a significant correlation between the virulence factor gene count and the multilocus sequence typing (MLST) (p = 0.00001). With a Simpson's Diversity Index of 0.98, the urine culture specimens showed the greatest ST diversity. Plasmids were detected in twelve samples (20.93%) in total. In general, this study provided a detailed description of the bacterial future for Pseudomonas and Proteus organisms using WGS data. This research shows the applicability of the in-house and 1928 pipelines in the identification of sepsis-causing organisms with accuracy. It also showed the need for an organized and easy-to-use international pipeline to implement and analyze WGS bacterial data and to compare it with laboratory results as needed.

Insertions/deletions (indels) are potentially powerful evolutionary markers, but little is known about their evolution and few tools exist to effectively study them. To address this, I developed SeqFIRE, a tool for automated identification and extraction of indels from protein multiple sequence alignments. The program also extracts conserved alignment blocks, thus covering all major steps in preparing multiple sequence alignments for phylogenetic analysis.

I then used SeqFIRE to build an indel database, using 299 single copy proteins from a broad taxonomic sampling of mainly multicellular eukaryotes. A total of 4,707 indels were extracted, of which 901 are simple (one genetic event) and 3,806 are complex (multiple events). The most abundant indels are single amino acid simple indels. Indel frequency decreases exponentially with length and shows a linear relationship with host protein size. Singleton indels reveal a strong bias towards insertions (2.31 x deletions on average). These analyses also identify 43 indels marking major clades in Plantae and Fungi (clade defining indels or CDIs), but none for Metazoa.

In order to study the 3806 complex indels they were first classified by number of states. Analysis of the 2-state complex and simple indels combined (“bi-state indels”) confirms that insertions are over 2.5 times as frequent as deletions. Three-quarters of the complex indels had three-nine states (“slightly complex indels”). A tree-assisted search method was developed allowing me to identify 1,010 potential CDIs supporting all examined major branches of Plantae and Fungi.

Forty-two proteins were also found to host complex indel CDIs for the deepest branches of Metazoa. After expanding the taxon set for these proteins, I identified a total of 49 non-bilaterian specific CDIs. Parsimony analysis of these indels places Ctenophora as sister taxon to all other Metazoa including Porifera. Six CDIs were also found placing Placozoa as sister to Bilateria. I conclude that slightly complex indels are a rich source of CDIs, and my tree-assisted search strategy could be automated and implemented in the program SeqFIRE to facilitate their discovery. This will have important implications for mining the phylogenomic content of the vast resource of protist genome data soon to become available.

Analyses of multiple sequence alignments generally focus on well-defined conserved sequence blocks, while the rest of the alignment is largely ignored or discarded. This is especially true in phylogenomics, where large multigene datasets are produced through automated pipelines. However, some of the most powerful phylogenetic markers have been found in the variable length regions of multiple alignments, particularly insertions/deletions (indels) in protein sequences. We have developed Sequence Feature and Indel Region Extractor (SeqFIRE) to enable the automated identification and extraction of indels from protein sequence alignments. The program can also extract conserved blocks and identify fast evolving sites using a combination of conservation and entropy. All major variables can be adjusted by the user, allowing them to identify the sets of variables most suited to a particular analysis or dataset. Thus, all major tasks in preparing an alignment for further analysis are combined in a single flexible and user-friendly program. The output includes a numbered list of indels, alignments in NEXUS format with indels annotated or removed and indel-only matrices. SeqFIRE is a user-friendly web application, freely available online at www.seqfire.org/.

All seed plants go through a juvenile period before they initiate seed- and pollen-bearing organs and reproduce. Reproductive phase-change is well characterized in angiosperm model species, but much less well described in gymnosperms. Here, we utilize the early cone-setting acrocona mutant to study reproductive phase change in the conifer Picea abies; a representative of the gymnosperm lineage. The acrocona mutant frequently initiates cone-like structures, called transition shoots, in positions where wild-type P. abies always produces vegetative shoots. By sequence analysis of mRNA and microRNA transcripts, we demonstrate that orthologous components of the Age-dependent flowering pathway are active at the time of cone initiation. We show that a member of the SQUAMOSA BINDING PROTEIN-LIKE (SPL) gene family, PaSPL7, is active in reproductive meristems, whereas a putative negative regulator of PaSPL7, microRNA156 is upregulated in vegetative meristem. By allele-specific assembly, we also identify a short nucleotide polymorphism (SNP) in the miRNA156 binding of PaSPL7. By genotyping a segregating population of inbred acrocona trees, we show a clear co-segregation between the early cone-setting phenotype and trees homozygous for the SNP. Hence, the data presented demonstrate evolutionary conservation of the age-dependent flowering pathway and involvement of this pathway in regulating cone-setting in the conifer P. abies.

Identifying the evolutionary root of eukaryotic tree of life (eToL) is a central problem in systematic biology that has been receiving growing attention. This task has been aided by the development of advanced phylogenetic methods and the availability of large amounts of genomic data from across the tree. Recently, two studies have tried a novel approach to define the eToL root, using euBacteria (instead of the more distantly related Archaea) as the outgroup. The results of these two recent studies are partially overlapping datasets, which produce contradictory results. One study, using mixed eubacterial data (euBac), makes the case for a neozoan-excavate root, while the other study, using alpha-proteobacterial (aP) data, concluded the traditional unikont-bikont root. These two results suggest different theories of early eukaryote evolution. However, there is also evidence of substantial artefacts in these datasets and traces of horizontal gene transfer (HGT), the exchange of DNA between unrelated organisms. This project aims to re-examine the datasets of both publications (61 total protein markers). The work started with updating both datasets with solid new phylogenomic data from the supervisor lab and new publicly available data. I then used these data to systematically investigate the phylogenetic signals of the 61 protein markers across 88 taxa (68 eukaryotes and 20 Bacteria). These were first subjected to preliminary phylogenetic analyses to sort orthologues from paralogues. All orthologues were then combined into a single dataset and subjected to in depth phylogenetic analyses to evaluate the support for various hypotheses. I also investigated potential sources of artefact in the data using traditional and novel methods I devised and developed myself including computer scripts specifically written for this work. I created a pipeline for the data curation process to make it fast and efficient by automating various parts of the workflow, including concatenating the multigene dataset into a super matrix. I estimated the level of incongruence in each dataset, excluded the protein markers that have a strong phylogenetic bias, and reconstructed new datasets. I conclude that the data in hand (protein markers and taxa) contain conflicting and inconsistent phylogenetic signal and that a few proteins can have a very strong effect on the results of the analyses. However, a third possible hypothesis is clearly rejected. This suggests that there are specific artefacts in the data, favouring one or the other of the two remaining hypotheses.

Pancreatic Ductal Adenocarcinomas initiates in the exocrine part of the pancreatic tissue and represents over 90% of all the pancreatic cancers. Pancreatic Ductal Adenocarcinomas are extremely aggressive and are one of the most lethal malignant neoplasms. The five-year relative survival is currently less than 8% of the patients. The main reason behind such a low survival rate is that most of the cases are diagnosed at a very late stage. Although substantial advancement in pancreatic cancer research has been done, there has not been any remarkable significance in the mortality to incidence ratio. This is mainly a result of the scarce of early diagnostic characteristic symptoms and reliable biomarkers besides the unresponsiveness to the treatments. In this study, transcriptomics and proteomics data were used for the construction of a genome-scale metabolic model that was used in the detection of altered metabolic pathways, genes and metabolites using gene set analysis and reporter metabolites analysis. As a result, altered metabolic pathways in PDAC tumours were detected, including the lipid metabolism-related pathways as well as carbohydrate metabolism, in addition to nucleotide metabolism, which are considered as potential candidates for diagnostic biomarkers. Moreover, classification of the filtered DIRAC tightly regulated network genes, based on their prognostic values from the pathology atlas, detected two groups of PDAC patients that have significantly different survival outcome. The differential expression analysis of the two groups showed that six of the eight genes used in clustering were showing significantly altered expression, which suggests their importance in PDAC patient stratification. As a conclusion, this study shows the valuable outcome of the GEM reconstructions and other systems-level analyses for elucidating the underlying altered metabolic mechanisms of PDAC. Such analyses results should provide more insights into the biomarker discovery and developing of potential treatments.

 The analysis of kinship from ancient DNA (aDNA) data has the potential to provide insight into social structures of prehistoric societies. Kinship analysis is gaining popularity as optimised wet-lab methods allow for studies with sample sizes on the level of whole cemeteries. However, the specifics of ancient DNA require different methods than what would be used for modern DNA. A common way is to use the sites that are identical-bydescent (IBD), however, detecting these is often a challenging task since it is not easy to determine whether a shared locus between two individuals is inherited from the ancestor or if another factor caused the similarity. Most methods used in the field are able to identify up to 2nd or 3rd degree relatives from aDNA data but do not distinguish between different types of relationship for the same degree, for instance not being able to differentiate between parentoffspring and full sibling-sibling relationship in first degree. The aDNA kinship methods often use either of window-based or single-site approaches, however, these two approaches have not been compared formally before in terms of effectivity and efficiency. In this work, READv2 is presented as a re-implementation of a popular kinship analysis method for aDNA studies with additional features such as accepting .bed files as input, which take up less space than the previous input type, plain-text .tped files. It is shown that the new version works more efficiently in terms of runtime. However, the memory requirements seem to be increased with the new implementation. Furthermore, a window-based approach is compared with the single-site approach of READv2, as well as varying window sizes, with benchmarked simulation data which contains approximately 700 individuals with known 1st degree, 2nd degree and 3rd degree relationships. According to the comparison, the sensitivity of the method does not vary between the approaches and different window sizes for high coverages. However, the single-site approach has been shown to be the superior one by a small margin for lower coverages. In addition to these, using the variance of non-shared alleles in windows along the genome has been used to implement a method to differentiate different first-degree relationships, parent-offspring and siblings. The method is tested with an independent dataset from the 1000 Genomes Project which shows that the proposed method is able to work with different datasets with varying sets of SNPs. Nevertheless, the first-degree classification method requires further analyses to determine the stress-point where the True Positive rates for both categories start to drop. Additionally, some necessary changes and decisions are required for READv2 to be a user-friendly method that can be used by other researchers. The preliminary release of READv2, including example data as well as instructions to install the necessary packages and to run the algorithm can be found in https://github.com/GuntherLab/READv2/releases/tag/READ. 

Sepsis is a life-threatening organ dysfunction that is caused by a dysregulated host response to infection. Sepsis is a substantial health care and economic burden worldwide and is one of the most common reasons for admission to the hospital and intensive care unit. Early diagnosis and targeted treatment of sepsis are the bases to reduce the mortality and morbidity. Conventional blood culturing is the gold standard method for sepsis diagnostics. However, blood culturing is a time consuming method, requiring at least 48 to 72 hours to get the first results with very low sensitivity and specificity. The aim of this study was to determine and assess the direct sepsis-related costs for PCR-based diagnostic strategies (SeptiFast and POC/LAB). A mathematical model was constructed to compare PCR-based diagnostic strategies with the conventional blood culturing. Three case scenarios were investigated based on data from the United Kingdom, Spain and the Czech Republic. It was found that, POC/LAB was the most cost effective strategy in all countries if it could reduce the hospitalization length of stay with at least 3 days in the normal hospital ward and 1 day in the intensive care unit. Reducing the hospitalization length of stay had the greatest impact on the economic outcomes. While, reducing the costs of the diagnostic strategies did not show a remarkable effect on the economic results. In conclusion, the findings suggest that PCR-rapid diagnostic methods could be cost-effective for the diagnosis of patients with sepsis if they could reduce the hospitalization length of stay.

Common variable immunodeficiency (CVID) is a primary immune deficiency present in about 1 in 25,000 people, characterized by recurrent infections, low serum immunoglobulin (Ig) levels (IgG, IgA, and sometimes IgM), and reduced vaccine responses. It is genetically a heterogeneous illness that often affects adults or teenagers and requires lifetime clinical care. CVID patients experience recurrent or chronic sinopulmonary tract infections, gastrointestinal disorders, and malignancies. Ig reconstitution administered intravenously or subcutaneously is the main treatment. Although the fundamental causes of CVID are still undefined, studies suggest that a variety of variables, including impaired somatic hypermutation (SHM), B cell maturation, primary B cell dysfunctions, abnormalities in T cells, and antigen-presenting cells are implicated. Understanding the molecular mechanisms driving this disease's genome regulation requires a deep understanding of the gene expression. It is today possible to study both coding and non-coding sections of RNA using next-generation RNA-seq, which allows detecting differentially expressed genes in massive amounts of data, particularly in multifaceted illnesses like CVID. The aim of this study was to identify the differentially expressed genes between unstimulated (ex vivo) and stimulated (in vitro) naïve B cells of CVID patients and healthy donors (HD), and also to identify the underlying biological processes by gene enrichment analysis. The results of this study showed that both in CVID and HD, the stimulated and unstimulated cells were well separated. In the gene set analysis, it was discovered that significantly enriched CVID pathways were mostly involved in immune system-related processes such as adaptive immune response, cytoplasmic translation, granulocyte activation, lymphocyte activation, and lymphocyte differentiation. Therefore, the transcriptomic analysis of this study concluded that the majority of the genes that regulate the immune cell activation process function may have a greater impact on CVID patients than on HD which helps to understand the immunological defects in CVID patients. 

The multifunctional purpose of hepatocytes, the functional liver cells within the metabolic, endocrine and secretory functions highlights key importance in emphasizing the research and treatment methods that utilize these cells. Forming 80% of the liver's cells, hepatocytes are involved in many of the primary functions of the liver including the delivery of immune response against pathogens and aiding in the detoxification of drugs. As a result, it provides a valuable basis for medical research. Through the findings of Ghosheh et al. (2017), a method of generating mature hepatocytes was achieved through the human pluripotent stem cells (HPSC), but the generation of hepatocytes in which all the genes are expressed at the right amount through this method proves to be a difficult endeavor. The primary goal of this project is to utilize the established findings to enhance and improve the efficacy of the process that goes behind the generation of mature hepatocytes. The approach towards the current project was initiated with culturing and differentiating three human embryonic stem cell lines and three human-induced pluripotent stem cell lines into mature hepatocytes. In the study mentioned, k-means clustering along with Pearson correlation as the distant measure was run in R to subdivide the top 2000 genes with the highest differential expression into 10 clusters. The cluster data from this paper was used to do the current study, in which the up-regulated and down-regulated gene were first identified for clusters 2, 4 & 6 and 9. The interactions of up-regulated genes in these clusters were further analyzed using Enrichr to identify the different miRNAs for various genes from the clusters. Within cluster 2, a total of 8 genes showed the possibility of being regulated using 4 miRNAs. Transcription factors were also identified for cluster 2 and a combination of HNF1A, EP300, AHR, NFKB1 and HIF1A could repress 8 genes that were not repressed by miRNAs. In cluster 4 & 6, most of the up-regulated genes showcased tumorigenicity and all 20 genes identified could be regulated with the combination of 7 miRNAs. In cluster 9, a combination of 11 miRNAscould be used to regulate 26 out of 27 genes that were analyzed. Ensuring that stem cell products do not turn cancerous is a priority in the medical field. Conducting the analyses of the other clusters aside from 2, 4 & 6 and 9 will prove highly beneficial in reducing the risks pertaining to stem cell mutation due to overexpression of genes.

FunCoup (http://FunCoup.sbc.su.se) is a database that maintains and visualizes global gene/protein networks of functional coupling that have been constructed by Bayesian integration of diverse high-throughput data. FunCoup achieves high coverage by orthology-based integration of data sources from different model organisms and from different platforms. We here present release 2.0 in which the data sources have been updated and the methodology has been refined. It contains a new data type Genetic Interaction, and three new species: chicken, dog and zebra fish. As FunCoup extensively transfers functional coupling information between species, the new input datasets have considerably improved both coverage and quality of the networks. The number of high-confidence network links has increased dramatically. For instance, the human network has more than eight times as many links above confidence 0.5 as the previous release. FunCoup provides facilities for analysing the conservation of subnetworks in multiple species. We here explain how to do comparative interactomics on the FunCoup website.

This thesis provides an in-depth analyze of expressed sequence tags (EST) that represent pieces of eukaryotic mRNA by using unsupervised hidden Markov model (HMM). ESTs are short nucleotide sequences that are used primarily for rapid identificationof new genes with potential coding regions (CDS). ESTs are made by sequencing on double-stranded cDNA and the synthesizedESTs are stored in digital form, usually in FASTA format. Since sequencing is often randomized and that parts of mRNA contain non-coding regions, some ESTs will not represent CDS.It is desired to remove these unwanted ESTs if the purpose is to identifygenes associated with CDS. Application of stochastic HMM allow identification of region contents in a EST. Softwares like ESTScanuse HMM in which a training of the HMM is done by supervised learning with annotated data. However, because there are not always annotated data at hand this thesis focus on the ability to train an HMM with unsupervised learning on data containing ESTs, both with and without CDS. But the data used for training is not annotated, i.e. the regions that an EST consists of are unknown. In this thesis a new HMM is introduced where the parameters of the HMM are in focus so that they are reasonablyconsistent with biologically important regionsof an mRNA such as the Kozak sequence, poly(A)-signals and poly(A)-tails to guide the training and decoding correctly with ESTs to proper statesin the HMM. Transition probabilities in the HMMhas been adapted so that it represents the mean length and distribution of the different regions in mRNA. Testing of the HMM's specificity and sensitivityhave been performed via BLAST by blasting each EST and compare the BLAST results with the HMM prediction results.A regression analysis test shows that the length of ESTs used when training the HMM is significantly important, the longer the better. The final resultsshows that it is possible to train an HMM with unsupervised machine learning but to be comparable to supervised machine learning as ESTScan, further expansion of the HMM is necessary such as frame-shift correction of ESTs byimproving the HMM's ability to choose correctly positioned start codons or nucleotides. Usually the false positive results are because of incorrectly positioned start codons leadingto too short CDS lengths. Since no frame-shift correction is implemented, short predicted CDS lengths are not acceptable and is hence not counted as coding regionsduring prediction. However, when there is a lack of supervised models then unsupervised HMM is a potential replacement with stable performance and able to be adapted forany eukaryotic organism.

 Convergence assessment and burnin estimation are central concepts in Markov chain Monte Carlo algorithms. Studies on eects, statistical properties, and comparisons between dierent convergence assessment methods have been conducted during the past few decades. However, not much work has been done on the eect of convergence diagnostic on posterior distribution of tree parameters and which method should be used by researchers in Bayesian phylogenetics inference. In this study, we propose and evaluate two novel burnin estimation methods that estimate burnin using all parameters jointly. We also consider some other popular convergence diagnostics, evaluate them in light of parallel chains and quantify the eect of burnin estimates from various convergence diagnostics on the posterior distribution of trees. We motivate the use of convergence diagnostics to assess convergence and estimate burnin in Bayesian phylogenetics inference and found out that it is better to employ convergence diagnostics rather than remove a xed percentage as burnin. We concluded that the last burnin estimator using eective sample size appears to estimate burnin better than all other convergence diagnostics.

Motivation: MCMC-based methods are important for Bayesian inference of phylogeny and related parameters. Although being computationally expensive, MCMC yields estimates of posterior distributions that are useful for estimating parameter values and are easy to use in subsequent analysis. There are, however, sometimes practical diculties with MCMC, relating to convergence assessment and determining burn-in, especially in large-scale analyses. Currently, multiple software are required to perform, e.g., convergence, mixing and interactive exploration of both continuous and tree parameters.

Results: We have written a software called VMCMC to simplify post-processing of MCMC traces with, for example, automatic burn-in estimation. VMCMC can also be used both as a GUI-based application, supporting interactive exploration, and as a command-line tool suitable for automated pipelines.

Availability: VMCMC is available for Java SE 6+ under the New BSD License. Executable jar les, tutorial manual and source code can be downloaded from https://bitbucket.org/rhali/visualmcmc/.

Background: Homology inference is pivotal to evolutionary biology and is primarily based on significant sequence similarity, which, in general, is a good indicator of homology. Algorithms have also been designed to utilize conservation in gene order as an indication of homologous regions. We have developed GenFamClust, a method based on quantification of both gene order conservation and sequence similarity. Results: In this study, we validate GenFamClust by comparing it to well known homology inference algorithms on a synthetic dataset. We applied several popular clustering algorithms on homologs inferred by GenFamClust and other algorithms on a metazoan dataset and studied the outcomes. Accuracy, similarity, dependence, and other characteristics were investigated for gene families yielded by the clustering algorithms. GenFamClust was also applied to genes from a set of complete fungal genomes and gene families were inferred using clustering. The resulting gene families were compared with a manually curated gold standard of pillars from the Yeast Gene Order Browser. We found that the gene-order component of GenFamClust is simple, yet biologically realistic, and captures local synteny information for homologs. Conclusions: The study shows that GenFamClust is a more accurate, informed, and comprehensive pipeline to infer homologs and gene families than other commonly used homology and gene-family inference methods.

Sepsis is defined as a syndrome of life-threatening organ dysfunction caused by a dysregulated host response to an infection. Early detection of sepsis and immediate treatment with antibiotics is critical for patient outcomes. Haemophilus influenzae (H. influenzae) is a gram-negative bacteria known to be a human-adapted pathogen that may cause a variety of communityacquired infections such as sepsis. A rapid increase in beta-lactam resistance in H. influenzae has been noticed and has become a major problem in clinical care. By implementing bacterial whole genome sequencing (WGS) in the clinical laboratory, it can provide a great amount of information such as species identification, serotype identification, antimicrobial resistance prediction, typing for epidemiologic purposes and tracking infectious disease outbreaks. The aim of this study was to analyze WGS data for clinical H. influenzae isolates using an in-house developed bioinformatic pipeline and an automated 1928 Diagnostics platform to evaluate and compare the predicted results in terms of species identification, prediction of resistance and virulence genes. Furthermore, the predicted genotypic antibiotic resistance genes were compared to the phenotypic antimicrobial susceptibility testing obtained from the clinical laboratory. For the in-house developed pipeline, the analysis of H. influenzae WGS data started with quality control and preprocessing (trimming) of FASTQ files. Following, de novo assembly and quality assessment of assembled contigs and lastly gene annotation tools were performed. For 1928 Diagnostics, the untrimmed FASTQ files were uploaded to the 1928 platform. Species identification resulted in a high agreement of predicted H. influenzae for both phenotypic and genotypic methods except for one sample that may have been contaminated. The analysis of antibiotic resistance genes resulted in both in-house developed pipeline and 1928 Diagnostics having a high agreement regarding the prediction of broad-spectrum beta-lactamase in six clinical isolates, all of which predicted bla TEM-1B. The four most common sequence types found in the MLST analysis from the in-house pipeline were ST159, ST388, ST14 and ST12. The analysis of virulence genes yielded a large number of different virulence genes and each of the identified virulence genes codes a specific function that is crucial to the pathogenesis of H. influenzae. In conclusion, the obtained results provide valuable insights into using WGS-based analysis as a reliable tool for determining the pathogen characteristics in clinical bacterial isolates.

Background: Knowledge within organizations can play a vital role for organizational development. The role of sharing knowledge in public organizations by means of the use of information systems have not been studied to a larger extent. During 2016 the thirteen municipalities within Region Jönköping’s län adhered to a so-called digital agenda to develop the municipal organizations and service delivery. One of the goals of the digital agenda was to increase knowledge sharing by digital means between municipalities. 

Purpose: The purpose of the thesis was to investigate how knowledge sharing practices taking place between municipalities in region Jönköping’s län. The authors performed a pilot case study in the educational department within three municipalities. 

Method: This study based on qualitative research and data were gathered through semi-structured interviews and analyzed according to the conventional content analysis. Semi-structured interviews were performed based on the theoretical frameworks of Nonaka’s Model of Knowledge Management, which resulted in an interview guide with open-ended questions. Conventional content was used for qualitative data analysis. 

Conclusion: According to our analysis we have found that knowledge sharing in public organization is generally seen as one of the most important elements that should be wisely managed. Collaboration in public sector basically depend on the so many things and it starts with the government initiative but ends with public awareness. It is also very important that organizations can manage knowledge resources more successfully if employees are willingly to share their knowledge with colleagues. People of organizations are quite comfortable with collaborative technologies because the advance of the internet and related technologies. In the public sector worker or employees should motivated, get more encouragement and support by the leaders. 

Robust detection and localization of biomolecules inside cells is of great importance to better understand the functions related to them. Fluorescence microscopy and specific staining methods make biomolecules appear as point-like signals on image data, often acquired in 3D. Visual detection of such point-like signals can be time consuming and problematic if the 3D images are large, containing many, sometimes overlapping, signals. This sets a demand for robust automated methods for accurate detection of signals in 3D fluorescence microscopy. We propose a new 3D point-source signal detection method that is based on Fourier series. The method consists of two parts, a detector, which is a cosine filter to enhance the point-like signals, and a verifier, which is a sine filter to validate the result from the detector. Compared to conventional methods, our method shows better robustness to noise and good ability to resolve signals that are spatially close. Tests on image data show that the method has equivalent accuracy in signal detection in comparison to Visual detection by experts. The proposed method can be used as an efficient point-like signal detection tool for various types of biological 3D image data.

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug–omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug–drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.

The advent of machine learning-based structure prediction algorithms such as AlphaFold2 (AF2) and RoseTTa Fold have moved the generation of accurate structural models for the entire cellular protein machinery into the reach of the scientific community. However, structure predictions of protein complexes are based on user-provided input and may require experimental validation. Mass spectrometry (MS) is a versatile, time-effective tool that provides information on post-translational modifications, ligand interactions, conformational changes, and higher-order oligomerization. Using three protein systems, we show that native MS experiments can uncover structural features of ligand interactions, homology models, and point mutations that are undetectable by AF2 alone. We conclude that machine learning can be complemented with MS to yield more accurate structural models on a small and large scale.

Microbial organisms are a vital part of our global ecosystem. Yet, our knowledge of them is still lacking. Direct sequencing of microbial communities, i.e. metagenomics, have enabled detailed studies of these microscopic organisms by inspection of their DNA sequences without the need to culture them. Furthermore, the development of modern high- throughput sequencing technologies have made this approach more powerful and cost-effective. Taken together, this has shifted the field of microbiology from previously being centered around microscopy and culturing studies, to largely consist of computational analyses of DNA sequences. One such computational analysis which is the main focus of this thesis, aims at reconstruction of the complete DNA sequence of an organism, i.e. its genome, directly from short metagenomic sequences.

This thesis consists of an introduction to the subject followed by five papers. Paper I describes a large metagenomic data resource spanning the Baltic Sea microbial communities. This dataset is complemented with a web-interface allowing researchers to easily extract and visualize detailed information. Paper II introduces a bioinformatic method which is able to reconstruct genomes from metagenomic data. This method, which is termed CONCOCT, is applied on Baltic Sea metagenomics data in Paper III and Paper V. This enabled the reconstruction of a large number of genomes. Analysis of these genomes in Paper III led to the proposal of, and evidence for, a global brackish microbiome. Paper IV presents a comparison between genomes reconstructed from metagenomes with single-cell sequenced genomes. This further validated the technique presented in Paper II as it was found to produce larger and more complete genomes than single-cell sequencing.

The genome encodes the metabolic and functional capabilities of an organism and should be a major determinant of its ecological niche. Yet, it is unknown if the niche can be predicted directly from the genome. Here, we conduct metagenomic binning on 123 water samples spanning major environmental gradients of the Baltic Sea. The resulting 1961 metagenome-assembled genomes represent 352 species-level clusters that correspond to 1/3 of the metagenome sequences of the prokaryotic size-fraction. By using machine-learning, the placement of a genome cluster along various niche gradients (salinity level, depth, size-fraction) could be predicted based solely on its functional genes. The same approach predicted the genomes’ placement in a virtual niche-space that captures the highest variation in distribution patterns. The predictions generally outperformed those inferred from phylogenetic information. Our study demonstrates a strong link between genome and ecological niche and provides a conceptual framework for predictive ecology based on genomic data.

Aquatic microorganism are key drivers of global biogeochemical cycles and form the basis of aquatic food webs. However, there is still much left to be learned about these organisms and their interaction within specific environments, such as the Baltic Sea. Crucial information for such an understanding can be found within the genome sequences of organisms within the microbial community.

In this study, the previous set of Baltic Sea clusters, constructed by Hugert et al., is greatly expanded using a large set of metagenomic samples, spanning the environmental gradients of the Baltic Sea. In total, 124 samples were individually assembled and binned to obtain 2,032 Metagenome Assembled Genomes (MAGs), clustered into 353 prokaryotic and 14 eukaryotic species- level clusters. The prokaryotic genomes were widely distributed over the prokaryotic tree of life, representing 20 different phyla, while the eukaryotic genomes were mostly limited to the division of Chlorophyta. The large number of reconstructed genomes allowed us to identify key factors determining the quality of the genome reconstructions.

The Baltic Sea is heavily influenced of human activities of which we might not see the full implications. The genomes reported within this study will greatly aid further studies in our strive for an understanding of the Baltic Sea microbial ecosystem.

Background: Prokaryotes dominate the biosphere and regulate biogeochemical processes essential to all life. Yet, our knowledge about their biology is for the most part limited to the minority that has been successfully cultured. Molecular techniques now allow for obtaining genome sequences of uncultivated prokaryotic taxa, facilitating in-depth analyses that may ultimately improve our understanding of these key organisms.

Results: We compared results from two culture-independent strategies for recovering bacterial genomes: single-amplified genomes and metagenome-assembled genomes. Single-amplified genomes were obtained from samples collected at an offshore station in the Baltic Sea Proper and compared to previously obtained metagenome-assembled genomes from a time series at the same station. Among 16 single-amplified genomes analyzed, seven were found to match metagenome-assembled genomes, affiliated with a diverse set of taxa. Notably, genome pairs between the two approaches were nearly identical (average 99.51% sequence identity; range 98.77-99.84%) across overlapping regions (30-80% of each genome). Within matching pairs, the single-amplified genomes were consistently smaller and less complete, whereas the genetic functional profiles were maintained. For the metagenome-assembled genomes, only on average 3.6% of the bases were estimated to be missing from the genomes due to wrongly binned contigs.

Conclusions: The strong agreement between the single-amplified and metagenome-assembled genomes emphasizes that both methods generate accurate genome information from uncultivated bacteria. Importantly, this implies that the research questions and the available resources are allowed to determine the selection of genomics approach for microbiome studies.

Background: Prokaryotes dominate the biosphere and regulate biogeochemical processes essential to all life. Yet, our knowledge about their biology is for the most part limited to the minority that has been successfully cultured. Molecular techniques now allow for obtaining genome sequences of uncultivated prokaryotic taxa, facilitating in-depth analyses that may ultimately improve our understanding of these key organisms.

Results: We compared results from two culture-independent strategies for recovering bacterial genomes: single-amplified genomes and metagenome-assembled genomes. Single-amplified genomes were obtained from samples collected at an offshore station in the Baltic Sea Proper and compared to previously obtained metagenome-assembled genomes from a time series at the same station. Among 16 single-amplified genomes analyzed, seven were found to match metagenome-assembled genomes, affiliated with a diverse set of taxa. Notably, genome pairs between the two approaches were nearly identical (>98.7% identity) across overlapping regions (30-80% of each genome). Within matching pairs, the single-amplified genomes were consistently smaller and less complete, whereas the genetic functional profiles were maintained. For the metagenome-assembled genomes, only on average 3.6% of the bases were estimated to be missing from the genomes due to wrongly binned contigs; the metagenome assembly was found to cause incompleteness to a higher degree than the binning procedure.

Conclusions: The strong agreement between the single-amplified and metagenome-assembled genomes emphasizes that both methods generate accurate genome information from uncultivated bacteria. Importantly, this implies that the research questions and the available resources are allowed to determine the selection of genomics approach for microbiome studies.

The Baltic Sea is one of the world’s largest brackish water bodies and is characterised by pronounced physicochemical gradients where microbes are the main biogeochemical catalysts. Meta-omic methods provide rich information on the composition of, and activities within microbial ecosystems, but are computationally heavy to perform. We here present the BAltic Sea Reference Metagenome (BARM), complete with annotated genes to facilitate further studies with much less computational effort. The assembly is constructed using 2.6 billion metagenomic reads from 81 water samples, spanning both spatial and temporal dimensions, and contains 6.8 million genes that have been annotated for function and taxonomy. The assembly is useful as a reference, facilitating taxonomic and functional annotation of additional samples by simply mapping their reads against the assembly. This capability is demonstrated by the successful mapping and annotation of 24 external samples. In addition, we present a public web interface, BalticMicrobeDB, for interactive exploratory analysis of the dataset.

The Baltic Sea is one of the world's largest brackish water bodies and is characterised by pronounced physicochemical gradients where microbes are the main biogeochemical catalysts. Meta-omic methods provide rich information on the composition of, and activities within, microbial ecosystems, but are computationally heavy to perform. We here present the Baltic Sea Reference Metagenome (BARM), complete with annotated genes to facilitate further studies with much less computational effort. The assembly is constructed using 2.6 billion metagenomic reads from 81 water samples, spanning both spatial and temporal dimensions, and contains 6.8 million genes that have been annotated for function and taxonomy. The assembly is useful as a reference, facilitating taxonomic and functional annotation of additional samples by simply mapping their reads against the assembly. This capability is demonstrated by the successful mapping and annotation of 24 external samples. In addition, we present a public web interface, BalticMicrobeDB, for interactive exploratory analysis of the dataset. [GRAPHICS] .

The Baltic Sea is one of the world's largest brackish water bodies and is characterised by pronounced physicochemical gradients where microbes are the main biogeochemical catalysts. Meta-omic methods provide rich information on the composition of, and activities within, microbial ecosystems, but are computationally heavy to perform. We here present the Baltic Sea Reference Metagenome (BARM), complete with annotated genes to facilitate further studies with much less computational effort. The assembly is constructed using 2.6 billion metagenomic reads from 81 water samples, spanning both spatial and temporal dimensions, and contains 6.8 million genes that have been annotated for function and taxonomy. The assembly is useful as a reference, facilitating taxonomic and functional annotation of additional samples by simply mapping their reads against the assembly. This capability is demonstrated by the successful mapping and annotation of 24 external samples. In addition, we present a public web interface, BalticMicrobeDB, for interactive exploratory analysis of the dataset.

Neoehrlichia mikurensis is a bacterium transmitted to humans by tick bites, usually from Ixodes ricinus species, causing an infection known as neoehrlichiosis. The importance of B cells in host defence against this infection has previously been demonstrated in patients undergoing anti-B cell therapy for lymphoma. As a causal relationship between N. mikurensis and malignant B-cell lymphomas has not yet been established, the identification of a genetic signature caused by the infection would be a step forward in the development of therapeutics. Findings have been published on the heavy chain repertoire of the major B-cell clonotypes of N. mikurensis-positive lymphoma patients, and it is essential to gain a deeper understanding of the broader heavy chain repertoire. That can be achieved by analysing other aspects such as VDJ gene frequency, somatic hypermutations in complementarity determining regions (CDRs) and framework regions (FWRs) of the variable domain, and the physicochemical properties of the H-CDR3 repertoire, the latter being the most hypervariable part of the heavy chain and a major determinant of its specificity. Specifically, eight infected lymphoma patients were analysed for these variables. In short, the analysis of the results separates the data into at least two distinct groups of patients studied. It was also possible to group the patients by integrating the data into heatmap representations that clustered the patients in an unsupervised manner based on the use of IGHV, IGHD and IGHJ gene frequencies and the physicochemical properties of H-CDR3. Interestingly, this correlated closely with the lymphoma classifications of the patients infected with N. mikurensis. Although the sample size is not very large, and work is currently being to recruit more patients and controls, these results are promising as not only may there be an underlying signature related to the infection, but they may also help to separate the heavy chain repertoire data into groups, which in turn may help to diagnose lymphoma patients more accurately and perhaps more quickly and find the right treatment from the outset. 

One of the main components of atherosclerotic plaque is the production and accumulation of cholesterol crystals (CCs), which could serve as a biomarker of atherosclerosis. Atherosclerosis is a chronic inflammatory artery disease that is the root cause of myocardial infarction and stroke. Endothelial dysfunction is one of the main contributors to the development of atherosclerosis. The aim of the study was to investigate whether Human Umbilical Vein Endothelial Cells (HUVECs) can uptake CCs and to examine CCs-induced inflammatory response in HUVECs. Using molecular and functional techniques, the distinctive characteristic of CC-mediated immune response was discovered in HUVECs. CCs were mostly taken up by HUVECs by macropinocytosis and phagocytosis. CCs were found to induce Signal Transducer of Activators of Transcription (STAT) 3 phosphorylation and Interleukin (IL)-6 release in HUVEC. In addition, Caspase activation and recruitment domain 8 (CARD8) knockdown drastically reduced CCs uptake and CCs induced IL-6 expression in HUVECs. Moreover, the stem cell growth factor (SCGF)-b protein release was downregulated in response to CCs. IL-1A and Colony Stimulating Factor (CSF) 2 were identified as the topmost hub nodes interacting with all other differentially expressed proteins. A significant increase in neutrophil adhesion on HUVECS was found in response to CCs and conditioned medium from CCs-treated HUVECs. In conclusion, the study findings suggest that the CCs induceSTAT3-mediated IL-6 release and neutrophil adhesion, thereby promoting inflammation in HUVECs.

The advancement in high-throughput sequencing technologies and systems biology approaches have revolutionized our understanding of biological systems and opened a new path to investigate unacknowledged biological phenomena. In parallel, the field of human microbiome research has greatly evolved and the relative contribution of the gut microbiome to health and disease have been systematically explored. This review provides an overview of the network-based and translational systems biology-based studies focusing on the function and composition of gut microbiota. We also discussed the association between the gut microbiome and the overall human physiology, as well as hepatic diseases and other metabolic disorders.

Burkholderia cenocepacia is among the important pathogens isolated from cystic fibrosis (CF) patients. It has attracted considerable attention because of its capacity to evade host immune defenses during chronic infection. Advances in systems biology methodologies have led to the emergence of methods that integrate experimental transcriptomics data and genome-scale metabolic models (GEMs). Here, we integrated transcriptomics data of bacterial cells grown on exponential and biofilm conditions into a manually curated GEM of B. cenocepacia. We observed substantial differences in pathway response to different growth conditions and alternative pathway susceptibility to extracellular nutrient availability. For instance, we found that blockage of the reactions was vital through the lipid biosynthesis pathways in the exponential phase and the absence of microenvironmental lysine and tryptophan are essential for survival. During biofilm development, bacteria mostly had conserved lipid metabolism but altered pathway activities associated with several amino acids and pentose phosphate pathways. Furthermore, conversion of serine to pyruvate and 2,5-dioxopentanoate synthesis are also identified as potential targets for metabolic remodeling during biofilm development. Altogether, our integrative systems biology analysis revealed the interactions between the bacteria and its microenvironment and enabled the discovery of antimicrobial targets for biofilm-related diseases.

Drug discovery is a complicated and expensive process in the billion dollar range. One way of making the drug development process more efficient is better information handling, modelling and visualisation. The majority of todays drugs are small molecules, which interact with drug targets to cause an effect. Since the 1980s large amounts of compounds have been systematically tested by robots in so called high-throughput screening. Ligand-based drug discovery is based on modelling drug molecules. In the field known as Quantitative Structure–Activity Relationship (QSAR) molecules are described by molecular descriptors which are used for building mathematical models. Based on these models molecular properties can be predicted and using the molecular descriptors molecules can be compared for, e.g., similarity. Bioclipse is a workbench for the life sciences which provides ligand-based tools through a point and click interface. 

The aims of this thesis were to research, and develop new or improved ligand-based methods and open source software, and to work towards making these tools available for users through the Bioclipse workbench. To this end, a series of molecular signature studies was done and various Bioclipse plugins were developed.

An introduction to the field is provided in the thesis summary which is followed by five research papers. Paper I describes the Bioclipse 2 software and the Bioclipse scripting language. In Paper II the laboratory information system Brunn for supporting work with dose-response studies on microtiter plates is described. In Paper III the creation of a molecular fingerprint based on the molecular signature descriptor is presented and the new fingerprints are evaluated for target prediction and found to perform on par with industrial standard commercial molecular fingerprints. In Paper IV the effect of different parameter choices when using the signature fingerprint together with support vector machines (SVM) using the radial basis function (RBF) kernel is explored and reasonable default values are found. In Paper V the performance of SVM based QSAR using large datasets with the molecular signature descriptor is studied, and a QSAR model based on 1.2 million substances is created and made available from the Bioclipse workbench.

Lipids have many important biological roles, such as energy storage sources, structural components of plasma membranes and as intermediates in metabolic and signaling pathways. Lipid metabolism is under tight homeostatic control, exhibiting spatial and dynamic complexity at multiple levels. Consequently, lipid-related disturbances play important roles in the pathogenesis of most of the common diseases. Lipidomics, defined as the study of lipidomes in biological systems, has emerged as a rapidly-growing field. Due to the chemical and functional diversity of lipids, the application of a systems biology approach is essential if one is to address lipid functionality at different physiological levels. In parallel with analytical advances to measure lipids in biological matrices, the field of computational lipidomics has been rapidly advancing, enabling modeling of lipidomes in their pathway, spatial and dynamic contexts. This review focuses on recent progress in systems biology approaches to study lipids in health and disease, with specific emphasis on methodological advances and biomedical applications.