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  • 1.
    Ahlinder, Jon
    et al.
    Totalförsvarets Forskningsinstitut, FOI, Stockholm, Sweden.
    Nordgaard, Anders
    Swedish National Forensic Centre (NFC), Linköping, Sweden.
    Wiklund Lindström, Susanne
    Totalförsvarets Forskningsinstitut, FOI, Stockholm, Sweden.
    Chemometrics comes to court: evidence evaluation of chem–bio threat agent attacks2015In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 29, no 5, p. 267-276Article in journal (Refereed)
    Abstract [en]

    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.

  • 2.
    Alexsson, Andrei
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics .
    Unsupervised hidden Markov model for automatic analysis of expressed sequence tags2011Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    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.

  • 3.
    Barrientos-Somarribas, Mauricio
    et al.
    Karolinska Inst, Sweden.
    Messina, David N.
    Stockholm Univ, Sweden.
    Pou, Christian
    Karolinska Inst, Sweden.
    Lysholm, Fredrik
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Bjerkner, Annelie
    Karolinska Univ Hosp, Sweden.
    Allander, Tobias
    Karolinska Univ Hosp, Sweden.
    Andersson, Björn
    Karolinska Inst, Sweden.
    Sonnhammer, Erik L. L.
    Stockholm Univ, Sweden.
    Discovering viral genomes in human metagenomic data by predicting unknown protein families2018In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 8, article id 28Article in journal (Refereed)
    Abstract [en]

    Massive amounts of metagenomics data are currently being produced, and in all such projects a sizeable fraction of the resulting data shows no or little homology to known sequences. It is likely that this fraction contains novel viruses, but identification is challenging since they frequently lack homology to known viruses. To overcome this problem, we developed a strategy to detect ORFan protein families in shotgun metagenomics data, using similarity-based clustering and a set of filters to extract bona fide protein families. We applied this method to 17 virus-enriched libraries originating from human nasopharyngeal aspirates, serum, feces, and cerebrospinal fluid samples. This resulted in 32 predicted putative novel gene families. Some families showed detectable homology to sequences in metagenomics datasets and protein databases after reannotation. Notably, one predicted family matches an ORF from the highly variable Torque Teno virus (TTV). Furthermore, follow-up from a predicted ORFan resulted in the complete reconstruction of a novel circular genome. Its organisation suggests that it most likely corresponds to a novel bacteriophage in the microviridae family, hence it was named bacteriophage HFM.

  • 4.
    Bartoszek, Krzysztof
    Department of Mathematics, Uppsala University, Uppsala, Sweden.
    Phylogenetic effective sample size2016In: Journal of Theoretical Biology, ISSN 0022-5193, E-ISSN 1095-8541, Vol. 407, p. 371-386Article in journal (Refereed)
    Abstract [en]

    In this paper I address the question—how large is a phylogenetic sample? I propose a definition of a phylogenetic effective sample size for Brownian motion and Ornstein-Uhlenbeck processes-the regression effective sample size. I discuss how mutual information can be used to define an effective sample size in the non-normal process case and compare these two definitions to an already present concept of effective sample size (the mean effective sample size). Through a simulation study I find that the AICc is robust if one corrects for the number of species or effective number of species. Lastly I discuss how the concept of the phylogenetic effective sample size can be useful for biodiversity quantification, identification of interesting clades and deciding on the importance of phylogenetic correlations.

  • 5.
    Bauer, Eva
    et al.
    Technical University of Munich, Germany.
    Schmutzer, Thomas
    Leibniz Institute Plant Genet and Crop Plant Research IPK Gat, Germany.
    Barilar, Ivan
    University of Hohenheim, Germany.
    Mascher, Martin
    Leibniz Institute Plant Genet and Crop Plant Research IPK Gat, Germany.
    Gundlach, Heidrun
    Helmholtz Zentrum Munchen, Germany.
    Martis, Mihaela-Maria
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences. Helmholtz Zentrum Munchen, Germany.
    Twardziok, Sven O.
    Helmholtz Zentrum Munchen, Germany.
    Hackauf, Bernd
    Julius Kuhn Institute, Germany.
    Gordillo, Andres
    KWS LOCHOW GMBH, Germany.
    Wilde, Peer
    KWS LOCHOW GMBH, Germany.
    Schmidt, Malthe
    KWS LOCHOW GMBH, Germany.
    Korzun, Viktor
    KWS LOCHOW GMBH, Germany.
    Mayer, Klaus F. X.
    Helmholtz Zentrum Munchen, Germany.
    Schmid, Karl
    University of Hohenheim, Germany.
    Schoen, Chris-Carolin
    Technical University of Munich, Germany.
    Scholz, Uwe
    Leibniz Institute Plant Genet and Crop Plant Research IPK Gat, Germany.
    Towards a whole-genome sequence for rye (Secale cereale L.)2017In: The Plant Journal, ISSN 0960-7412, E-ISSN 1365-313X, Vol. 89, no 5, p. 853-869Article in journal (Refereed)
    Abstract [en]

    We report on a whole-genome draft sequence of rye (Secale cereale L.). Rye is a diploid Triticeae species closely related to wheat and barley, and an important crop for food and feed in Central and Eastern Europe. Through whole-genome shotgun sequencing of the 7.9-Gbp genome of the winter rye inbred line Lo7 we obtained a de novo assembly represented by 1.29 million scaffolds covering a total length of 2.8 Gbp. Our reference sequence represents nearly the entire low-copy portion of the rye genome. This genome assembly was used to predict 27 784 rye gene models based on homology to sequenced grass genomes. Through resequencing of 10 rye inbred lines and one accession of the wild relative S. vavilovii, we discovered more than 90 million single nucleotide variants and short insertions/deletions in the rye genome. From these variants, we developed the high-density Rye600k genotyping array with 600 843 markers, which enabled anchoring the sequence contigs along a high-density genetic map and establishing a synteny-based virtual gene order. Genotyping data were used to characterize the diversity of rye breeding pools and genetic resources, and to obtain a genome-wide map of selection signals differentiating the divergent gene pools. This rye whole-genome sequence closes a gap in Triticeae genome research, and will be highly valuable for comparative genomics, functional studies and genome-based breeding in rye.

  • 6.
    Bergamino, Maurizio
    et al.
    Laureate Institute for Brain Research, Tulsa, OK, USA.
    Farmer, Madison
    Roosevelt University, Department of Industrial and Organizational Psychology, Chicago, IL, USA.
    Yeh, Hung-Wen
    Laureate Institute for Brain Research, Tulsa, OK, USA.
    Paul, Elisabeth
    Linköping University, Department of Clinical and Experimental Medicine, Center for Social and Affective Neuroscience. Linköping University, Faculty of Medicine and Health Sciences.
    Hamilton, Paul J.
    Linköping University, Department of Clinical and Experimental Medicine, Center for Social and Affective Neuroscience. Linköping University, Faculty of Medicine and Health Sciences.
    Statistical differences in the white matter tracts in subjects with depression by using different skeletonized voxel-wise analysis approaches and DTI fitting procedures2017In: Brain Research, ISSN 0006-8993, E-ISSN 1872-6240, Vol. 1669, p. 131-140Article in journal (Refereed)
    Abstract [en]

    Major depressive disorder (MDD) is one of the most significant contributors to the global burden of illness. Diffusion tensor imaging (DTI) is a procedure that has been used in several studies to characterize abnormalities in white matter (WM) microstructural integrity in MDD. These studies, however, have provided divergent findings, potentially due to the large variety of methodological alternatives available in conducting DTI research. In order to determine the importance of different approaches to coregistration of DTI-derived metrics to a standard space, we compared results from two different skeletonized voxel-wise analysis approaches: the standard TBBS pipeline and the Advanced Normalization Tools (ANTs) approach incorporating a symmetric image normalization (SyN) algorithm and a group-wise template (ANTs TBSS). We also assessed effects of applying twelve different fitting procedures for the diffusion tensor. For our dataset, lower fractional anisotropy (FA) and axial diffusivity (AD) in depressed subjects compared with healthy controls were found for both methods and for all fitting procedures. No group differences were found for radial and mean diffusivity indices. Importantly, for the AD metric, the normalization methods and fitting procedures showed reliable differences, both in the volume and in the number of significant between-groups difference clusters detected. Additionally, a significant voxel-based correlation, in the left inferior fronto-occipital fasciculus, between AD and self-reported stress was found only for one of the normalization procedure (ANTs TBSS). In conclusion, the sensitivity to detect group-level effects on DTI metrics might depend on the DTI normalization and/or tensor fitting procedures used.

  • 7.
    Bergenholm, Linnéa
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Health Sciences.
    Modeling as a Tool to Support Self-Management of Type 1 Diabetes2013Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Type 1 diabetes (T1D) is an auto-immune disease characterized by insulin-deficiency. Insulin is a metabolic hormone that is involved in lowering blood glucose (BG) levels in order to control BG level to a tight range. In T1D this glycemic control is lost, causing chronic hyperglycemia (excess glucose in blood stream). Chronic hyperglycemia damages vital tissues. Therefore, glycemic control must be restored.

    A common therapy for restoring glycemic control is intensive insulin therapy, where the missing insulin is replaced with regular insulin injections. When dosing this compensatory insulin many factors that affect glucose metabolism must be considered. Linkura is a company that has developed tools for monitoring the most important factors, which are meals and exercise. In the Linkura meal and exercise tools, the nutrition content in meals and the calorie consumption during exercise are estimated. Another tool designed to aid control of BG is the bolus calculator. Bolus calculators use input of BG level, carbohydrate intake, and insulin history to estimate insulin need. The accuracy of these insulin bolus calculations suffer from two problems. First, errors occur when users inaccurately estimate the carbohydrate content in meals. Second, exercise is not included in bolus calculations. To reduce these problems, it was suggested that the Linkura web tools could be utilized in combination with a bolus calculator.

    For this purpose, a bolus calculator was developed. The bolus calculator was based on existing models that utilize clinical parameters to relate changes in BG levels to meals, insulin, and exercise stimulations. The bolus calculator was evaluated using data collected from Linkura's web tools. The collected data showed some inconsistencies which cannot be explained by any model.  The performance of the bolus calculator in predicting BG levels using general equations to derive the clinical parameters was inadequate. Performance was increased by adopting an update-algorithm where the clinical parameters were updated daily using previous data. Still, better model performance is prefered for use in a bolus calculator.  

    The results show potential in developing bolus calculator tools combined with the Linkura tools. For such bolus calculator, further evaluation on modeling long-term exercise and additional safety features minimizing risk of hypoglycemia are required.

  • 8.
    Bergman Laurila, Jonas
    Linköping University. Linköping University, Department of Computer and Information Science.
    Ontology Slice Generation and Alignment for Enhanced Life Science Literature Search2009Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Query composition is an often complicated and cumbersome task for persons performing a literature search. This thesis is part of a project which aims to present possible queries to the user in form of natural language expressions. The thesis presents methods of ontology slice generation. Slices are parts of ontologies connecting two concepts along all possible paths between them. Those slices hence represent all relevant queries connecting the concepts and the paths can in a later step be translated into natural language expressions. Methods of slice alignment, connecting slices that originate from different ontologies, are also presented. The thesis concludes with some example scenarios and comparisons to related work.

  • 9.
    Bzhalava, David
    et al.
    Karolinska Institutet and Karolinska University Hospital, Stockholm.
    Ekström, Johanna
    Lund University, Malmö.
    Lysholm, Fredrik
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, The Institute of Technology.
    Hultin, Emilie
    Karolinska Institutet and Karolinska University Hospital, Stockholm.
    Faust, Helena
    Lund University, Malmö.
    Persson, Bengt
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, The Institute of Technology.
    Lehtinen, Matti
    National Institute for Health and Welfare, Oulu, Finland.
    de Villiers, Ethel-Michele
    Deutsches Krebsforschungszentrum, Heidelberg, Germany.
    Dillner, Joakim
    Karolinska Institutet and Karolinska University Hospital, Stockholm.
    Phylogenetically diverse TT virus viremia among pregnant women2012In: Virology, ISSN 0042-6822, E-ISSN 1096-0341, Vol. 432, no 2, p. 427-434Article in journal (Refereed)
    Abstract [en]

    Infections during pregnancy have been suggested to be involved in childhood leukemias. We used high-throughput sequencing to describe the viruses most readily detectable in serum samples of pregnantwomen. Serum DNA of 112 mothers to leukemic children was amplified using whole genome amplification. Sequencing identified one TTvirus (TTV) isolate belonging to a known type and two putatively new TTVs. For 22 mothers, we also performed TTV amplification by general primer PCR before sequencing. This detected 39 TTVs, two of which were identical to the TTVs found after whole genome amplification.

    Altogether, we found 40 TTV isolates, 29 of which were putatively new types (similarities ranging from 89% to 69%). In conclusion, high throughput sequencing is useful to describe the known or unknown viruses that are present in serum samples of pregnantwomen.

  • 10.
    Carlsson, Jonas
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics . Linköping University, The Institute of Technology.
    Mutational effects on protein structure and function2009Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    In this thesis several important proteins are investigated from a structural perspective. Some of the proteins are disease related while other have important but not completely characterised functions. The techniques used are general as demonstrated by applications on metabolic proteins (CYP21, CYP11B1, IAPP, ADH3), regulatory proteins (p53, GDNF) and a transporter protein (ANTR1).

    When the protein CYP21 (steroid 21-hydroxylase) is deficient it causes CAH (congenital adrenal hyperplasia). For this protein, there are about 60 known mutations with characterised clinical phenotypes. Using manual structural analysis we managed to explain the severity of all but one of the mutations. By observing the properties of these mutations we could perform good predictions on, at the time, not classified mutations.

    For the cancer suppressor protein p53, there are over thousand mutations with known activity. To be able to analyse such a large number of mutations we developed an automated method for evaluation of the mutation effect called PREDMUT. In this method we include twelve different prediction parameters including two energy parameters calculated using an energy minimization procedure. The method manages to differentiate severe mutations from non-severe mutations with 77% accuracy on all possible single base substitutions and with 88% on mutations found in breast cancer patients.

    The automated prediction was further applied to CYP11B1 (steroid 11-beta-hydroxylase), which in a similar way as CYP21 causes CAH when deficient. A generalized method applicable to any kind of globular protein was developed. The method was subsequently evaluated on nine additional proteins for which mutants were known with annotated disease phenotypes. This prediction achieved 84% accuracy on CYP11B1 and 81% accuracy in total on the evaluation proteins while leaving 8% as unclassified. By increasing the number of unclassified mutations the accuracy of the remaining mutations could be increased on the evaluation proteins and substantially increase the classification quality as measured by the Matthews correlation coefficient. Servers with predictions for all possible single based substitutions are provided for p53, CYP21 and CYP11B1.

    The amyloid formation of IAPP (islet amyloid polypeptide) is strongly connected to diabetes and has been studied using both molecular dynamics and Monte Carlo energy minimization. The effects of mutations on the amount and speed of amyloid formation were investigated using three approaches. Applying a consensus of the three methods on a number of interesting mutations, 94% of the mutations could be correctly classified as amyloid forming or not, evaluated with in vitro measurements.

    In the brain there are many proteins whose functions and interactions are largely unknown. GDNF (glial cell line-derived neurotrophic factor) and NCAM (neural cell adhesion molecule) are two such neuron connected proteins that are known to interact. The form of interaction was studied using protein--protein docking where a docking interface was found mediated by four oppositely charged residues in respective protein. This interface was subsequently confirmed by mutagenesis experiments. The NCAM dimer interface upon binding to the GDNF dimer was also mapped as well as an additional interacting protein, GFRα1, which was successfully added to the protein complex without any clashes.

    A large and well studied protein family is the alcohol dehydrogenase family, ADH. A class of this family is ADH3 (alcohol dehydrogenase class III) that has several known substrates and inhibitors. By using virtual screening we tried to characterize new ligands. As some ligands were already known we could incorporate this knowledge when the compound docking simulations were scored and thereby find two new substrates and two new inhibitors which were subsequently successfully tested in vitro.

    ANTR1 (anion transporter 1) is a membrane bound transporter important in the photosynthesis in plants. To be able to study the amino acid residues involved in inorganic phosphate transportation a homology model of the protein was created. Important residues were then mapped onto the structure using conservation analysis and we were in this way able to propose roles of amino acid residues involved in the transportation of inorganic phosphate. Key residues were subsequently mutated in vitro and a transportation process could be postulated.

    To conclude, we have used several molecular modelling techniques to find functional clues, interaction sites and new ligands. Furthermore, we have investigated the effect of muations on the function and structure of a multitude of disease related proteins.

     

    List of papers
    1. Molecular Model of Human CYP21 Based onMammalian CYP2C5: Structural Features Correlatewith Clinical Severity of Mutations CausingCongenital Adrenal Hyperplasia
    Open this publication in new window or tab >>Molecular Model of Human CYP21 Based onMammalian CYP2C5: Structural Features Correlatewith Clinical Severity of Mutations CausingCongenital Adrenal Hyperplasia
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    2006 (English)In: Molecular Endocrinology, ISSN 0888-8809, E-ISSN 1944-9917, Vol. 20, no 11, p. 2946-2964Article in journal (Refereed) Published
    Abstract [en]

    Enhanced understanding of structure-function relationshipsof human 21-hydroxylase, CYP21, is requiredto better understand the molecular causesof congenital adrenal hyperplasia. To this end, astructural model of human CYP21 was calculatedbased on the crystal structure of rabbit CYP2C5.All but two known allelic variants of missense type,a total of 60 disease-causing mutations and sixnormal variants, were analyzed using this model. Astructural explanation for the corresponding phenotypewas found for all but two mutants for whichavailable clinical data are also discrepant with invitro enzyme activity. Calculations of protein stabilityof modeled mutants were found to correlateinversely with the corresponding clinical severity.Putative structurally important residues were identifiedto be involved in heme and substrate binding,redox partner interaction, and enzyme catalysisusing docking calculations and analysis of structurallydetermined homologous cytochrome P450s(CYPs). Functional and structural consequences ofseven novel mutations, V139E, C147R, R233G,T295N, L308F, R366C, and M473I, detected inScandinavian patients with suspected congenitaladrenal hyperplasia of different severity, were predictedusing molecular modeling. Structural featuresdeduced from the models are in good correlationwith clinical severity of CYP21 mutants,which shows the applicability of a modeling approachin assessment of new CYP21 mutations.

    Place, publisher, year, edition, pages
    Stanford: The endocrin society, 2006
    Keywords
    Mutations, prediction, CAH, CYP21, homology model
    National Category
    Bioinformatics and Systems Biology
    Identifiers
    urn:nbn:se:liu:diva-21305 (URN)10.1210/me.2006-0172 (DOI)
    Available from: 2009-09-30 Created: 2009-09-30 Last updated: 2017-12-13Bibliographically approved
    2. Investigation and prediction of the severity of p53 mutants using parameters from structural calculations
    Open this publication in new window or tab >>Investigation and prediction of the severity of p53 mutants using parameters from structural calculations
    2009 (English)In: The FEBS Journal, ISSN 1742-464X, E-ISSN 1742-4658, Vol. 276, no 15, p. 4142-4155Article in journal (Refereed) Published
    Abstract [en]

    A method has been developed to predict the effects of mutations in the p53 cancer suppressor gene. The new method uses novel parameters combined with previously established parameters. The most important parameter is the stability measure of the mutated structure calculated using molecular modelling. For each mutant, a severity score is reported, which can be used for classification into deleterious and nondeleterious. Both structural features and sequence properties are taken into account. The method has a prediction accuracy of 77% on all mutants and 88% on breast cancer mutations affecting WAF1 promoter binding. When compared with earlier methods, using the same dataset, our method clearly performs better. As a result of the severity score calculated for every mutant, valuable knowledge can be gained regarding p53, a protein that is believed to be involved in over 50% of all human cancers.

    Keywords
    Cancer; molecular modelling; mutations; p53; structural prediction
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-20141 (URN)10.1111/j.1742-4658.2009.07124.x (DOI)
    Available from: 2009-08-31 Created: 2009-08-31 Last updated: 2017-12-13Bibliographically approved
    3. A structural model of human steroid 11-betahydroxylase,CYP11B1, used to predict consequences of mutations
    Open this publication in new window or tab >>A structural model of human steroid 11-betahydroxylase,CYP11B1, used to predict consequences of mutations
    2009 (English)Article in journal (Other academic) Submitted
    Abstract [en]

    A prediction method has been developed to estimate the severity of amino acid residue exchanges in human steroid 11-beta-hydroxylase, CYP11B1, due to mutations in the corresponding gene. The prediction is based both on structural and on sequence dependent parameters. The method uses two approaches; one with general molecular property weights and one with a consensus voting strategy based upon distribution of molecular properties, which does not require any training. Both methods are tested on known mutations in CYP11B1 and result in 85% prediction accuracy. The consensus voting method is then further evaluated on 9 proteins with an average of 81% prediction accuracy. A server utilizing the results from the consensus voting on CYP11B1 is provided where the user can extract information about new mutants. A similar server is also provided for mutants in human steroid 21-hydroxylase (CYP21).

    Keywords
    CYP11B1, steroid 11-beta-hydroxylase, molecular modeling, structural prediction, mutations
    National Category
    Natural Sciences
    Identifiers
    urn:nbn:se:liu:diva-51118 (URN)
    Available from: 2009-10-19 Created: 2009-10-19 Last updated: 2009-10-19Bibliographically approved
    4. Disruption of the GDNF Binding Site in NCAM DissociatesLigand Binding and Homophilic Cell Adhesion
    Open this publication in new window or tab >>Disruption of the GDNF Binding Site in NCAM DissociatesLigand Binding and Homophilic Cell Adhesion
    Show others...
    2007 (English)In: Journal of Biological Chemistry, ISSN 0021-9258, E-ISSN 1083-351X, Vol. 282, no 17, p. 12734-12740Article in journal (Refereed) Published
    Abstract [en]

    Most plasma membrane proteins are capable of sensing multiple cell-cell and cell-ligand interactions, but the extent towhich this functional versatility is founded on their modular design is less clear. We have identified the third immunoglobulin domain of the Neural Cell Adhesion Molecule (NCAM) as the necessary and sufficient determinant for its interaction with Glial Cell Line-derived Neurotrophic Factor (GDNF). Four charged contacts were identified by molecular modeling as the main contributors to binding energy. Their mutation abolished GDNF binding to NCAM but left intact the ability of NCAM tomediate cell adhesion, indicating that the two functions are genetically separable. The GDNF-NCAM interface allows complex formation with the GDNF family receptor α1, shedding light on the molecular architecture of a multicomponent GDNF receptor.

    Place, publisher, year, edition, pages
    Bethesda, MD: American Society for Biochemistry and Molecular Biology, 2007
    Keywords
    homology model, protein complex, interaction interface, mutagenesis
    National Category
    Bioinformatics and Systems Biology
    Identifiers
    urn:nbn:se:liu:diva-21306 (URN)10.1074/jbc.M701588200 (DOI)
    Available from: 2009-09-30 Created: 2009-09-30 Last updated: 2017-12-13Bibliographically approved
    5. Functionally Important Amino Acids in the Arabidopsis Thylakoid Phosphate Transporter: Homology Modeling and Site-directed Mutagenesis
    Open this publication in new window or tab >>Functionally Important Amino Acids in the Arabidopsis Thylakoid Phosphate Transporter: Homology Modeling and Site-directed Mutagenesis
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    2010 (English)In: Biochemistry, ISSN 0006-2960, E-ISSN 1520-4995, Vol. 49, no 30, p. 6430-6439Article in journal (Other academic) Published
    Abstract [en]

    The anion transporter 1 (ANTR1) from Arabidopsis thaliana, homologous to the mammalian SLC17 family, has recently been localized to the chloroplast thylakoid membrane. When expressed heterologously in Escherichia coli, ANTR1 mediates a Na+-dependent active transport of inorganic phosphate (Pi). The aim of this study was to identify amino acids involved in substrate binding/translocation by ANTR1 and in the Na+-dependence of its activity. A threedimensional structural model of ANTR1 was constructed using the crystal structure of glycerol-3-phosphate/phosphate antiporter (GlpT) from E.coli as a template. Based on this model and multiple sequence alignments, five highly conserved residues in plant ANTRs and mammalian SLC17 homologues have been selected for site-directed mutagenesis, namely Arg-120, Ser-124 and Arg-201 inside the putative translocation pathway, Arg-228 and Asp-382 exposed at the cytosolic surface of the protein. The activities of the wild type and mutant proteins have been analyzed using expression in E. coli and radioactive transport assays, and compared with bacterial cells carrying an empty plasmid. Based on Pi- and Na+-dependent kinetics, we propose that Arg-120, Arg-201 and Arg-228 are involved in binding and translocation of the substrate, Ser-124 functions as a periplasmic gate for Na+ ions, and finally Asp-382 participates in the turnover of the transporter via ionic interaction with either Arg-228 or Na+ ions. We also propose that the corresponding residues may have a similar function in other plant and mammalian SLC17 homologous transporters.

    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-51119 (URN)10.1021/bi100239j (DOI)
    Note
    On the day of the defence day the status of this article was ManuscriptAvailable from: 2009-10-19 Created: 2009-10-19 Last updated: 2017-12-12Bibliographically approved
    6. A folding study on IAPP (Islet Amyloid Polypeptide) using molecular dynamics simulations
    Open this publication in new window or tab >>A folding study on IAPP (Islet Amyloid Polypeptide) using molecular dynamics simulations
    Show others...
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    Amyloidosis is the largest group among the protein misfolding diseases, and includes well known diseases such as Alzheimer’s disease and type 2 diabetes. In the latter, islet amyloid is present in the pancreas in almost all individuals. Today, more than 25 different proteins have been isolated from amyloid deposits in human. Even though these proteins differ in size, charge and sequence they all have the capacity to assemble in to fibrillar structures with inseparable morphological appearance. Therefore, it can be assumed that the fibril process is based upon principles that are general for all proteins and knowledge derived from one protein can be used for other amyloid proteins. In this paper, we study the process of amyloid formation in parts of islet amyloid polypeptide (residues 18-29 and 11-37) by analyzing mutations using three different in silico methods. Finally, we use the methods to predict the amyloidogenic properties of the native IAPP and 16 variants thereof and compare the result with in vitro measurements. Using a consensus prediction of the three methods we managed to correctly classify all but two peptides. We have also given further evidence to the importance of S28P for inhibiting amyloid fibre formation, found evidence for antiparallel stacking, and identified important regions for beta sheet stability.

    Keywords
    IAPP, molecular modeling, amyloid, prediction, molecular dynamics, Monte Carlo
    National Category
    Natural Sciences
    Identifiers
    urn:nbn:se:liu:diva-51120 (URN)
    Available from: 2009-10-19 Created: 2009-10-19 Last updated: 2010-01-14Bibliographically approved
    7. Virtual screening for ligands to human alcohol dehydrogenase 3
    Open this publication in new window or tab >>Virtual screening for ligands to human alcohol dehydrogenase 3
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    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    Alcohol dehydrogenase 3 (ADH3) has been suggested a role in nitric oxide homeostasis due to its function as a S-nitrosoglutathione (GSNO) reductase. This has requested a modulator of the ADH3 activity for control of GSNO levels. Today virtual screenings are frequently used in drug discovery to dock and rank a large number of compounds. With molecular dockings of more than 40,000 compounds into the active site pocket of human ADH3 we ranked compounds with a novel method. Six top ranked compounds that were not known to interact with ADH3 were tested in vitro, where two showed substrate activity (9-decen-1-ol and dodecyltetraglycol), two showed inhibition capacity (deoxycholic acid and doxorubicin) and two did not have any detectable effect. For the substrates, site specific interactions and calculated binding scoring energies were determined with an extended docking simulation including flexible side chains of amino acids residues. The binding scoring energies correlated well with the logarithm of the substrates kcat over Km values. Furthermore, with these computational and experimental data three different lines for specific inhibitors for ADH3 are suggested: fatty acids, glutathione analogs and in addition deoxycholic acids.

    Keywords
    Alcohol dehydrogenase, Enzyme kinetics, Molecular docking, Virtual screening
    National Category
    Natural Sciences
    Identifiers
    urn:nbn:se:liu:diva-51121 (URN)
    Available from: 2009-10-19 Created: 2009-10-19 Last updated: 2010-01-14Bibliographically approved
  • 11.
    Carlsson, Jonas
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, The Institute of Technology.
    Persson, Bengt
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, The Institute of Technology.
    Investigating protein variants using structural calculation techniques2012In: Homology Modeling: Methods and Protocols / [ed] Andrew J. W. Orry and Ruben Abagyan, Springer, 2012, Vol. 857, p. 313-330Chapter in book (Other academic)
    Abstract [en]

    Knowledge about protein tertiary structure can guide experiments, assist in the understanding of structure-function relationships, and aid the design of new therapeutics for disease. Homology modeling is an in silico method that predicts the tertiary structure of an amino acid sequence based on a homologous experimentally determined structure. In, Homology Modeling: Methods and Protocols experts in the field describe each homology modeling step from first principles, provide case studies for challenging modeling targets and describe methods for the prediction of how other molecules such as drugs can interact with the protein. Written in the highly successful Methods in Molecular Biology series format, the chapters include the kind of detailed description and implementation advice that is crucial for getting optimal results in the laboratory. Thorough and intuitive, Homology Modeling: Methods and Protocols guides scientists in the available homology modeling methods.

  • 12.
    Durrieu, Lucía
    et al.
    IFIByNE, DFBMC, FCEN, UBA, Buenos Aires, Argentine.
    Johansson, Rikard
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Bush, Alan
    IFIByNE, DFBMC, FCEN, UBA, Buenos Aires, Argentine.
    Janzén, David
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Gollvik, Martin
    Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering.
    Cedersund, Gunnar
    Linköping University, Faculty of Science & Engineering. Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering.
    Colman-Lerner, Alejandro
    IFIByNE, DFBMC, FCEN, UBA, Buenos Aires, Argentine.
    Quantification of nuclear transport in single cells2014Other (Other academic)
    Abstract [en]

    Regulation of nuclear transport is a key cellular function involved in many central processes, such as gene expression regulation and signal transduction. Rates of protein movement between cellular compartments can be measured by FRAP. However, no standard and reliable methods to calculate transport rates exist. Here we introduce a method to extract import and export rates, suitable for noisy single cell data. This method consists of microscope procedures, routines for data processing, an ODE model to fit to the data, and algorithms for parameter optimization and error estimation. Using this method, we successfully measured import and export rates in individual yeast. For YFP, average transport rates were 0.15 sec-1. We estimated confidence intervals for these parameters through likelihood profile analysis. We found large cell-to-cell variation (CV = 0.79) in these rates, suggesting a hitherto unknown source of cellular heterogeneity. Given the passive nature of YFP diffusion, we attribute this variation to large differences among cells in the number or quality of nuclear pores. Owing to its broad applicability and sensitivity, this method will allow deeper mechanistic insight into nuclear transport processes and into the largely unstudied cell-to-cell variation in kinetic rates.

  • 13.
    Elfving, Eric
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics .
    Automated annotation of protein families2011Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Introduction: The great challenge in bioinformatics is data integration. The amount of available data is always increasing and there are no common unified standards of where, or how, the data should be stored. The aim of this workis to build an automated tool to annotate the different member families within the protein superfamily of medium-chain dehydrogenases/reductases (MDR), by finding common properties among the member proteins. The goal is to increase the understanding of the MDR superfamily as well as the different member families.This will add to the amount of knowledge gained for free when a new, unannotated, protein is matched as a member to a specific MDR member family.

    Method: The different types of data available all needed different handling. Textual data was mainly compared as strings while numeric data needed some special handling such as statistical calculations. Ontological data was handled as tree nodes where ancestry between terms had to be considered. This was implemented as a plugin-based system to make the tool easy to extend with additional data sources of different types.

    Results: The biggest challenge was data incompleteness yielding little (or no) results for some families and thus decreasing the statistical significance of the results. Results show that all the human and mouse MDR members have a Pfam ADH domain (ADH_N and/or ADH_zinc_N) and takes part in an oxidation-reduction process, often with NAD or NADP as cofactor. Many of the proteins contain zinc and are expressed in liver tissue.

    Conclusions: A python based tool for automatic annotation has been created to annotate the different MDR member families. The tool is easily extendable to be used with new databases and much of the results agrees with information found in literature. The utility and necessity of this system, as well as the quality of its produced results, are expected to only increase over time, even if no additional extensions are produced, as the system itself is able to make further and more detailed inferences as more and more data become available.

  • 14.
    Fallahshahroudi, Amir
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Biology. Linköping University, Faculty of Science & Engineering.
    de Kock, Nick
    Department of Chemistry - Biomedical Center, Analytical Chemistry and Science for Life Laboratory, Uppsala University, Sweden.
    Johnsson, Martin
    Linköping University, Department of Physics, Chemistry and Biology, Biology. Linköping University, Faculty of Science & Engineering.
    Ubhayasekera, S.J. Kumari A.
    Department of Chemistry - Biomedical Center, Analytical Chemistry and Science for Life Laboratory, Uppsala University, Sweden.
    Bergqvist, Jonas
    Department of Chemistry - Biomedical Center, Analytical Chemistry and Science for Life Laboratory, Uppsala University, Sweden.
    Wright, Dominic
    Linköping University, Department of Physics, Chemistry and Biology, Biology. Linköping University, Faculty of Science & Engineering.
    Jensen, Per
    Linköping University, Department of Physics, Chemistry and Biology, Biology. Linköping University, Faculty of Science & Engineering.
    Domestication Effects on Stress Induced Steroid Secretion and Adrenal Gene Expression in Chickens2015In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 5, p. 1-10, article id 15345Article in journal (Refereed)
    Abstract [en]

    Understanding the genetic basis of phenotypic diversity is a challenge in contemporary biology. Domestication provides a model for unravelling aspects of the genetic basis of stress sensitivity. The ancestral Red Junglefowl (RJF) exhibits greater fear-related behaviour and a more pronounced HPA-axis reactivity than its domesticated counterpart, the White Leghorn (WL). By comparing hormones (plasmatic) and adrenal global gene transcription profiles between WL and RJF in response to an acute stress event, we investigated the molecular basis for the altered physiological stress responsiveness in domesticated chickens. Basal levels of pregnenolone and dehydroepiandrosterone as well as corticosterone response were lower in WL. Microarray analysis of gene expression in adrenal glands showed a significant breed effect in a large number of transcripts with over-representation of genes in the channel activity pathway. The expression of the best-known steroidogenesis genes were similar across the breeds used. Transcription levels of acute stress response genes such as StAR, CH25 and POMC were upregulated in response to acute stress. Dampened HPA reactivity in domesticated chickens was associated with changes in the expression of several genes that presents potentially minor regulatory effects rather than by means of change in expression of critical steroidogenic genes in the adrenal.

  • 15.
    Forsgren, Mikael Fredrik
    Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Human Whole Body Pharmacokinetic Minimal Model for the Liver Specific Contrast Agent Gd-EOB-DTPA2011Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Magnetic resonance imaging (MRI) of the liver is an important non-invasive tool for diagnosing liver disease. A key application is dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). With the use of the hepatocyte specific contrast agent (CA) Gd-EOB-DTPA it is now possible to evaluate the liver function. Beyond traditional qualitative evaluation of the DCE-MRI images, parametric quantitative techniques are on the rise which yields more objective evaluations. Systems biology is a gradually expanding field using mathematical modeling to gain deeper mechanistic understanding in complex biological systems. The aim of this thesis to combine these two fields in order to derive a physiologically accurate minimal whole body model that can be used to quantitatively evaluate liver function using clinical DCE-MRI examinations. 

    The work is based on two previously published sources of data using Gd-EOB-DTPA in healthy humans; i) a region of interest analysis of the liver using DCE-MRI ii) a pre-clinical evaluation of the contrast agent using blood sampling.  The modeling framework consists of a system of ordinary differential equations for the contrast agent dynamics and non-linear models for conversion of contrast agent concentrations to relaxivity values in the DCE-MRI image volumes.

    Using a χ2-test I have shown that the model, with high probability, can fit the experimental data for doses up to twenty times the clinically used one, using the same parameters for all doses. The results also show that some of the parameters governing the hepatocyte flux of CA can be numerically identifiable. Future applications with the model might be as a basis for regional liver function assessment. This can lead to disease diagnosis and progression evaluation for physicians as well as support for surgeons planning liver resection.

  • 16.
    Ge, Yue
    et al.
    National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency.
    Wang, Da-Zhi
    State Key Laboratory of Marine Environmental Science, College of the Environment and Ecology, Xiamen University, China .
    Chiu, Jen-Fu
    University of Hong Kong and Shantou University College of Medicine, China.
    Cristobal, Susana
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Sheehan, David
    Department of Biochemistry, University College Cork, Ireland.
    Silvestre, Frédéric
    Research Unit in Environmental and Evolutionary Biology, University of Namur, Belgium.
    Peng, Xianxuan
    Center for Proteomics, State Key Laboratory of Bio-Control, School of Life Sciences, Sun Yat-Sen University, China.
    Li, Hui
    Center for Proteomics, State Key Laboratory of Bio-Control, School of Life Sciences, Sun Yat-Sen University, China.
    Gong, Zhiyuan
    Department of Biological Sciences, National University of Singapore, Singapore.
    Lam, Siew Hong
    Department of Biological Sciences, National University of Singapore, Singapore.
    Wentao, Hu
    Department of Biochemistry, University College Cork, Ireland.
    Iwahashi, Hitoshi
    Department of Applied Biological Sciences, Gifu University, Japan.
    Liu, Jianjun
    Shenzhen Center for Disease Control and Prevention, China.
    Mei, Nan
    National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
    Shi, Leming
    National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
    Bruno, Maribel
    National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency.
    Foth, Heidi
    Institute for Environmental Toxicology, Martin Luther University, Halle/Saale, Germany.
    Teichman, Kevin
    Office of Research and Development, U.S. Environmental Protection Agency, Washington D.C., USA.
    Environmental OMICS: Current Status and Future Directions2013In: JOURNAL OF INTEGRATED OMICS, ISSN 2182-0287, Vol. 3, no 2, p. 75-87Article in journal (Refereed)
    Abstract [en]

    Applications of OMICS to high throughput studies of changes of genes, RNAs, proteins, metabolites, and their associated functionsin cells or organisms exposed to environmental chemicals has led to the emergence of a very active research field: environmental OMICS.This developing field holds an important key for improving the scientific basis for understanding the potential impacts of environmentalchemicals on both health and the environment. Here we describe the state of environmental OMICS with an emphasis on its recent accomplishmentsand its problems and potential solutions to facilitate the incorporation of OMICS into mainstream environmental and healthresearch.Data sources: We reviewed relevant and recently published studies on the applicability and usefulness of OMICS technologies to the identificationof toxicity pathways, mechanisms, and biomarkers of environmental chemicals for environmental and health risk monitoring andassessment, including recent presentations and discussions on these issues at The First International Conference on Environmental OMICS(ICEO), held in Guangzhou, China during November 8-12, 2011. This paper summarizes our review.Synthesis: Environmental OMICS aims to take advantage of powerful genomics, transcriptomics, proteomics, and metabolomics tools toidentify novel toxicity pathways/signatures/biomarkers so as to better understand toxicity mechanisms/modes of action, to identify/categorize/prioritize/screen environmental chemicals, and to monitor and predict the risks associated with exposure to environmental chemicalson human health and the environment. To improve the field, some lessons learned from previous studies need to be summarized, aresearch agenda and guidelines for future studies need to be established, and a focus for the field needs to be developed.Conclusions: OMICS technologies for identification of RNA, protein, and metabolic profiles and endpoints have already significantly improvedour understanding of how environmental chemicals affect our ecosystem and human health. OMICS breakthroughs are empoweringthe fields of environmental toxicology, chemical toxicity characterization, and health risk assessment. However, environmental OMICS is stillin the data generation and collection stage. Important data gaps in linking and/or integrating toxicity data with OMICS endpoints/profilesneed to be filled to enable understanding of the potential impacts of chemicals on human health and the environment. It is expected thatfuture environmental OMICS will focus more on real environmental issues and challenges such as the characterization of chemical mixturetoxicity, the identification of environmental and health biomarkers, and the development of innovative environmental OMICS approachesand assays. These innovative approaches and assays will inform chemical toxicity testing and prediction, ecological and health risk monitoringand assessment, and natural resource utilization in ways that maintain human health and protects the environment in a sustainable manner.

  • 17.
    Gustafsson, Mika
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Hörnquist, Michael
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Gene Expression Prediction by Soft Integration and the Elastic Net: Best Performance of the DREAM3 Gene Expression Challenge2010In: PLoS ONE, ISSN 1932-6203, Vol. 5, no 2, p. e9134-Article in journal (Refereed)
    Abstract [en]

    Background: To predict gene expressions is an important endeavour within computational systems biology. It can both be a way to explore how drugs affect the system, as well as providing a framework for finding which genes are interrelated in a certain process. A practical problem, however, is how to assess and discriminate among the various algorithms which have been developed for this purpose. Therefore, the DREAM project invited the year 2008 to a challenge for predicting gene expression values, and here we present the algorithm with best performance.

    Methodology/Principal Findings: We develop an algorithm by exploring various regression schemes with different model selection procedures. It turns out that the most effective scheme is based on least squares, with a penalty term of a recently developed form called the “elastic net”. Key components in the algorithm are the integration of expression data from other experimental conditions than those presented for the challenge and the utilization of transcription factor binding data for guiding the inference process towards known interactions. Of importance is also a cross-validation procedure where each form of external data is used only to the extent it increases the expected performance.

    Conclusions/Significance: Our algorithm proves both the possibility to extract information from large-scale expression data concerning prediction of gene levels, as well as the benefits of integrating different data sources for improving the inference. We believe the former is an important message to those still hesitating on the possibilities for computational approaches, while the latter is part of an important way forward for the future development of the field of computational systems biology.

  • 18.
    Hedberg, Lilia
    Linköping University, Department of Clinical and Experimental Medicine.
    Identification of obesity-associated SNPs in the human genome: Method development and implementation for SOLiD sequencing data analysis2010Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Over the last few years, genome-wide association studies (GWAS) have been used to identify numerous obesity associated SNPs in the human genome. By using linkage studies, candidate obesity genes have been identified. When SNPs in the first intron of FTO were found to be associated to BMI, it became the first gene to be linked to common obesity. In order to look for causative explanations behind the associated SNPs, a re-sequencing of FTO had been performed on the SOLiD sequencing platform. In-house candidate gene, SLCX, was also sequenced in order to evaluate a potential obesity association. The purpose of this project was to analyse the sequences and also to evaluate the quality of the SOLiD sequencing. A part of the project consisted in performing PCRs and selecting genomic regions for future sequencing projects. I developed and implemented a sequence analysis strategy to identify obesity associated SNPs. I found 39 obesity-linked SNPs in FTO, a majority of which were located in introns 1 and 8. I also identified 3 associated intronic SNPs in SLCX. I found that the SOLiD sequencing coverage varies between non-repetitive and repetitive genomic regions, and that it is highest near amplicon ends. Interestingly, coverage varies significantly between different amplicons even after repetitive sequences have been removed, which indicates that it is affected by features inherent to the sequence. Still, the observed allele frequencies for known SNPs were highly correlated with the SNP frequencies documented in HapMap. In conclusion, I verify that SNPs in FTO are associated with obesity and also identify a previously unassociated gene, SLCX, as a potential obesity gene. Re-sequencing of genomic regions on the SOLiD platform was proven to be successful for SNP identification, although the difference in sequencing coverage might be problematic.

  • 19.
    Hyvönen, Martin
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics.
    Protein-Protein Docking Using Starting Points Based On Structural Homology2015Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Protein-protein interactions build large networks which are essential in understanding complex diseases. Due to limitations of experimental methodology there are problems with large amounts of false negative and positive interactions; and a large gap in the amount of known interactions and structurally determined interactions. By using computational methods these problems can be alleviated.

    In this thesis the quality of a newly developed pipeline (InterPred) were investigated for its ability to generate coarse interaction models and score them. This ability was investigated by performing docking experiments in Rosetta on models generated in InterPred.

    The results suggest that InterPred is highly successful in generating good starting points for docking proteins in silico and to distinguish the quality of models.

  • 20.
    Hörnquist, Michael
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Gustafsson, Mika
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Stability and Flexibility from a System Analysis of Gene RegulatoryNetworks Based on Ordinary Differential Equations2011In: The Open Bioinformatics Journal, ISSN 1875-0362, Vol. 5, p. 26-33Article in journal (Refereed)
    Abstract [en]

    The inference of large-scale gene regulatory networks from high-throughput data sets has revealed a diverse picture of only partially overlapping descriptions. Nevertheless, several properties in the organization of these networks are recurrent, such as hubs, a modular structure and certain motifs. Several authors have recently claimed cell systems to be stable against perturbations and random errors, but still able to rapidly switch between different states from specific stimuli. Since inferred mathematical models of large-scale systems need to be extremely simple to avoid overfitting, these two features are hard to attain simultaneously for a model. Here we review and discuss possible measures of how system stability and flexibility may be manifested and measured for linearized models based on systems of ordinary differential equations. Furthermore, we review how the network properties mentioned above together with the nature of the interactions contribute to these systems level properties. It turns out that the presence of repressed hubs, together with other phenomena of topological nature such as motifs and modules, contribute to the overall stability and/or flexibility of the model.

  • 21.
    Janzén, David
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Standard two-stage and Nonlinear mixed effect modelling for determination of cell-to-cell variation of transport parameters in Saccharomyces cerevisiae2012Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The interest for cell-to-cell variation has in recent years increased in a steady pace. Several studies have shown that a large portion of the observed variation in the nature originates from the fact that all biochemical reactions are in some respect stochastic. Interestingly, nature has evolved highly advanced frameworks specialized in dealing with stochasticity in order to still be able to produce the delicate signalling pathways that are present in even very simple single-cell organisms.

    Such a simple organism is Saccharomyces cerevisiae, which is the organism that has been studied in this thesis. More particulary, the distribution of the transport rate in S. cerevisiae has been studied by a mathematical modelling approach. It is shown that a two-compartment model can adequately describe the flow of a yellow fluorescent protein (YFP) between the cytosol and the nucleus. A profile likelihood (PLH) analysis shows that the parameters in the two-compartment model are identifiable and well-defined under the experimental data of YFP. Furthermore, the result from this model shows that the distribution of the transport rates in the 80 studied cells is lognormal. Also, in contradiction to prior beliefs, no significant difference between recently divided mother and daughter cells in terms of transport rates of YFP is to be seen. The modelling is performed by using both standard two-stage(STS) and nonlinear mixed effect model (NONMEM).

    A methodological comparison between the two very different mathematical STS and NONMEM is also presented. STS is today the conventional approach in studies of cell-to-cell variation. However, in this thesis it is shown that NONMEM, which has originally been developed for population pharmacokinetic/ pharmacodynamic (PK/PD) studies, is at least as good, or in some cases even a better approach than STS in studies of cell-to-cell variation.

    Finally, a new approach in studies of cell-to-cell variation is suggested that involves a combination of STS, NONMEM and PLH. In particular, it is shown that this combination of different methods would be especially useful if the data is sparse. By applying this combination of methods, the uncertainty in the estimation of the variability could be greatly reduced.

  • 22.
    Johansson, Mikaela
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, Faculty of Science & Engineering.
    Metaproteogenomics-guided enzyme discovery: Targeted identification of novel proteases in microbial communities2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Industrial biotechnology is a large and growing industry as it is part of establishing a “greener” and more sustainable bioeconomy-based society. Using enzymes as biocatalysts is a viable alternative to chemicals and energy intense industrial processes and is en route to a more sustainable industry. Enzymes have been used in different areas for ages and are today used in many industrial processes such as biofuels production, food industry, tanning, chemical synthesis, pharmaceuticals etc. Enzymes are today a billion-dollar industry in itself and the demand for novel catalysts for various present and future processes of renewable resources are high and perfectly in line with converting to a more sustainable society.

    Most enzymes used in industry today have been identified from isolated and pure cultured microorganisms with identified desirable traits and enzymatic capacities. However, it is known that less than 1% of all microorganisms can be can be obtained in pure cultures. Thus, if we were to rely solely on pure culturing, this would leave the 99% of the microorganisms that constitutes the “microbial dark matter” uninvestigated for their potential in coding for and producing valuable novel enzymes. Therefore, to investigate these “unculturable” microorganisms for novel and valuable enzymes, pure-culture independent methods are needed.

    During the last two decades there has been a fast and extensive development in techniques and methods applicable for this purpose. Especially important has been the advancements made in mass spectrometry for protein identification and next generation sequencing of DNA. With these technical developments new research fields of proteomics and genomics have been developed, by which the complete protein complement of cells (the proteome) and all genes (the genome) of organisms can be investigated. When these techniques are applied to microbial communities these fields of research are known as meta-proteomics and meta-genomics.

    However, when applied to complex microbial communities, difficulties different from those encountered in their original usage for analysis of single multicellular organisms or cell linages arises, and when used independently both methods have their own limitations and bottlenecks. In addition, both metaproteomics and metagenomics are largely non-targeting techniques. Thus, if the purpose is still to - somewhat contradictory – use these non-targeting methods for targeted identification of novel enzymes with certain desired activities and properties from within microbial communities, special measures need to be taken.

    The work presented in this thesis describes the development of a method that combines

    metaproteomics and metagenomics (i.e. metaproteogenomics) for the targeted discovery of novel enzymes with desired activities, and their correct coding genes, from within microbial communities. Thus, what is described is a method that can be used to circumvent the pure-culturing problem so that a much larger fraction of the microbial dark matter can be specifically investigated for the identification of novel valuable enzymes.

    List of papers
    1. Applying theories of microbial metabolism for induction of targeted enzyme activity in a methanogenic microbial community at a metabolic steady state
    Open this publication in new window or tab >>Applying theories of microbial metabolism for induction of targeted enzyme activity in a methanogenic microbial community at a metabolic steady state
    2016 (English)In: Applied Microbiology and Biotechnology, ISSN 0175-7598, E-ISSN 1432-0614, Vol. 100, no 18, p. 7989-8002Article in journal (Refereed) Published
    Abstract [en]

    Novel enzymes that are stable in diverse conditions are intensively sought because they offer major potential advantages in industrial biotechnology, and microorganisms in extreme environments are key sources of such enzymes. However, most potentially valuable enzymes are currently inaccessible due to the pure culturing problem of microorganisms. Novel metagenomic and metaproteomic techniques that circumvent the need for pure cultures have theoretically provided possibilities to identify all genes and all proteins in microbial communities, but these techniques have not been widely used to directly identify specific enzymes because they generate vast amounts of extraneous data. In a first step towards developing a metaproteomic approach to pinpoint targeted extracellular hydrolytic enzymes of choice in microbial communities, we have generated and analyzed the necessary conditions for such an approach by the use of a methanogenic microbial community maintained on a chemically defined medium. The results show that a metabolic steady state of the microbial community could be reached, at which the expression of the targeted hydrolytic enzymes were suppressed, and that upon enzyme induction a distinct increase in the targeted enzyme expression was obtained. Furthermore, no cross talk in expression was detected between the two focal types of enzyme activities under their respective inductive conditions. Thus, the described approach should be useful to generate ideal samples, collected before and after selective induction, in controlled microbial communities to clearly discriminate between constituently expressed proteins and extracellular hydrolytic enzymes that are specifically induced, thereby reducing the analysis to only those proteins that are distinctively up-regulated.

    Place, publisher, year, edition, pages
    Springer, 2016
    Keywords
    Microbial community; Enzyme discovery; Metaproteomics; Biogas; Cellulase; Protease
    National Category
    Microbiology
    Identifiers
    urn:nbn:se:liu:diva-131888 (URN)10.1007/s00253-016-7547-z (DOI)000382008000017 ()27115757 (PubMedID)
    Note

    Funding Agencies|Swedish Research Council [621-2009-4150]; InZymes Biotech AB

    Available from: 2016-10-13 Created: 2016-10-11 Last updated: 2018-05-15
    2. Assessment of sample preparation methods for metaproteomics of extracellular proteins
    Open this publication in new window or tab >>Assessment of sample preparation methods for metaproteomics of extracellular proteins
    2017 (English)In: Analytical Biochemistry, ISSN 0003-2697, E-ISSN 1096-0309, Vol. 516, p. 23-36Article in journal (Refereed) Published
    Abstract [en]

    Enzyme discovery in individual strains of microorganisms is compromised by the limitations of pure culturing. In principle, metaproteomics allows for fractionation and study of different parts of the protein complement but has hitherto mainly been used to identify intracellular proteins. However, the extracellular environment is also expected to comprise a wealth of information regarding important proteins. An absolute requirement for metaproteomic studies of protein expression, and irrespective of downstream methods for analysis, is that sample preparation methods provide clean, concentrated and representative samples of the protein complement. A battery of methods for concentration, extraction, precipitation and resolubilization of proteins in the extracellular environment of a constructed microbial community was assessed by means of 2D gel electrophoresis and image analysis to elucidate whether it is possible to make the extracellular protein complement available for metaproteomic analysis. Most methods failed to provide pure samples and therefore negatively influenced protein gel migration and gel background clarity. However, one direct precipitation method (TCA-DOC/acetone) and one extraction/precipitation method (phenol/methanol) provided complementary high quality 2D gels that allowed for high spot detection ability and thereby also spot detection of less abundant extracellular proteins.

    Place, publisher, year, edition, pages
    Elsevier, 2017
    Keywords
    Enzyme discovery, Microbial community, Metaproteome, Extracellular, Sample preparation, 2D gel electrophoresis
    National Category
    Analytical Chemistry Biocatalysis and Enzyme Technology
    Identifiers
    urn:nbn:se:liu:diva-132902 (URN)10.1016/j.ab.2016.10.008 (DOI)000388056800005 ()27742212 (PubMedID)
    Funder
    Swedish Research Council, 621-2009-4150
    Note

    Funding agencies: Swedish Research Council [621-2009-4150]; Tekniska Verken i Linkoping AB; InZymes Biotech AB

    Available from: 2016-12-01 Created: 2016-12-01 Last updated: 2018-05-15Bibliographically approved
  • 23.
    Johansson, Rikard
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Model-Based Hypothesis Testing in Biomedicine: How Systems Biology Can Drive the Growth of Scientific Knowledge2017Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The utilization of mathematical tools within biology and medicine has traditionally been less widespread compared to other hard sciences, such as physics and chemistry. However, an increased need for tools such as data processing, bioinformatics, statistics, and mathematical modeling, have emerged due to advancements during the last decades. These advancements are partly due to the development of high-throughput experimental procedures and techniques, which produce ever increasing amounts of data. For all aspects of biology and medicine, these data reveal a high level of inter-connectivity between components, which operate on many levels of control, and with multiple feedbacks both between and within each level of control. However, the availability of these large-scale data is not synonymous to a detailed mechanistic understanding of the underlying system. Rather, a mechanistic understanding is gained first when we construct a hypothesis, and test its predictions experimentally. Identifying interesting predictions that are quantitative in nature, generally requires mathematical modeling. This, in turn, requires that the studied system can be formulated into a mathematical model, such as a series of ordinary differential equations, where different hypotheses can be expressed as precise mathematical expressions that influence the output of the model.

    Within specific sub-domains of biology, the utilization of mathematical models have had a long tradition, such as the modeling done on electrophysiology by Hodgkin and Huxley in the 1950s. However, it is only in recent years, with the arrival of the field known as systems biology that mathematical modeling has become more commonplace. The somewhat slow adaptation of mathematical modeling in biology is partly due to historical differences in training and terminology, as well as in a lack of awareness of showcases illustrating how modeling can make a difference, or even be required, for a correct analysis of the experimental data.

    In this work, I provide such showcases by demonstrating the universality and applicability of mathematical modeling and hypothesis testing in three disparate biological systems. In Paper II, we demonstrate how mathematical modeling is necessary for the correct interpretation and analysis of dominant negative inhibition data in insulin signaling in primary human adipocytes. In Paper III, we use modeling to determine transport rates across the nuclear membrane in yeast cells, and we show how this technique is superior to traditional curve-fitting methods. We also demonstrate the issue of population heterogeneity and the need to account for individual differences between cells and the population at large. In Paper IV, we use mathematical modeling to reject three hypotheses concerning the phenomenon of facilitation in pyramidal nerve cells in rats and mice. We also show how one surviving hypothesis can explain all data and adequately describe independent validation data. Finally, in Paper I, we develop a method for model selection and discrimination using parametric bootstrapping and the combination of several different empirical distributions of traditional statistical tests. We show how the empirical log-likelihood ratio test is the best combination of two tests and how this can be used, not only for model selection, but also for model discrimination.

    In conclusion, mathematical modeling is a valuable tool for analyzing data and testing biological hypotheses, regardless of the underlying biological system. Further development of modeling methods and applications are therefore important since these will in all likelihood play a crucial role in all future aspects of biology and medicine, especially in dealing with the burden of increasing amounts of data that is made available with new experimental techniques.

    List of papers
    1. Combining test statistics and models in bootstrapped model rejection: it is a balancing act
    Open this publication in new window or tab >>Combining test statistics and models in bootstrapped model rejection: it is a balancing act
    2014 (English)In: BMC Systems Biology, ISSN 1752-0509, E-ISSN 1752-0509, Vol. 8, no 46Article in journal (Refereed) Published
    Abstract [en]

    Background: Model rejections lie at the heart of systems biology, since they provide conclusive statements: that the corresponding mechanistic assumptions do not serve as valid explanations for the experimental data. Rejections are usually done usinge.g. the chi-square test (χ2) or the Durbin-Watson test (DW). Analytical formulas for the corresponding distributions rely on assumptions that typically are not fulfilled. This problem is partly alleviated by the usage of bootstrapping, a computationally heavy approach to calculate an empirical distribution. Bootstrapping also allows for a natural extension to estimation of joint distributions, but this feature has so far been little exploited.

    Results: We herein show that simplistic combinations of bootstrapped tests, like the max or min of the individual p-values, give inconsistent, i.e. overly conservative or liberal, results. A new two-dimensional (2D) approach based on parametric bootstrapping, on the other hand, is found both consistent and with a higher power than the individual tests, when tested on static and dynamic examples where the truth is known. In the same examples, the most superior test is a 2D χ2 vs χ2, where the second χ2-value comes from an additional help model, and its ability to describe bootstraps from the tested model. This superiority is lost if the help model is too simple, or too flexible. If a useful help model is found, the most powerful approach is the bootstrapped log-likelihood ratio (LHR). We show that this is because the LHR is one-dimensional, because the second dimension comes at a cost, and because LHR has retained most of the crucial information in the 2D distribution. These approaches statistically resolve a previously published rejection example for the first time.

    Conclusions: We have shown how to, and how not to, combine tests in a bootstrap setting, when the combinatio is advantageous, and when it is advantageous to include a second model. These results also provide a deeper insight into the original motivation for formulating the LHR, for the more general setting of nonlinear and non-nested models. These insights are valuable in cases when accuracy and power, rather than computational speed, are prioritized.

    Place, publisher, year, edition, pages
    BioMed Central, 2014
    Keywords
    Model rejection, Bootstrapping, Combining information, 2D, Insulin signaling, Model Mimicry, Likelihood ratio
    National Category
    Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)
    Identifiers
    urn:nbn:se:liu:diva-106427 (URN)10.1186/1752-0509-8-46 (DOI)000335472800001 ()24742065 (PubMedID)
    Available from: 2014-05-07 Created: 2014-05-07 Last updated: 2017-12-05Bibliographically approved
    2. Dominant negative inhibition data should be analyzed using mathematical modeling - re-interpreting data from insulin signaling.
    Open this publication in new window or tab >>Dominant negative inhibition data should be analyzed using mathematical modeling - re-interpreting data from insulin signaling.
    Show others...
    2015 (English)In: The FEBS Journal, ISSN 1742-464X, E-ISSN 1742-4658, Vol. 282, no 4, p. 788-802Article in journal (Refereed) Published
    Abstract [en]

    As our ability to measure the complexity of intracellular networks has evolved, it has become increasingly clear that we need new methods for data analysis: methods involving mathematical modeling. Nevertheless, it is still uncontroversial to publish and interpret experimental results without a model-based proof that the reasoning is correct. In the present study, we argue that this attitude probably needs to change in the future. We illustrate this need for modeling by considering the common experimental technique of using dominant-negative constructs. More specifically, we consider published time-series and dose-response data which previously have been used to argue that the protein S6 kinase does not phosphorylate insulin receptor substrate-1 at a specific serine residue. Using a presented general approach to interpret such data, we now demonstrate that the given dominant-negative data are not conclusive (i.e. that in the absence of other proofs, S6 kinase still may be the kinase). Using simulations with uncertainty analysis and analytical solutions, we show that an alternative explanation is centered around depletion of substrate, which can be tested experimentally. This analysis thus illustrates both the necessity and the benefits of using mathematical modeling to fully understand the implications of biological data, even for a small system and relatively simple data.

    Keywords
    insulin signalling, dominant negative data, mathematical modelling
    National Category
    Bioinformatics and Systems Biology
    Identifiers
    urn:nbn:se:liu:diva-115805 (URN)10.1111/febs.13182 (DOI)000350288300011 ()25546185 (PubMedID)
    Funder
    Swedish Research Council
    Available from: 2015-03-20 Created: 2015-03-20 Last updated: 2017-12-04
    3. Quantification of nuclear transport in single cells
    Open this publication in new window or tab >>Quantification of nuclear transport in single cells
    Show others...
    2014 (English)Other (Other academic)
    Abstract [en]

    Regulation of nuclear transport is a key cellular function involved in many central processes, such as gene expression regulation and signal transduction. Rates of protein movement between cellular compartments can be measured by FRAP. However, no standard and reliable methods to calculate transport rates exist. Here we introduce a method to extract import and export rates, suitable for noisy single cell data. This method consists of microscope procedures, routines for data processing, an ODE model to fit to the data, and algorithms for parameter optimization and error estimation. Using this method, we successfully measured import and export rates in individual yeast. For YFP, average transport rates were 0.15 sec-1. We estimated confidence intervals for these parameters through likelihood profile analysis. We found large cell-to-cell variation (CV = 0.79) in these rates, suggesting a hitherto unknown source of cellular heterogeneity. Given the passive nature of YFP diffusion, we attribute this variation to large differences among cells in the number or quality of nuclear pores. Owing to its broad applicability and sensitivity, this method will allow deeper mechanistic insight into nuclear transport processes and into the largely unstudied cell-to-cell variation in kinetic rates.

    Place, publisher, year, pages
    New York, USA: Cold Spring Harbor Laboratory Press (CSHL), 2014. p. 23
    Series
    bioRxiv ; 001768
    National Category
    Bioinformatics and Systems Biology
    Identifiers
    urn:nbn:se:liu:diva-141807 (URN)10.1101/001768 (DOI)
    Note

    Article id 001768.

    Available from: 2017-10-06 Created: 2017-10-06 Last updated: 2018-05-21Bibliographically approved
  • 24.
    Johansson, Rikard
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Kreutz, Clemens
    University of Freiburg, Department of Physics.
    Bartolomé Rodríguez, M. M.
    University of Freiburg, Department of Medicine.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Timmer, Jens
    University of Freiburg, Department of Physics.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Elucidating mechanisms of early insulin signaling in primary adipocytes and hepatocytes: a joint systems biology effort2009Conference paper (Other academic)
    Abstract [en]

    Type II diabetes is one of the most common diseases afflicting people today. Understanding how this disease works, not only on a cellular level and between different organs and tissues, but also how it affects whole body level homeostasis is crucial for enhancement of its treatment. We use model-bases analysis as a tool for distinguishing different biological hypothesis on the system behavior.

    The Insulin Receptor (IR), is located in the cell membrane as a dimer, and thus has the potential two bind two different insulin molecules. It can also undergo a series of phosphorylations, as well as having the ability to become internalized, and thus be removed from the cell’s censing area. However, it can then be recycled back to the membrane again. The major target of IR is the Insulin Receptor Substrate 1 (IRS1). IRS1 in turn mediates the signal further downstream through Protein Kinase B (PBK) and mammalian Target of Rapamycin (mTOR).  In adipocytes the end result is the translocation of internal vesicles containing Glucose Transporters (GLUT4) to the membrane, thus increasing the uptake of glucose. The liver, on the other hand, responds by down regulating the endogenous glucose production.

    The activity of IRS1 is determined by its phospho-tyrosine composition. This in turn is regulated by at least two serine-phosphorylations, on ser307 and ser312. The serine levels of this protein are regulated by downstream kinases, of which only one is known, S6K. The ser307 phosphorylation appears to allow for a short term positive feedback while the ser312 phosphorylation has the dynamics of a more long term negative feedback.

    The overall dynamics of the IRS1 tyrosine phosphorylation is a mirror of that of the Insulin Receptor. They both have a quick response to insulin within minutes, manifested as a high overshoot before declining to a steady state level. The overshoot behavior of this system can be explained either by a downstream negative feedback, or by having an advanced internalization and recycling model. Several hypotheses of the negative feedback mechanisms necessary to allow for the receptor to adopt such a behavior have previously been rejected by us. So has the hypothesis of internalization (unpublished data). The internalized Insulin Receptors can account for only a small fraction of the total amount of receptors, it however seems to be necessary for its own down regulation, since without it the overshoot behavior disappears.

    The complexity of this system is immense and hence we keep to as minimal models as possible, only considering adding complexity to the system when data indicates so, or when a simpler model structure has been rejected. We model the system with a series of Ordinary Differential Equations (ODEs), optimize and estimate the parameters of a given model structure with the Systems Biology Toolbox (SBTB) and reject, or fail to reject, models based on their statistical agreement with our data. We search the entire approximated parameter space for a sample of all acceptable parameter values for any given parameter. We then look for commonalities shared between model simulations of all parameter sets in the sample. That is, a behavior of e.g. a state in the model that has to be above a certain threshold for it to be able to explain the data, while other states might be of arbitrary sizes. If we find such a commonality, we call it a core prediction. Assuming your data is correct and your analysis thorough, a Core Prediction has the same strength as a model rejection. The common aspect, shared between all acceptable parameter etc, is something that has to be true, no matter how much more data you acquire. One such core prediction, which led to the rejection of the internalization hypothesis, was that the amount of internalized IR had to be above 80% of the total receptor pool.  We subsequently rejected this experimentally.

  • 25.
    Johnsson, Anna
    Linköping University, Department of Physics, Chemistry and Biology, Biotechnology .
    Mining for Lung Cancer Biomarkers in Plasma Metabolomics Data2010Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Lung cancer is the cancer form that has the highest mortality worldwide and inaddition the survival of lung cancer is very low. Only 15% of the patients are alivefive years from set diagnosis. More research is needed to understand the biologyof lung cancer and thus make it possible to discover the disease at an early stage.Early diagnosis leads to an increased chance of survival. In this thesis 179 lungcancer- and 116 control samples of blood serum were analyzed for identificationof metabolomic biomarkers. The control samples were derived from patients withbenign lung diseases.Data was gained from GC/TOF-MS analysis and analyzed with the help ofthe multivariate analysis methods PCA and OPLS/OPLS-DA. In this thesis it isinvestigated how to pre-treat and analyze the data in the best way in order todiscover biomarkers. One part of the aim was to give directions for how to selectsamples from a biobank for further biological validation of suspected biomarkers.Models for different stages of lung cancer versus control samples were computedand validated. The most influencing metabolites in the models were selected andconfoundings with other clinical characteristics like gender and hemoglobin levelswere studied. 13 lung cancer biomakers were identified and validated by raw dataand new OPLS models based solely upon the biomarkers.In summary the identified biomarkers are able to separate fairly good betweencontrol samples and late lung cancer, but are poor for separation of early lungcancer from control samples. The recommendation is to select controls and latelung cancer samples from the biobank for further confirmation of the biomarkers.NyckelordLung cancer is the cancer form that has the highest mortality worldwide and inaddition the survival of lung cancer is very low. Only 15% of the patients are alivefive years from set diagnosis. More research is needed to understand the biologyof lung cancer and thus make it possible to discover the disease at an early stage.Early diagnosis leads to an increased chance of survival. In this thesis 179 lungcancer- and 116 control samples of blood serum were analyzed for identificationof metabolomic biomarkers. The control samples were derived from patients withbenign lung diseases.Data was gained from GC/TOF-MS analysis and analyzed with the help ofthe multivariate analysis methods PCA and OPLS/OPLS-DA. In this thesis it isinvestigated how to pre-treat and analyze the data in the best way in order todiscover biomarkers. One part of the aim was to give directions for how to selectsamples from a biobank for further biological validation of suspected biomarkers.Models for different stages of lung cancer versus control samples were computedand validated. The most influencing metabolites in the models were selected andconfoundings with other clinical characteristics like gender and hemoglobin levelswere studied. 13 lung cancer biomakers were identified and validated by raw dataand new OPLS models based solely upon the biomarkers.In summary the identified biomarkers are able to separate fairly good betweencontrol samples and late lung cancer, but are poor for separation of early lungcancer from control samples. The recommendation is to select controls and latelung cancer samples from the biobank for further confirmation of the biomarkers.Nyckelord

  • 26.
    Jullesson, David
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology. Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Johansson, Rikard
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology. Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Rohini Rajan, Meenu
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Dominant negative inhibition data should be analyzed using mathematical modeling - re-interpreting data from insulin signaling.2015In: The FEBS Journal, ISSN 1742-464X, E-ISSN 1742-4658, Vol. 282, no 4, p. 788-802Article in journal (Refereed)
    Abstract [en]

    As our ability to measure the complexity of intracellular networks has evolved, it has become increasingly clear that we need new methods for data analysis: methods involving mathematical modeling. Nevertheless, it is still uncontroversial to publish and interpret experimental results without a model-based proof that the reasoning is correct. In the present study, we argue that this attitude probably needs to change in the future. We illustrate this need for modeling by considering the common experimental technique of using dominant-negative constructs. More specifically, we consider published time-series and dose-response data which previously have been used to argue that the protein S6 kinase does not phosphorylate insulin receptor substrate-1 at a specific serine residue. Using a presented general approach to interpret such data, we now demonstrate that the given dominant-negative data are not conclusive (i.e. that in the absence of other proofs, S6 kinase still may be the kinase). Using simulations with uncertainty analysis and analytical solutions, we show that an alternative explanation is centered around depletion of substrate, which can be tested experimentally. This analysis thus illustrates both the necessity and the benefits of using mathematical modeling to fully understand the implications of biological data, even for a small system and relatively simple data.

  • 27.
    Kallberg, Yvonne
    et al.
    Karolinska Institutet, Stockholm, Sweden.
    Segerstolpe, Åsa
    Stockholm University, Sweden.
    Lackman, Fredrik
    Stockholm University, Sweden.
    Persson, Bengt
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, The Institute of Technology.
    Wieslander, Lars
    Stockholm University, Sweden.
    Evolutionary Conservation of the Ribosomal Biogenesis Factor Rbm19/Mrd1: Implications for Function2012In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 7, no 9Article in journal (Refereed)
    Abstract [en]

    Ribosome biogenesis in eukaryotes requires coordinated folding and assembly of a pre-rRNA into sequential pre-rRNA-protein complexes in which chemical modifications and RNA cleavages occur. These processes require many small nucleolar RNAs (snoRNAs) and proteins. Rbm19/Mrd1 is one such protein that is built from multiple RNA-binding domains (RBDs). We find that Rbm19/Mrd1 with five RBDs is present in all branches of the eukaryotic phylogenetic tree, except in animals and Choanoflagellates, that instead have a version with six RBDs and Microsporidia which have a minimal Rbm19/Mrd1 protein with four RBDs. Rbm19/Mrd1 therefore evolved as a multi-RBD protein very early in eukaryotes. The linkers between the RBDs have conserved properties; they are disordered, except for linker 3, and position the RBDs at conserved relative distances from each other. All but one of the RBDs have conserved properties for RNA-binding and each RBD has a specific consensus sequence and a conserved position in the protein, suggesting a functionally important modular design. The patterns of evolutionary conservation provide information for experimental analyses of the function of Rbm19/Mrd1. In vivo mutational analysis confirmed that a highly conserved loop 5-β4-strand in RBD6 is essential for function.

  • 28.
    Klasson, Filip
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Väyrynen, Patrik
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Development of an API for creating and editing openEHR archetypes2009Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    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.

  • 29.
    Kuruvilla, Jacob
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Proteomics as a multifaceted tool in medicine and environmental assessment2017Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Proteomics is evolving as a multi-faceted tool for addressing various biochemical and biomedical queries in the field of scientific research. This involves various stages, ranging from sample preparation to data analysis and biological interpretation. Sample preparation involves isolating proteins from the sample source, purifying and digesting them to initiate shotgun proteomics. Shotgun proteomics identifies proteins by bottom-up proteomic approaches where proteins are identified from the fragmentation spectra of their own peptides.

    Paper I: deals with the simplification of functional characterization for nanoparticles intended for use in biomedicine. Proteomics was constructive in differentiating and semi-quantifying the surface of protein corona. This could be beneficial in predicting the interactions between nanoparticles and a biological entity like the cell or a receptor protein and provide initial valuable information related to targeting, uptake and safety.

    Paper II: deals with understanding effects of TiO2 nanoparticles on endothelial cells. A combinatorial approach, involving transcriptomics and proteomics was used to identify aberrations in the permeability and integrity of endothelial cells and tissues. Our study also investigated the correlation of size and how they motivated a differential cellular response. In case of intravenous entry for nanoparticles in targeted drug delivery systems, endothelial cells are the first barrier encountered by these drug carriers. This evaluation involving endothelial cell response could be very instrumental during the designing of NP based drug delivery systems.

    Paper III: Pharmaceuticals and its metabolites could be very hazardous, especially if its disposal is not managed properly. Since water bodies are the ultimate sink, these chemicals could end up there, culminating in toxicity and other ‘mixture effects’ in combination with other factors. To evaluate the effects of the pharmaceutical, propranolol and climatic factors like low salinity conditions, a microcosm exposure was designed and shotgun proteomics helped understand its impact on mussel gills. In this study too, a combination of transcriptomics and proteomics unveiled molecular mechanisms altered in response to stressors, both individually and in combination.

    Paper IV: An interplay of various factors like EBF1 and PAX5 determines B-cell lineage and commitment. This might have been materialized by direct and transient proteinprotein interactions. A unique method called BioID helped screen relevant interactions in living cells by the application of a promiscuous biotin ligase enzyme capable of tagging proteins through biotinylation based on a proximity radius. Biotinylation of endogenous proteins enabled their selective isolation by exploiting the high affinity of biotin and streptavidin on streptavidin coated agarose beads, leading to their identification by mass spectrometry. The biotinylated proteins were potential candidate interactors of EBF1 and PAX5, which were later confirmed by sequencing techniques like ChIP-Seq, ATAC seq, and visualization techniques like proximity ligation assay (PLA).

    List of papers
    1. Surface proteomics on nanoparticles, a step to simplify the rapid prototyping of nanoparticles
    Open this publication in new window or tab >>Surface proteomics on nanoparticles, a step to simplify the rapid prototyping of nanoparticles
    2017 (English)In: Nanoscale Horizons, ISSN 2055-6756, no 1, p. 55-64Article in journal (Refereed) Published
    Abstract [en]

    Engineered nanoparticles for biomedical applications requireincreasing effectiveness in targeting specific cells while preservingnon-target cell’s safety. We developed a surface proteomicsmethod for a rapid and systematic analysis of the interphasebetween the nanoparticle protein corona and the targeting cellsthat could implement the rapid prototyping of nanomedicines.Native nanoparticles entering in a protein-rich liquid mediaquickly form a macromolecular structure called protein corona.This protein structure defines the physical interaction betweennanoparticles and target cells. The surface proteins compose thefirst line of interaction between this macromolecular structureand the cell surface of a target cell. We demonstrated that SUSTU(SUrface proteomics, Safety, Targeting, Uptake) provides aqualitative and quantitative analysis from the protein coronasurface. With SUSTU, the spatial dynamics of the protein coronasurface can be studied. Data from SUSTU would ascertain thenanoparticle functionalized groups exposed at destiny that couldcircumvent preliminary in vitro experiments. Therefore thismethod could implement the analysis of nanoparticle targetingand uptake capability and could be integrated into a rapidprototyping strategy which is a major challenge in nanomaterialscience. Data are available via ProteomeXchange with identifierPXD004636.

    Place, publisher, year, edition, pages
    Royal Society of Chemistry, 2017
    Keywords
    nanoparticle, protein corona, mass spectrometry, surface proteomics, targeting, rapid prototyping, nanomedicine
    National Category
    Biochemistry and Molecular Biology
    Identifiers
    urn:nbn:se:liu:diva-132406 (URN)10.1039/c6nh00162a (DOI)000391450000006 ()
    Projects
    Nanoimpact; nanoparticles and rapid prototyping
    Available from: 2016-11-09 Created: 2016-11-09 Last updated: 2018-04-17Bibliographically approved
    2. Shotgun proteomics to unravel marine mussel (Mytilus edulis) response to long-term exposure to low salinity and propranolol in a Baltic Sea microcosm
    Open this publication in new window or tab >>Shotgun proteomics to unravel marine mussel (Mytilus edulis) response to long-term exposure to low salinity and propranolol in a Baltic Sea microcosm
    Show others...
    2016 (English)In: Journal of Proteomics, ISSN 1874-3919, E-ISSN 1876-7737, Vol. 137, p. 97-106Article in journal (Refereed) Published
    Abstract [en]

    Pharmaceuticals, among them the β-adrenoreceptor blocker propranolol, are an important group of environmental contaminants reported in European waters. Laboratory exposure to pharmaceuticals on marine species has been performed without considering the input of the ecosystem flow. To unravel the ecosystem response to long-term exposure to propranolol we have performed long-term exposure to propranolol and low salinity in microcosms. We applied shotgun proteomic analysis to gills of Mytilus edulis from those Baltic Sea microcosms and identified 2071 proteins with a proteogenomic strategy. The proteome profiling patterns from the 587 highly reproductive proteins among groups define salinity as a key factor in the mussel´s response to propranolol. Exposure at low salinity drives molecular mechanisms of adaptation based on a decrease in the abundance of several cytoskeletal proteins, signalling and intracellular membrane trafficking pathway combined with a response towards the maintenance of transcription and translation. The exposure to propranolol combined with low salinity modulates the expression of structural proteins including cilia functions and decrease the expression membrane protein transporters. This study reinforces the environment concerns of the impact of low salinity in combination with anthropogenic pollutants and anticipate critical physiological conditions for the survival of the blue mussel in the northern areas.

    Place, publisher, year, edition, pages
    Elsevier, 2016
    Keywords
    Mytilus edulis, shotgun proteomics, propranolol, low salinity, environmental monitoring, climate change
    National Category
    Biochemistry and Molecular Biology
    Identifiers
    urn:nbn:se:liu:diva-124213 (URN)10.1016/j.jprot.2016.01.010 (DOI)000374368800010 ()
    Funder
    Swedish Research Council
    Note

    Funding agencies: Swedish Research Council-Natural Science; VR-NT; Carl Trygger Foundation; Oscar and Lilli Lamms Minne Foundation; Angpanneforening Research Foundation; Magnus Bergsvall Foundation; IKERBASQUE; Basque Foundation for Science; VINNOVA; County Council of Oste

    Available from: 2016-01-22 Created: 2016-01-22 Last updated: 2017-11-30Bibliographically approved
  • 30.
    Lentini, Antonio
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences.
    Lagerwall, Cathrine
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences.
    Vikingsson, Svante
    Linköping University, Department of Medical and Health Sciences, Division of Drug Research. Linköping University, Faculty of Medicine and Health Sciences. Natl Board Forens Med, Dept Forens Genet and Forens Toxicol, Linkoping, Sweden.
    Mjoseng, Heidi K.
    Univ Edinburgh, Scotland.
    Douvlataniotis, Dimitrios Karolos
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences.
    Vogt, Hartmut
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, Department of Paediatrics in Linköping.
    Green, Henrik
    Linköping University, Department of Medical and Health Sciences, Division of Drug Research. Linköping University, Faculty of Medicine and Health Sciences. Natl Board Forens Med, Dept Forens Genet and Forens Toxicol, Linkoping, Sweden.
    Meehan, Richard R.
    Univ Edinburgh, Scotland.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Allergy Center.
    Nestor, Colm
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences.
    A reassessment of DNA-immunoprecipitation-based genomic profiling2018In: Nature Methods, ISSN 1548-7091, E-ISSN 1548-7105, Vol. 15, no 7, p. 499-+Article in journal (Refereed)
    Abstract [en]

    DNA immunoprecipitation followed by sequencing (DIP-seq) is a common enrichment method for profiling DNA modifications in mammalian genomes. However, the results of independent DIP-seq studies often show considerable variation between profiles of the same genome and between profiles obtained by alternative methods. Here we show that these differences are primarily due to the intrinsic affinity of IgG for short unmodified DNA repeats. This pervasive experimental error accounts for 50-99% of regions identified as enriched for DNA modifications in DIP-seq data. Correction of this error profoundly altered DNA-modification profiles for numerous cell types, including mouse embryonic stem cells, and subsequently revealed novel associations among DNA modifications, chromatin modifications and biological processes. We conclude that both matched input and IgG controls are essential in order for the results of DIP-based assays to be interpreted correctly, and that complementary, non-antibody-based techniques should be used to validate DIP-based findings to avoid further misinterpretation of genome-wide profiling data.

  • 31.
    Lingemark, Maria
    Linköping University, Department of Computer and Information Science.
    A Lexicon for Gene Normalization2009Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Researchers tend to use their own or favourite gene names in scientific literature, even though there are official names. Some names may even be used for more than one gene. This leads to problems with ambiguity when automatically mining biological literature. To disambiguate the gene names, gene normalization is used. In this thesis, we look into an existing gene normalization system, and develop a new method to find gene candidates for the ambiguous genes. For the new method a lexicon is created, using information about the gene names, symbols and synonyms from three different databases. The gene mention found in the scientific literature is used as input for a search in this lexicon, and all genes in the lexicon that match the mention are returned as gene candidates for that mention. These candidates are then used in the system's disambiguation step. Results show that the new method gives a better over all result from the system, with an increase in precision and a small decrease in recall.

  • 32.
    Lysholm, Fredrik
    Linköping University, Department of Physics, Chemistry and Biology.
    Structural characterization of overrepresented2008Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Background: Through the last decades vast amount of sequence information have been produced by various protein sequencing projects, which enables studies of sequential patterns. One of the bestknown efforts to chart short peptide sequences is the Prosite pattern data bank. While sequential patterns like those of Prosite have proved very useful for classifying protein families, functions etc. structural analysis may provide more information and possible crucial clues linked to protein folding. Today PDB, which is the main repository for protein structure, contains more than 50’000 entries which enables structural protein studies.

    Result: Strongly folded pentapeptides, defined as pentapeptides which retained a specific conformation in several significantly structurally different proteins, were studied out of PDB. Among these several groups were found. Possibly the most well defined is the “double Cys” pentapeptide group, with two amino acids in between (CXXCX|XCXXC) which were found to form backbone loops where the two Cysteine amino acids formed a possible Cys-Cys bridge. Other structural motifs were found both in helixes and in sheets like "ECSAM" and "TIKIW", respectively.

    Conclusion: There is much information to be extracted by structural analysis of pentapeptides and other oligopeptides. There is no doubt that some pentapeptides are more likely to obtain a specific fold than others and that there are many strongly folded pentapeptides. By combining the usage of such patterns in a protein folding model, such as the Hydrophobic-polar-model improvements in speed and accuracy can be obtained. Comparing structural conformations for important overrepresented pentapeptides can also help identify and refine both structural information data banks such as SCOP and sequential pattern data banks such as Prosite.

  • 33.
    Lysholm, Fredrik
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, The Institute of Technology.
    Wetterbom, Anna
    Karolinska Institutet, Stockholm.
    Lindau, Cecilia
    Karolinska Institutet, Stockholm.
    Darban, Hamid
    Karolinska Institutet, Stockholm.
    Bjerkner, Annelie
    Karolinska Institutet, Stockholm.
    Fahlander, Kristina
    Karolinska Institutet, Stockholm.
    Lindberg, A Michael
    Linnaeus University.
    Persson, Bengt
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, The Institute of Technology.
    Allander, Tobias
    Karolinska Institutet, Stockholm.
    Andersson, Bjorn
    Karolinska Institutet, Stockholm.
    Characterization of the Viral Microbiome in Patients with Severe Lower Respiratory Tract Infections, Using Metagenomic Sequencing2012In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 7, no 2Article in journal (Refereed)
    Abstract [en]

    The human respiratory tract is heavily exposed to microorganisms. Viral respiratory tract pathogens, like RSV, influenza and rhinoviruses cause major morbidity and mortality from respiratory tract disease. Furthermore, as viruses have limited means of transmission, viruses that cause pathogenicity in other tissues may be transmitted through the respiratory tract. It is therefore important to chart the human virome in this compartment. We have studied nasopharyngeal aspirate samples submitted to the Karolinska University Laboratory, Stockholm, Sweden from March 2004 to May 2005 for diagnosis of respiratory tract infections. We have used a metagenomic sequencing strategy to characterize viruses, as this provides the most unbiased view of the samples. Virus enrichment followed by 454 sequencing resulted in totally 703,790 reads and 110,931 of these were found to be of viral origin by using an automated classification pipeline. The snapshot of the respiratory tract virome of these 210 patients revealed 39 species and many more strains of viruses. Most of the viral sequences were classified into one of three major families; Paramyxoviridae, Picornaviridae or Orthomyxoviridae. The study also identified one novel type of Rhinovirus C, and identified a number of previously undescribed viral genetic fragments of unknown origin.

  • 34.
    Magnusson, Rasmus
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Mariotti, Guido
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Köpsén, Mattias
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Lövfors, William
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Gawel, Danuta
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences.
    Jornsten, Rebecka
    University of Gothenburg, Sweden.
    Linde, Joerg
    Hans Knoell Institute, Germany; Hans Knoell Institute, Germany.
    Nordling, Torbjorn
    National Cheng Kung University, Taiwan; Science Life Lab, Sweden.
    Nyman, Elin
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Schulze, Sylvie
    Hans Knoell Institute, Germany.
    Nestor, Colm
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences.
    Zhang, Hanmin
    Linköping University, Department of Physics, Chemistry and Biology. Linköping University, The Institute of Technology.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Allergy Center.
    Tjärnberg, Andreas
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Gustafsson, Mika
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    LASSIM-A network inference toolbox for genome-wide mechanistic modeling2017In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 13, no 6, article id e1005608Article in journal (Refereed)
    Abstract [en]

    Recent technological advancements have made time-resolved, quantitative, multi-omics data available for many model systems, which could be integrated for systems pharmacokinetic use. Here, we present large-scale simulation modeling (LASSIM), which is a novel mathematical tool for performing large-scale inference using mechanistically defined ordinary differential equations (ODE) for gene regulatory networks (GRNs). LASSIM integrates structural knowledge about regulatory interactions and non-linear equations with multiple steady state and dynamic response expression datasets. The rationale behind LASSIM is that biological GRNs can be simplified using a limited subset of core genes that are assumed to regulate all other gene transcription events in the network. The LASSIM method is implemented as a general-purpose toolbox using the PyGMO Python package to make the most of multicore computers and high performance clusters, and is available at https://gitlab.com/Gustafsson-lab/lassim. As a method, LASSIM works in two steps, where it first infers a non-linear ODE system of the pre-specified core gene expression. Second, LASSIM in parallel optimizes the parameters that model the regulation of peripheral genes by core system genes. We showed the usefulness of this method by applying LASSIM to infer a large-scale non-linear model of naive Th2 cell differentiation, made possible by integrating Th2 specific bindings, time-series together with six public and six novel siRNA-mediated knock-down experiments. ChIP-seq showed significant overlap for all tested transcription factors. Next, we performed novel time-series measurements of total T-cells during differentiation towards Th2 and verified that our LASSIM model could monitor those data significantly better than comparable models that used the same Th2 bindings. In summary, the LASSIM toolbox opens the door to a new type of model-based data analysis that combines the strengths of reliable mechanistic models with truly systems-level data. We demonstrate the power of this approach by inferring a mechanistically motivated, genome-wide model of the Th2 transcription regulatory system, which plays an important role in several immune related diseases.

  • 35.
    Niklasson, Markus
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, Faculty of Science & Engineering.
    Coding to cure: NMR and thermodynamic software applied to congenital heart disease research2017Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Regardless of scientific field computers have become pivotal tools for data analysis and the field of structural biology is not an exception. Here, computers are the main tools used for tasks including structural calculations of proteins, spectral analysis of nuclear magnetic resonance (NMR) spectroscopy data and fitting mathematical models to data. As results reported in papers heavily rely on software and scripts it is of key importance that the employed computational methods are robust and yield reliable results. However, as many scientific fields are niched and possess a small potential user base the task to develop necessary software often falls on researchers themselves. This can cause divergence when comparing data analyzed by different measures or by using subpar methods. Therein lies the importance of development of accurate computational methods that can be employed by the scientific community.

    The main theme of this thesis is software development applied to structural biology, with the purpose to aid research in this scientific field by speeding up the process of data analysis as well as to ensure that acquired data is properly analyzed. Among the original results of this thesis are three user-friendly software:

    COMPASS - a resonance assignment software for NMR spectroscopy data capable of analyzing chemical shifts and providing the user with suggestions to potential resonance assignments, based on a meticulous database comparison.

    CDpal - a curve fitting software used to fit thermal and chemical denaturation data of proteins acquired by circular dichroism (CD) spectroscopy or fluorescence spectroscopy.

    PINT - a line shape fitting and downstream analysis software forNMRspectroscopy data, designed with the important purpose to easily and accurately fit peaks in NMR spectra and extract parameters such as relaxation rates, intensities and volumes of peaks.

    This thesis also describes a study performed on variants of the life essential regulatory protein calmodulin that have been associated with the congenital life threatening heart disease long QT syndrome (LQTS). The study provided novel insights revealing that all variants are distinct from the wild type in regards to structure and dynamics on a detailed level; the presented results are useful for the interpretation of results from protein interaction studies. The underlying research of this paper makes use of all three developed software, which validates that all developed methods fulfil a scientific purpose and are capable of producing solid results.

    List of papers
    1. Fast and Accurate Resonance Assignment of Small-to-Large Proteins by Combining Automated and Manual Approaches
    Open this publication in new window or tab >>Fast and Accurate Resonance Assignment of Small-to-Large Proteins by Combining Automated and Manual Approaches
    Show others...
    2015 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 11, no 1, p. e1004022-Article in journal (Refereed) Published
    Abstract [en]

    The process of resonance assignment is fundamental to most NMR studies of protein structure and dynamics. Unfortunately, the manual assignment of residues is tedious and time-consuming, and can represent a significant bottleneck for further characterization. Furthermore, while automated approaches have been developed, they are often limited in their accuracy, particularly for larger proteins. Here, we address this by introducing the software COMPASS, which, by combining automated resonance assignment with manual intervention, is able to achieve accuracy approaching that from manual assignments at greatly accelerated speeds. Moreover, by including the option to compensate for isotope shift effects in deuterated proteins, COMPASS is far more accurate for larger proteins than existing automated methods. COMPASS is an open-source project licensed under GNU General Public License and is available for download from http://www.liu.se/forskning/foass/tidigare-foass/patrik-lundstrom/software?l=en. Source code and binaries for Linux, Mac OS X and Microsoft Windows are available.

    Place, publisher, year, edition, pages
    Public Library of Science, 2015
    National Category
    Chemical Sciences
    Identifiers
    urn:nbn:se:liu:diva-115010 (URN)10.1371/journal.pcbi.1004022 (DOI)000349309400013 ()25569628 (PubMedID)
    Note

    Funding Agencies|Swedish Research Council [Dnr. 2012-5136]

    Available from: 2015-03-09 Created: 2015-03-06 Last updated: 2017-12-04
    2. Robust and convenient analysis of protein thermal and chemical stability
    Open this publication in new window or tab >>Robust and convenient analysis of protein thermal and chemical stability
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    2015 (English)In: Protein Science, ISSN 0961-8368, E-ISSN 1469-896X, Vol. 24, no 12, p. 2055-2062Article in journal (Refereed) Published
    Abstract [en]

    We present the software CDpal that is used to analyze thermal and chemical denaturation data to obtain information on protein stability. The software uses standard assumptions and equations applied to two-state and various types of three-state denaturation models in order to determine thermodynamic parameters. It can analyze denaturation monitored by both circular dichroism and fluorescence spectroscopy and is extremely flexible in terms of input format. Furthermore, it is intuitive and easy to use because of the graphical user interface and extensive documentation. As illustrated by the examples herein, CDpal should be a valuable tool for analysis of protein stability.

    Place, publisher, year, edition, pages
    WILEY-BLACKWELL, 2015
    Keywords
    protein stability; thermal denaturation; chemical denaturation; circular dichroism; fluorescence; curve fitting; protein stability software; protein denaturation software
    National Category
    Chemical Sciences Clinical Medicine
    Identifiers
    urn:nbn:se:liu:diva-124648 (URN)10.1002/pro.2809 (DOI)000368292000014 ()26402034 (PubMedID)
    Note

    Funding Agencies|Swedish Research Council [2012-5136]; LiU Cancer

    Available from: 2016-02-08 Created: 2016-02-08 Last updated: 2017-11-30
    3. Comprehensive analysis of NMR data using advanced line shape fitting.
    Open this publication in new window or tab >>Comprehensive analysis of NMR data using advanced line shape fitting.
    Show others...
    2017 (English)In: Journal of Biomolecular NMR, ISSN 0925-2738, E-ISSN 1573-5001, Vol. 69, no 2, p. 93-99Article in journal (Refereed) Published
    Abstract [en]

    NMR spectroscopy is uniquely suited for atomic resolution studies of biomolecules such as proteins, nucleic acids and metabolites, since detailed information on structure and dynamics are encoded in positions and line shapes of peaks in NMR spectra. Unfortunately, accurate determination of these parameters is often complicated and time consuming, in part due to the need for different software at the various analysis steps and for validating the results. Here, we present an integrated, cross-platform and open-source software that is significantly more versatile than the typical line shape fitting application. The software is a completely redesigned version of PINT ( https://pint-nmr.github.io/PINT/ ). It features a graphical user interface and includes functionality for peak picking, editing of peak lists and line shape fitting. In addition, the obtained peak intensities can be used directly to extract, for instance, relaxation rates, heteronuclear NOE values and exchange parameters. In contrast to most available software the entire process from spectral visualization to preparation of publication-ready figures is done solely using PINT and often within minutes, thereby, increasing productivity for users of all experience levels. Unique to the software are also the outstanding tools for evaluating the quality of the fitting results and extensive, but easy-to-use, customization of the fitting protocol and graphical output. In this communication, we describe the features of the new version of PINT and benchmark its performance.

    Place, publisher, year, edition, pages
    Springer, 2017
    Keywords
    Dynamics, Line shape fitting, Peak integration, Relaxation, Spectral analysis
    National Category
    Biochemistry and Molecular Biology Chemical Sciences
    Identifiers
    urn:nbn:se:liu:diva-142786 (URN)10.1007/s10858-017-0141-6 (DOI)000414206400004 ()29043470 (PubMedID)2-s2.0-85031497711 (Scopus ID)
    Note

    Funding agencies: Swedish Research Council [2012-5136]

    Available from: 2017-11-03 Created: 2017-11-03 Last updated: 2017-12-04Bibliographically approved
  • 36.
    Nilsson, Artur
    et al.
    Lund University, Sweden.
    Erlandsson, Arvid
    Linköping University, Department of Behavioural Sciences and Learning, Psychology. Linköping University, Faculty of Arts and Sciences. Lund University, Sweden.
    Västfjäll, Daniel
    Linköping University, Department of Behavioural Sciences and Learning, Psychology. Linköping University, Faculty of Arts and Sciences. Decis Research, OR 97401 USA.
    The congruency between moral foundations and intentions to donate, self-reported donations, and actual donations to charity2016In: journal of Research in Personality, ISSN 0092-6566, E-ISSN 1095-7251, Vol. 65, p. 22-29Article in journal (Refereed)
    Abstract [en]

    We extend past research on the congruency between moral foundations and morally relevant outcomes to ingroup- and outgroup-focused charitable giving. We measured intentions to donate to outgroup members (begging EU-migrants) and self-reported donations to ingroup (medical research) and outgroup (international aid) charity organizations in a heterogeneous sample (N = 1008) and actual donations to ingroup (cancer treatment) and outgroup (hunger relief) organizations in two experimental studies (N = 126; N = 200). Individualizing intuitions predicted helping in general across self-report and behavioral data. Binding intuitions predicted higher donations to ingroup causes, lower donations to outgroup causes, and less intentions to donate to outgroup members in the self-report data, and they predicted lower donations overall in the behavioral data. (C) 2016 Elsevier Inc. All rights reserved.

  • 37.
    Nordgaard, Anders
    et al.
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Hedberg, Karin
    Statens Kriminaltekniska Laboratorium.
    Widén, Christina
    Statens Kriminaltekniska Laboratorium.
    Ansell, Ricky
    Linköping University, Department of Physics, Chemistry and Biology, Molecular genetics. Linköping University, The Institute of Technology.
    Comments on ‘‘The database search problem’’ with respect to a recent publication in Forensic Science International: Letter to the Editor2012In: Forensic Science International, ISSN 0379-0738, E-ISSN 1872-6283, Vol. 217, no 1-3, p. e32-e33Article in journal (Refereed)
  • 38.
    Olsen, Jessica
    Linköping University, Department of Physics, Chemistry and Biology, Molecular genetics .
    Stress-induced alternative splicing of Serine/Arginine-rich proteins in the moss Physcomitrella patens2011Independent thesis Advanced level (degree of Master (Two Years)), 40 credits / 60 HE creditsStudent thesis
    Abstract [en]

    Plants are sessile organisms and thus more exposed to stressful environments. By changing the expression of stress related genes, plants are able to cope with stress. Alternative splicing (AS) of pre-mRNA is a major contributor to proteome diversity in eukaryotes. It has been shown that different abiotic stresses affect AS patterns, suggesting a functional role of AS in stress tolerance. The Serine/Arginine-rich proteins (SR proteins) are a conserved family of splicing regulators in eukaryotes. SR proteins are essential for AS and studies have shown that they are themselves subjects to AS after stress exposure which means that they can control their own splicing. In this study, the aim was to characterize the different SR-proteins in the SR subfamily in P. patens, analyze their phylogeny and measure the change in expression of the genes after exposure to five types of stress; osmotic, salinity, dehydration, cold and hormonal. The result showed both individual and overlapping changes in their expression profiles of the three genes. Furthermore, there was an alteration in the alternative splicing pattern for two genes during three of the stresses which resulted in intron retention and possibly a premature termination codon and subseqent non-sense mediated decay.

  • 39.
    Palmér, Robert
    et al.
    Wolfram MathCore AB, Linköping, Sweden;.
    Nyman, Elin
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences. Wolfram MathCore AB, Linköping, Sweden;.
    Penney, Mark
    Department of Clinical Pharmacology, Drug Metabolism, and Pharmacokinetics, MedImmune, Cambridge, UK.
    Marley, Anna
    Bioscience, Astra Zeneca, Alderley Park, UK.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences. Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Agoram, Balaji
    Department of Clinical Pharmacology, Drug Metabolism, and Pharmacokinetics, MedImmune, Cambridge.
    Effects of IL-1β-Blocking Therapies in Type 2 Diabetes Mellitus: A Quantitative Systems Pharmacology Modeling Approach to Explore Underlying Mechanisms2014In: CPT: pharmacometrics & systems pharmacology, ISSN 2163-8306, Vol. 3, no 6, p. 1-8Article in journal (Refereed)
    Abstract [en]

    Recent clinical studies suggest sustained treatment effects of interleukin-1β (IL-1β)-blocking therapies in type 2 diabetes mellitus. The underlying mechanisms of these effects, however, remain underexplored. Using a quantitative systems pharmacology modeling approach, we combined ex vivo data of IL-1β effects on β-cell function and turnover with a disease progression model of the long-term interactions between insulin, glucose, and β-cell mass in type 2 diabetes mellitus. We then simulated treatment effects of the IL-1 receptor antagonist anakinra. The result was a substantial and partly sustained symptomatic improvement in β-cell function, and hence also in HbA1C, fasting plasma glucose, and proinsulin-insulin ratio, and a small increase in β-cell mass. We propose that improved β-cell function, rather than mass, is likely to explain the main IL-1β-blocking effects seen in current clinical data, but that improved β-cell mass might result in disease-modifying effects not clearly distinguishable until >1 year after treatment.

  • 40.
    Pan, Wei
    et al.
    Imperial Coll London, England; Imperial Coll London, England.
    Yuan, Ye
    University of Cambridge, England; University of Calif Berkeley, CA USA.
    Ljung, Lennart
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Goncalves, Jorge
    University of Cambridge, England; Luxembourg Centre Syst Biomed, Luxembourg.
    Stan, Guy-Bart
    Imperial Coll London, England; Imperial Coll London, England.
    Identifying Biochemical Reaction Networks From Heterogeneous Datasets2015In: 2015 54TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), IEEE , 2015, p. 2525-2530Conference paper (Refereed)
    Abstract [en]

    In this paper, we propose a new method to identify biochemical reaction networks (i.e. both reactions and kinetic parameters) from heterogeneous datasets. Such datasets can contain (a) data from several replicates of an experiment performed on a biological system; (b) data measured from a biochemical network subjected to different experimental conditions, for example, changes/perturbations in biological inductions, temperature, gene knock-out, gene over-expression, etc. Simultaneous integration of various datasets to perform system identification has the potential to avoid non-identifiability issues typically arising when only single datasets are used.

  • 41.
    Perez-Patino, Cristina
    et al.
    University of Murcia, Spain.
    Barranco, Isabel
    University of Murcia, Spain.
    Parrilla, Inmaculada
    University of Murcia, Spain.
    Luz Valero, M.
    University of Valencia, Spain.
    Martinez, Emilio A.
    University of Murcia, Spain.
    Rodriguez-Martinez, Heriberto
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Roca, Jordi
    University of Murcia, Spain.
    Characterization of the porcine seminal plasma proteome comparing ejaculate portions2016In: Journal of Proteomics, ISSN 1874-3919, E-ISSN 1876-7737, Vol. 142, p. 15-23Article in journal (Refereed)
    Abstract [en]

    Full identification of boar seminal plasma (SP) proteins remains challenging. This study aims to provide an extensive proteomic analysis of boar SP and to generate an accessible database of boar SP-proteome. A SP-pool (33 entire ejaculates/11 boars; 3 ejaculates/boar) was analyzed to characterize the boar SP-proteome. Twenty ejaculates (5 boars, 4 ejaculates/boar) collected in portions (P1: first 10 mL of sperm rich ejaculate fraction (SRF), P2: rest of SRF and P3: post-SRF) were analyzed to evaluate differentially expressed SP-proteins among portions. SP-samples were analyzed using a combination of SEC, 1-D SDS PAGE and NanoLC-ESI-MS/MS followed by functional bioinformatics. The identified proteins were quantified from normalized LFQ intensity data. A total of 536 SP-proteins were identified, 409 of them in Sus scrofa taxonomy (374 validated with amp;gt;-99% confidence). Barely 20 of the identified SP-proteins were specifically implicated in reproductive processes, albeit other SP proteins could be indirectly involved in functionality and fertility of boar spermatozoa. Thirty-four proteins (16 identified in S. scrofa taxonomy) were differentially expressed among ejaculate portions, 16 being over expressed and 18 under-expressed in Pl-P2 regarding to P3. This major proteome mapping of the boar SP provides a complex inventory of proteins with potential roles as sperm function- and fertility- biomarkers. Biological significance: This proteomic study provides the major characterization of the boar SP-proteome with amp;gt;250 proteins first reported. The boar SP-proteome is described so that a spectral library can be built for relative label free protein quantification with SWATH approach. This proteomic profiling allows the creation of a publicly accessible database of the boar SP-proteome, as a first step for further understanding the role of SP-proteins in reproductive outcomes as well as for the identification of biomarkers for sperm quality and fertility. (C) 2016 Elsevier B.V. All rights reserved.

  • 42.
    Peterson, Rickard
    Linköping University, Department of Computer and Information Science.
    Enkel navigering i webbdatabaser inom bioinformatik: En implementation av moduler för ett urval av databaser2010Independent thesis Basic level (university diploma), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The aim of this thesis was to develop modules for the system BioSpider that are developed by ADIT division at IDA institute at Linköping University. The objective is to simplify for biologist when they seek for information about research findings.

    There is a large number of databases that contains research results about proteins, reactions, pathways etc. Examples of these databases are UniProt, Reactome, IntAct, BioModels and KEGG. Problems emerges since the databases are constructed in different ways and cannot be used in a universal way, they must be individually tailored and adjusted to be compatible with other databases. This is where BioSpider comes in, BioSpider is a program that is supposed to build up a tree of the different databases. Each database is managed individually by BioSpider and is presented to the user in a universal way in the form of a tree. This thesis extends the BioSpider system so that more databases are supported than just the database BioModels.

    The need to support more databases was necessary to be able to produce a usable version of BioSpider with more than one database. This is important to show that the method works in practice.

    The work has been performed by a pilot study of a number of databases. Within these we selected appropriate information that was implemented in BioSpider with different modules for different databases.

    At start of this thesis one database was supported by BioSpider, this database is BioModels. Now BioSpider supports by additional four databases UniProt, Reactome, KEGG and IntAct. BioSpider also supports linking to websites where information can be retrieved, the supported databases are DIP, Ensembl, EMBL, FlyBase, GO, InterPro, OMIM, PDB, PIR, PROSIT and RefSeq.

  • 43.
    Pettersson, Sofia
    et al.
    Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, Faculty of Science & Engineering.
    Forchheimer, Robert
    Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, Faculty of Science & Engineering.
    Larsson, Jan-Åke
    Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, Faculty of Science & Engineering.
    Meta-Boolean models of asymmetric division patterns in the C. elegans intestinal lineage: Implications for the posterior boundary of intestinal twist2013In: Worm, ISSN 2162-4054, Vol. 2, article id e23701Article in journal (Refereed)
    Abstract [en]

    The intestine of Caenorhabditis elegans is derived from 20 cells that are organized into nine intestinal rings. During embryogenesis, three of the rings rotate approximately 90 degrees in a process known as intestinal twist. The underlying mechanisms for this morphological event are not fully known, but it has been demonstrated that both left-right and anterior-posterior asymmetry is required for intestinal twist to occur. We have recently presented a rule-based meta-Boolean tree model intended to describe complex lineages. In this report we apply this model to the E lineage of C. elegans, specifically targeting the asymmetric anterior-posterior division patterns within the lineage. The resulting model indicates that cells with the same factor concentration are located next to each other in the intestine regardless of lineage origin. In addition, the shift in factor concentrations coincides with the boundary for intestinal twist. When modeling lit-1 mutant data according to the same principle, the factor distributions in each cell are altered, yet the concurrence between the shift in concentration and intestinal twist remains. This pattern suggests that intestinal twist is controlled by a threshold mechanism. In the current paper we present the factor concentrations for all possible combinations of symmetric and asymmetric divisions in the E lineage and relate these to the potential threshold by studying existing data for wild-type and mutant embryos. Finally, we discuss how the resulting models can serve as a basis for experimental design in order to reveal the underlying mechanisms of intestinal twist.

  • 44.
    Pham, Tuan D
    et al.
    James Cook University, Townsville, QLD 4811, Australia..
    Beck, Dominik
    University of Applied Sciences Weihenstephan Weihenstephan, 85350 Freising, Germany.
    Crane, Denis I
    Nathan Campus, Griffith University, QLD 4111, Australia..
    Hidden Markov Models for Unaligned DNA Sequence Comparison2005In: WSEAS Transactions on Biology and Biomedicine, ISSN 1109-9518, E-ISSN 2224-2902, Vol. 2, p. 64-69Article in journal (Refereed)
    Abstract [en]

    Comparison of similarity between sequences can provide information for inferring the function of a newly discovered sequence, and understanding the evolutionary relationships among genes, proteins, and entire species. This paper presents a technique for computing the similarity between unaligned DNA sequences. The computation is based on the Kullback-Leibler divergence of hidden Markov models. We used the data sets taken from the threonine operons of Escherichia coli K-12 and Shigella flexneri to test the proposed method. The result obtained agrees with an alignment-based method. We further tested the proposed method with a data set of 34 complete mammalian mtDNA genomes. The phylogenetic tree derived from the second experiment shows reasonable evolutionary relationships between these species.

  • 45.
    Pham, Tuan D
    et al.
    Aizu Research Cluster for Medical Engineering and Informatics Research Center for Advanced Information Science and Technology The University of Aizu, Aizuwakamatsu, Fukushima, Japan.
    Ichikawa, Kazuhisa
    Department of Cancer Biology, Division of Mathematical Oncology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan .
    Characterization of Cancer and Normal Intracellular Images by the Power Law of a Fuzzy Partition Functional2013In: Image Analysis and Recognition: 10th International Conference, ICIAR 2013, Póvoa do Varzim, Portugal, June 26-28, 2013. Proceedings / [ed] Mohamed Kamel, Aurélio Campilho, Berlin, Heidelberg: Springer Berlin/Heidelberg, 2013, p. 597-604Chapter in book (Other academic)
    Abstract [en]

    The discovery of detailed structures of spatial organelles within a single cell obtained by state-of-the-art molecular imaging technology has provided essential biological information for gaining insights into the study of complex human diseases. In particular, such information is helpful for cancer modeling and simulation. This paper presents a novel concept for characterizing the intracellular space of cancer and normal cells using the mathematical principle of power laws applied to a fuzzy partition functional for cluster validity. Experimental results and comparison with image texture analysis suggest the promising application of the proposed method.

  • 46.
    Pham, Tuan D
    et al.
    School of Information Technology and Electrical Engineering, The University of New South Wales, Canberra, Australia.
    Muller, Catharina C
    Biomolecular and Physical Sciences, and Eskitis Institute for Cell and Molecular Therapies, Griffith University, Nathan, Queensland, Australia.
    Crane, Denis I
    Biomolecular and Physical Sciences, and Eskitis Institute for Cell and Molecular Therapies, Griffith University, Nathan, Queensland, Australia.
    Fuzzy scaling analysis of a mouse mutant with brain morphological changes2009In: Information Technology in Biomedicine, IEEE Transactions on, ISSN 1089-7771, Vol. 13, no 4, p. 629-635Article in journal (Refereed)
    Abstract [en]

    Scaling behavior inherently exists in fundamental biological structures, and the measure of such an attribute can only be known at a given scale of observation. Thus, the properties of fractals and power-law scaling have become attractive for research in biology and medicine because of their potential for discovering patterns and characteristics of complex biological morphologies. Despite the successful applications of fractals for the life sciences, the quantitative measure of the scale invariance expressed by fractal dimensions is limited in more complex situations, such as for histopathological analysis of tissue changes in disease. In this paper, we introduce the concept of fuzzy scaling and its analysis of a mouse mutant with postnatal brain morphological changes.

  • 47.
    Pham, Tuan D
    et al.
    Bioinformatics Applications Research Center; School of Information Technology, James Cook University, Townsville, QLD, Australia.
    Shim, Byung-Sub
    Bioinformatics Applications Research Center.
    A cepstral distortion measure for protein comparison and identification2005In: Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on, 2005, Vol. 9, p. 5609-5614Conference paper (Refereed)
    Abstract [en]

    Protein sequence comparison is the most powerful tool for the identification of novel protein structure and function. This type of inference is commonly based on the similar sequence-similar structure-similar function paradigm, and derived by sequence similarity searching on databases of protein sequences. As entire genomes have been being determined at a rapid rate, computational methods for comparing protein sequences will be more essential for probing the complexity of molecular machines. In this paper we introduce a pattern-comparison algorithm, which is based on the mathematical concept of linear-predictive-coding based cepstral distortion measure, for comparison and identification of protein sequences. Experimental results on a real data set of functionally related and functionally non-related protein sequences have shown the effectiveness of the proposed approach on both accuracy and computational efficiency.

  • 48.
    Pilstål, Robert
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    On protein structure, function and modularity from an evolutionary perspective2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    We are compounded entities, given life by a complex molecular machinery. When studying these molecules we have to make sense of a diverse set of dynamical nanostructures with wast and intricate patterns of interactions. Protein polymers is one of the major groups of building blocks of such nanostructures which fold up into more or less distinct three dimensional structures. Due to their shape, dynamics and chemical properties proteins are able to perform a plethora of specific functions essential to all known cellular lifeforms.

    The connection between protein sequence, translated into protein structure and in the continuation into protein function is well accepted but poorly understood. Malfunction in the process of protein folding is known to be implicated in natural aging, cancer and degenerative diseases such as Alzheimer's.

    Protein folds are described hierarchically by structural ontologies such as SCOP, CATH and Pfam all which has yet to succeed in deciphering the natural language of protein function. These paradigmatic views centered on protein structure fail to describe more mutable entities, such as intrinsically disordered proteins (IDPs) which lack a clear defined structure.

    As of 2012, about two thirds of cancer patients was predicted to survive past 5 years of diagnosis. Despite this, about a third do not survive and numerous of successfully treated patients suffer from secondary conditions due to chemotherapy, surgery and the like. In order to handle cancer more efficiently we have to better understand the underlying molecular mechanisms.

    Elusive to standard methods of investigation, IDPs have a central role in pathology; dysfunction in IDPs are key factors in cellular system failures such as cancer, as many IDPs are hub regulators for major cell functions. These IDPs carry short conserved functional boxes, that are not described by known ontologies, which suggests the existence of a smaller entity. In an investigation of a pair of such boxes of c-MYC, a plausible structural model of its interacting with Pin1 emerged, but such a model still leaves the observer with a puzzle of understanding the actual function of that interaction.

    If the protein is represented as a graph and modeled as the interaction patterns instead of as a structural entity, another picture emerges. As a graph, there is a parable from that of the boxes of IDPs, to that of sectors of allosterically connected residues and the theory of foldons and folding units. Such a description is also useful in deciphering the implications of specific mutations.

    In order to render a functional description feasible for both structured and disordered proteins, there is a need of a model separate from form and structure. Realized as protein primes, patterns of interaction, which has a specific function that can be defined as prime interactions and context. With function defined as interactions, it might be possible that the discussion of proteins and their mechanisms is thereby simplified to the point rendering protein structural determination merely supplementary to understanding protein function.

    List of papers
    1. Pre-Anchoring of Pin1 to Unphosphorylated c-Myc in a Fuzzy Complex Regulates c-Myc Activity
    Open this publication in new window or tab >>Pre-Anchoring of Pin1 to Unphosphorylated c-Myc in a Fuzzy Complex Regulates c-Myc Activity
    Show others...
    2015 (English)In: Structure, ISSN 0969-2126, E-ISSN 1878-4186, Vol. 23, no 12, p. 2267-2279Article in journal (Refereed) Published
    Abstract [en]

    Hierarchic phosphorylation and concomitant Pin1-mediated proline isomerization of the oncoprotein c-Myc controls its cellular stability and activity. However, the molecular basis for Pin1 recognition and catalysis of c-Myc and other multisite, disordered substrates in cell regulation and disease is unclear. By nuclear magnetic resonance, surface plasmon resonance, and molecular modeling, we show that Pin1 subdomains jointly pre-anchor unphosphorylated c-Myc1–88 in the Pin1 interdomain cleft in a disordered, or “fuzzy”, complex at the herein named Myc Box 0 (MB0) conserved region N-terminal to the highly conserved Myc Box I (MBI). Ser62 phosphorylation in MBI intensifies previously transient MBI-Pin1 interactions in c-Myc1–88 binding, and increasingly engages Pin1PPIase and its catalytic region with maintained MB0 interactions. In cellular assays, MB0 mutated c-Myc shows decreased Pin1 interaction, increased protein half-life, but lowered rates of Myc-driven transcription and cell proliferation. We propose that dynamic Pin1 recognition of MB0 contributes to the regulation of c-Myc activity in cells

    Place, publisher, year, edition, pages
    Cell Press, 2015
    National Category
    Natural Sciences
    Identifiers
    urn:nbn:se:liu:diva-106184 (URN)10.1016/j.str.2015.10.010 (DOI)
    Note

    The previous status of this article was Manuscript and the original title was Pre-anchoring of Pin1 to unphosphorylated c-Myc in a dynamic complex affects c-Myc stability andactivity.

    Funding Agencies|Knut and Alice Wallenberg Foundation; Swedish Cancer Foundation; Swedish Child Cancer Foundation; Carl Trygger foundation; LiU Cancer Research Network; Swedish Research Council; NCI [R01s CA129040, CA100855]

    Available from: 2014-04-28 Created: 2014-04-28 Last updated: 2018-05-06Bibliographically approved
    2. Mutation-Induced Population Shift in the MexR Conformational Ensemble Disengages DNA Binding: A Novel Mechanism for MarR Family Derepression
    Open this publication in new window or tab >>Mutation-Induced Population Shift in the MexR Conformational Ensemble Disengages DNA Binding: A Novel Mechanism for MarR Family Derepression
    Show others...
    2016 (English)In: Structure, ISSN 0969-2126, E-ISSN 1878-4186, Vol. 24, no 8, p. 1311-1321Article in journal (Refereed) Published
    Abstract [en]

    MexR is a repressor of the MexAB-OprM multidrug efflux pump operon of Pseudomonas aeruginosa, where DNA-binding impairing mutations lead to multidrug resistance (MDR). Surprisingly, the crystal structure of an MDR-conferring MexR mutant R21W (2.19 angstrom) presented here is closely similar to wildtype MexR. However, our extended analysis, by molecular dynamics and small-angle X-ray scattering, reveals that the mutation stabilizes a ground state that is deficient of DNA binding and is shared by both mutant and wild-type MexR, whereas the DNA-binding state is only transiently reached by the more flexible wild-type MexR. This population shift in the conformational ensemble is effected by mutation-induced allosteric coupling of contact networks that are independent in the wild-type protein. We propose that the MexR-R21W mutant mimics derepression by small-molecule binding to MarR proteins, and that the described allosteric model based on population shifts may also apply to other MarR family members.

    Place, publisher, year, edition, pages
    CELL PRESS, 2016
    National Category
    Structural Biology
    Identifiers
    urn:nbn:se:liu:diva-131908 (URN)10.1016/j.str.2016.06.008 (DOI)000383244600012 ()27427478 (PubMedID)
    Note

    Funding Agencies|European Communitys Seventh Framework Program (FP7) under BioStruct-X [283570]; Swedish e-Science Research Center; Swedish Research Council; Tage Erlander Visiting Professor grant.

    The original status of this article was Manuscript and the titel was Population shift disengages DNA binding in a multidrug resistance MexR mutant.

    Available from: 2016-10-13 Created: 2016-10-11 Last updated: 2018-05-06
    3. Methods for estimation of model accuracy in CASP12
    Open this publication in new window or tab >>Methods for estimation of model accuracy in CASP12
    Show others...
    2018 (English)In: Proteins: Structure, Function, and Bioinformatics, ISSN 0887-3585, E-ISSN 1097-0134, Vol. 86, p. 361-373Article in journal (Refereed) Published
    Abstract [en]

    Methods to reliably estimate the quality of 3D models of proteins are essential drivers for the wide adoption and serious acceptance of protein structure predictions by life scientists. In this article, the most successful groups in CASP12 describe their latest methods for estimates of model accuracy (EMA). We show that pure single model accuracy estimation methods have shown clear progress since CASP11; the 3 top methods (MESHI, ProQ3, SVMQA) all perform better than the top method of CASP11 (ProQ2). Although the pure single model accuracy estimation methods outperform quasi-single (ModFOLD6 variations) and consensus methods (Pcons, ModFOLDclust2, Pcomb-domain, and Wallner) in model selection, they are still not as good as those methods in absolute model quality estimation and predictions of local quality. Finally, we show that when using contact-based model quality measures (CAD, lDDT) the single model quality methods perform relatively better.

    Place, publisher, year, edition, pages
    WILEY, 2018
    Keywords
    CASP; consensus predictions; estimates of model accuracy; machine learning; protein structure prediction; quality assessment
    National Category
    Bioinformatics (Computational Biology)
    Identifiers
    urn:nbn:se:liu:diva-145749 (URN)10.1002/prot.25395 (DOI)000425523000031 ()28975666 (PubMedID)
    Note

    Funding Agencies|Swedish Research Council [VR-NT 2012-5046, 2012-5270]; Swedish e-Science Research Center; National Research Foundation of Korea (NRF) - Korea government (MEST) [2008-0061987]; Saudi Arabian Government; United States-Israel Binational Science Foundation (BSF) [2009432]; Israel Science Foundation (ISF) [1122/14]

    Available from: 2018-03-22 Created: 2018-03-22 Last updated: 2018-05-06
  • 49.
    Robins, Tiina
    et al.
    Department of Molecular Medicine and Surgery, Center for Molecular Medicine, L8:02, Karolinska Institutet/Karolinska University Hospital, S-171 76 Stockholm, Sweden.
    Carlsson, Jonas
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics . Linköping University, The Institute of Technology.
    Sunnerhagen, Maria
    Linköping University, Department of Physics, Chemistry and Biology, Molecular Biotechnology . Linköping University, The Institute of Technology.
    Wedell, Anna
    Department of Molecular Medicine and Surgery, Center for Molecular Medicine, L8:02, Karolinska Institutet/Karolinska University Hospital, S-171 76 Stockholm, Sweden.
    Persson, Bengt
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics . Linköping University, The Institute of Technology.
    Molecular Model of Human CYP21 Based onMammalian CYP2C5: Structural Features Correlatewith Clinical Severity of Mutations CausingCongenital Adrenal Hyperplasia2006In: Molecular Endocrinology, ISSN 0888-8809, E-ISSN 1944-9917, Vol. 20, no 11, p. 2946-2964Article in journal (Refereed)
    Abstract [en]

    Enhanced understanding of structure-function relationshipsof human 21-hydroxylase, CYP21, is requiredto better understand the molecular causesof congenital adrenal hyperplasia. To this end, astructural model of human CYP21 was calculatedbased on the crystal structure of rabbit CYP2C5.All but two known allelic variants of missense type,a total of 60 disease-causing mutations and sixnormal variants, were analyzed using this model. Astructural explanation for the corresponding phenotypewas found for all but two mutants for whichavailable clinical data are also discrepant with invitro enzyme activity. Calculations of protein stabilityof modeled mutants were found to correlateinversely with the corresponding clinical severity.Putative structurally important residues were identifiedto be involved in heme and substrate binding,redox partner interaction, and enzyme catalysisusing docking calculations and analysis of structurallydetermined homologous cytochrome P450s(CYPs). Functional and structural consequences ofseven novel mutations, V139E, C147R, R233G,T295N, L308F, R366C, and M473I, detected inScandinavian patients with suspected congenitaladrenal hyperplasia of different severity, were predictedusing molecular modeling. Structural featuresdeduced from the models are in good correlationwith clinical severity of CYP21 mutants,which shows the applicability of a modeling approachin assessment of new CYP21 mutations.

  • 50.
    Sandin, Linnea
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Bergkvist, Liza
    Linköping University, Faculty of Science & Engineering. Linköping University, Department of Physics, Chemistry and Biology, Chemistry.
    Nath, Sangeeta
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Kielkopf, Claudia
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Janefjord, Camilla
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences.
    Helmfors, Linda
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, Faculty of Science & Engineering.
    Zetterberg, Henrik
    Clinical Neurochemistry Laboratory, Department of Neuroscience and Physiology, Sahlgrenska University Hospital, Mölndal, Sweden / UCL Institute of Neurology, London, UK.
    Blennow, Kaj
    Clinical Neurochemistry Laboratory, Department of Neuroscience and Physiology, Sahlgrenska University Hospital, Mölndal, Sweden.
    Li, Hongyun
    Illawarra Health and Medical Research Institute, University of Wollongong, Australia.
    Nilsberth, Camilla
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Local Health Care Services in Central Östergötland, Department of Acute Internal Medicine and Geriatrics.
    Garner, Brett
    Illawarra Health and Medical Research Institute, University of Wollongong, Australia / School of Biological Sciences, University of Wollongong, Australia.
    Brorsson, Ann-Christin
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, Faculty of Science & Engineering.
    Kågedal, Katarina
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Beneficial effects of increased lysozyme levels in Alzheimer’s disease modelled in Drosophila melanogaster2016In: The FEBS Journal, ISSN 1742-464X, E-ISSN 1742-4658, Vol. 283, no 19, p. 3508-3522Article in journal (Refereed)
    Abstract [en]

    Genetic polymorphisms of immune genes that associate with higher risk to develop Alzheimer’s disease (AD) have led to an increased research interest on the involvement of the immune system in AD pathogenesis. A link between amyloid pathology and immune gene expression was suggested in a genome-wide gene expression study of transgenic amyloid mouse models. In this study, the gene expression of lysozyme, a major player in the innate immune system, was found to be increased in a comparable pattern as the amyloid pathology developed in transgenic mouse models of AD. A similar pattern was seen at protein levels of lysozyme in human AD brain and CSF, but this lysozyme pattern was not seen in a tau transgenic mouse model. Lysozyme was demonstrated to be beneficial for different Drosophila melanogaster models of AD. In flies that expressed Aβ1-42 or AβPP together with BACE1 in the eyes, the rough eye phenotype indicative of toxicity was completely rescued by coexpression of lysozyme. In Drosophila flies bearing the Aβ1-42 variant with the Arctic gene mutation, lysozyme increased the fly survival and decreased locomotor dysfunction dose dependently. An interaction between lysozyme and Aβ1-42 in the Drosophila eye was discovered. We propose that the increased levels of lysozyme, seen in mouse models of AD and in human AD cases, were triggered by Aβ1-42 and caused a beneficial effect by binding of lysozyme to toxic species of Aβ1-42, which prevented these from exerting their toxic effects. These results emphasize the possibility of lysozyme as biomarker and therapeutic target for AD.

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