The key to the complexity of higher organisms lies not in the number of protein coding genes they carry, but rather in the intrinsic complexity of the gene regulatory networks. The major effectors of transcriptional regulation are proteins called transcription factors, and in this thesis four papers describing genome-wide studies of seven such factors are presented, together with studies on components of the chromatin and transcriptome. In Paper I, we optimized a large-scale in vivo method, ChIP-chip, to study protein – DNA interactions using microarrays. The metabolic-disease related transcription factors USF1, HNF4a and FOXA2 were studied in 1 % of the genome, and a surprising number of binding sites were found, mostly far from annotated genes. In Paper II, a novel sequencing based method, ChIP-seq, was applied to FOXA2, HNF4a and GABPa, allowing a true genome-wide view of binding sites. A large overlap between the datasets were seen, and molecular interactions were verified in vivo. Using a ChIP-seq specific motif discovery method, we identified both the expected motifs and several for co-localized transcription factors. In Paper III, we identified and studied a novel transcription factor, ZBED6, using the ChIP-seq method. Here, we went from one known binding site to several hundred sites throughout the mouse genome. Finally, in Paper IV, we studied the chromatin landscape by deep sequencing of nucleosomal DNA, and further used RNA-sequencing to quantify expression levels, and extended the knowledge about the binding profiles for the transcription factors NFY and TCF7L2.

Weak Affinity Chromatography (WAC) is a technology that was developed to analyse weak (KD > 10-5 M) although selective interactions between biomolecules. The focus of this thesis was to develop this method for various applications in the drug development process.   Fragment Based Drug Discovery is a new approach in finding new small molecular drugs. Here, relatively small libraries (a few hundreds to a few thousands of compounds) of fragments (150 – 300 Da) are screened against the target. Fragment hits are then developed into lead molecules by linking, growing or merging fragments binding to different locations of the protein’s active site. However, due to the weakly binding nature of fragments, methods that are able to detect very weak binding events are needed. In this thesis, WAC is presented as a new robust and highly reproducible technology for fragment screening. The technology is demonstrated against a number of different protein targets – proteases, kinases, chaperones and protein-protein interaction (PPI) targets. Comparison of data from fragment screening of 111 fragments by WAC and other more established technologies for fragment screening, such as surface plasmon resonance (SPR) and nuclear magnetic resonance (NMR), validates WAC as a screening technology. It also points at the importance of performing fragment screening by multiple methods as they complement each other.   Other applications of WAC in drug development are also presented. The method can be used for chiral separations of racemic mixtures during fragment screening, which enables affinity measurements of individual enantiomers binding to the target of interest. Further, analysis of crude reaction mixtures is shown. By these procedures, the affinity of the product can be assessed directly after synthesis without any time-consuming purification steps. In addition, a high performance liquid chromatography (HPLC) system for highly efficient drug partition studies was developed by stable immobilization of lipid bilayer disks – lipodisks – on a high performance silica support material. These lipodisks are recognized model membranes for drug partition studies. A WAC system with incorporated membrane proteins into immobilized lipodisks has also been produced and evaluated with the ultimate objective to study affinity interactions between ligands and membrane proteins. / Ett läkemedel utövar sin funktion genom att påverka aktiviteten hos ett protein i kroppen då det binder till dess aktiva säte. Förändringen i aktivitet leder till fysiologiska förändringar i kroppen beroende på vilken funktion proteinet har. Med läkemedelsmolekyl avses här en liten organisk molekyl. Fragment-baserad läkemedelsutveckling är en ny metod for att ta fram nya läkemedel. Metoden fungerar genom att man bygger läkemedelsmolekyler utifrån mindre fragment som binder till målproteinet. Fragmenten hittar man genom att screena hela bibliotek av olika fragment mot samma målprotein för att urskilja de som binder till proteinets aktiva säte. Fördelen med den här metoden är bl. a. att med mindre molekyler som utgångspunkt kan en större del av antalet möjliga kombinationer av atomer representeras med ett mindre antal fragment än för större molekyler. Normalt utgörs ett fragmentbibliotek enbart av några hundra till några tusen substanser. Eftersom fragmenten är små har de få interaktionspunker och binder relativt svagt. De svaga bindningarna är svåra att se och mycket känsliga metoder behövs.   Svagaffinitetskromatografi är en vätskekromatografisk metod som utvecklades för att studera svaga men mycket selektiva bindningar mellan biomolekyler. Den här avhandlingen syftar till att utveckla metoden för olika användningsområden inom läkemedelsutveckling, främst som en ny metod för fragment-screening. Här mäter man interaktionen mellan ett protein och ett fragment. Proteinet kopplas till ett material som sedan packas i en kolonn i formen av en cylinder. När provet pumpas igenom kolonnen kommer de analyter med affinitet till proteinets aktiva säte att fördröjas på kolonnen i relation till hur starkt de interagerar med målproteinet.   I den här avhandlingen presenteras fragment-screening med svagaffinitetskromatografi gentemot ett antal olika typer av målproteiner. Resultatet överensstämmer väl med andra metoder för fragment-screening. Analys av reaktionsblandningar med svagaffinitetskromatografi demonstreras också. Därmed kan bindningen mellan en produkt i en reaktionsblandning och ett målprotein mätas direkt utan föregående uppreningssteg av reaktionsblandningen. Lipodiskar är små diskformade modellmembran som kan användas för att bl. a. mäta hur effektivt läkemedlet tas upp i kroppen vid behandling. Ett system med immobiliserade lipodiskar i en kolonn utvecklades med det framtida målet att kunna arbeta med membranproteiner med svagaffinitetskromatografi.   Detta arbete utgör en del i att utveckla svagaffinitetskromatografi som en lättillgänglig och relativt billig metod för användning inom industrin och akademin för läkemedelsutveckling.

We introduce a new software system called Formal Concept Analysis Research Toolbox (FCART). Our goal is to create a universal integrated environment for knowledge and data engineers. FCART is constructed upon an iterative data analysis methodology and provides a built-in set of research tools based on Formal Concept Analysis techniques for working with object-attribute data representations. The provided toolset allows for the fast integration of extensions on several levels: from internal scripts to plugins. FCART was successfully applied in several data mining and knowledge discovery tasks. Examples of applying the system in medicine and criminal investigations are considered.

Due to complexity of biomedical problems, adaptive and intelligent knowledge discovery and data mining systems are highly needed to help humans to understand the inherent mechanism of diseases. For biomedical classification problems, typically it is impossible to build a perfect classifier with 100% prediction accuracy. Hence a more realistic target is to build an effective Decision Support System (DSS). In this dissertation, a novel adaptive Fuzzy Association Rules (FARs) mining algorithm, named FARM-DS, is proposed to build such a DSS for binary classification problems in the biomedical domain. Empirical studies show that FARM-DS is competitive to state-of-the-art classifiers in terms of prediction accuracy. More importantly, FARs can provide strong decision support on disease diagnoses due to their easy interpretability. This dissertation also proposes a fuzzy-granular method to select informative and discriminative genes from huge microarray gene expression data. With fuzzy granulation, information loss in the process of gene selection is decreased. As a result, more informative genes for cancer classification are selected and more accurate classifiers can be modeled. Empirical studies show that the proposed method is more accurate than traditional algorithms for cancer classification. And hence we expect that genes being selected can be more helpful for further biological studies.

<p>Obesity is a globally increasing disease particularly in developing countries and among children. It is mainly caused by intake of diets high in fat and the lack of physical activity. Obesity is a risk factor for diseases such as type II diabetes, high blood pressure, high cholesterol and certain cancers. Prediabetes is a condition where blood glucose levels are above normal but have not&nbsp / reached those of diabetes. It is difficult to diagnose, as there are no signs or symptoms. Some type II diabetes patients bear no symptoms at all and the disease is discovered late. Proteomics is a field that can provide opportunities for early diagnosis of diseases through biomarker discovery. The early diagnosis of diabetes can assist in the prevention and treatment of diabetes. Therefore there is a need for the early diagnosis of diabetes. Twenty Wistar rats were used. The rats were initially fed a CHOW diet, which is the standard balanced diet for rats, for 4 weeks. The rats were then divided into 2 groups of 10 where 1 group was fed CHOW and another was fed a high fat (HF) diet in order to induce obesity. The two groups were fed their respective diets for 18 weeks. Rats were weighed. Rats were placed in metabolic chambers and 24 hour urine samples were collected. Ketone levels were measured by Ketostix. Urine proteins were precipitated by acetone, quantified and separated on both the 1D SDS-PAGE and the 2D SDS-PAGE. Protein expression changes between CHOW and HF fed rats were determined and identified using MALDI-TOF mass spectrometry. Protein spots intensities increased and decreased between the CHOW and HF fed rats. Transducin (beta)-like 3 was identified as the only differentially expressed protein, which might serve as a potential biomarker for prediabetes.</p>

This thesis examines Dar&uuml / lf&uuml / nun Konferanslari which consists of a series of lectures that were delivered by Salih Zeki in 1914 &ndash / 1915 in Ottoman State. These lectures were on geometry, its history and especially on the discovery of non-Euclidean geometries. And the purpose of this thesis is to propose the sufficiency and the legitimacy of these lectures as an account on the history of geometry. As a preliminary to analyzing Salih Zeki&rsquo / s lectures, different views on geometry&rsquo / s history and progress will be analyzed and compared. The results of this comparison will be the guide by means of which Dar&uuml / lf&uuml / nun Konferanslari will be examined. This thesis also serves as a source that makes Salih Zeki&rsquo / s ideas accessible, by presenting an English summary of his lectures which were originally published in Ottoman Turkish.

This thesis introduces LIFT, a combination of a cluster-based approach with a cross-layer scheme in order to discover services in MANET. In this proposal, High Capability Devices (HCD) are differentiated from Limited Capability Devices (LCD). HCD are set up as the cluster leaders in each cluster so as to perform most of the service discovery activities. Thus, LIFT manages local traffic instead of global traffic. Consequently, messages, energy, computing processes, and bandwidth were reduced due to the optimum usage of network resources. In order to know if LIFT achieves its goal to minimize resources, we have compared LIFT with another well-known solution (AODV-SD) in terms of control message overhead, energy consumption, PDR, throughput, hop count average, NRL, end-to-end delay, and service acquisition time. After carrying out many trials and simulations, LIFT improved previous results in the area. / La tesis presenta a LIFT, una solución para descubrir servicios en MANET que combina un enfoque basado en cluster con un esquema cross-layer. En esta propuesta se diferencian los dispositivos de capacidades superiores (HCD) de los dispositivos de capacidades limitadas (LCD). Los HCD se establecen como líderes en cada cluster para ejecutar la mayoría de las actividades de descubrimiento de servicios. De esta forma, LIFT maneja tráfico local en vez de tráfico global. Por tanto, se reduce el consumo de mensajes, energía y cómputo al hacer uso óptimo de los recursos de la red. Para saber si LIFT logra el objetivo de minimizar recursos, lo hemos comparado contra otra solución (AODV-SD) en aspectos como sobrecarga de paquetes de control, consumo de energía, PDR, throughput, promedio de saltos, NRL, retardo extremo a extremo y tiempo de adquisición de servicios. Después de muchas pruebas y simulaciones, LIFT mejora resultados anteriores en este campo

Influenza B virus hemagglutinin (HA) is a major surface glycoprotein with frequent amino-acid substitutions. However, the roles of antibody selection in the amino-acid substitutions of HA were still poorly understood. An analysis was conducted on a total of 271 HA 1 sequences of influenza B virus strains isolated during 1940∼2007 finding positively selected sites all located in the four major epitopes (120-loop, 150-loop, 160-loop and 190-helix) supporting a predominant role of antibody selection in HA evolution. Of particular significance is the involvement of the 120-loop in positive selection. Influenza B virus HA continues to evolve into new sublineages, within which the four major epitopes were targeted selectively in positive selection. Thus, any newly emerging strains need to be placed in the context of their evolutionary history in order to understand and predict their epidemic potential. As key epigenetic regulators, polycomb group (PcG) proteins are responsible for the control of cell proliferation and differentiation as well as stem cell pluripotency and self-renewal. To facilitate experimental identification of PcG target genes, which are poorly understood, we propose a novel computational method, EpiPredictor , which models transcription factor interaction using a non-linear kernel. The resulting targets suggests that multiple transcription factor networking at the cis -regulatory elements is critical for PcG recruitment, while high GC content and high conservation level are also important features of PcG target genes. To try to translate the EpiPredictor into human data, we performed a computational study utilizing 22 human genome-wide CHIP data to identify DNA motifs and genome features that would potentially specify PRC2 using five motif discovery algorithms, Jaspar known transcription binding motifs, and other whole genome data. We have found multiple motifs within the various subgroups of experimental categories that have much higher enrichment against CHIP identified gene promoter than among random gene promoters. Specifically, we have identified Low CpG content CpG Islands (LeG's) as being critical in the separation of Cancer cell line identified targets from Embryonic Stem cell line identified targets. Additionally, there are differences between human and mouse ES cell predictions using the same motifs and features suggesting relevant evolutionary divergence.

This thesis investigates the correlations between short protein peptide sequences and local tertiary structures. In particular, it introduces a novel algorithm for partitioning short protein segments into clusters of local sequence-structure motifs, and demonstrates that these motif clusters contain useful structural information via two applications to structural prediction. The first application utilizes motif clusters to predict local protein tertiary structures. A novel dynamic programming algorithm that performs comparably with some of the best existing algorithms is described. The second application exploits the capability of motif clusters in recognizing regular secondary structures to improve the performance of secondary structure prediction based on Support Vector Machines. Empirical results show significant improvement in overall prediction accuracy with no performance degradation in any specific aspect being measured. The encouraging results obtained illustrate the great potential of using local sequence-structure motifs to tackle protein structure predictions and possibly other important problems in computational biology.

<p>The initiation of two major processes in the eukaryotic cell, gene transcription and DNA replication, is regulated largely through interactions between proteins or protein complexes and DNA. Although a lot is known about the interacting proteins and their role in regulating transcription and replication, the specific DNA binding motifs of many regulatory proteins and complexes are still to be determined. For this purpose, many computational tools for DNA motif discovery have been developed in the last two decades. These tools employ a variety of strategies, from exhaustive search to sampling techniques, with the hope of finding over-represented motifs in sets of co-regulated or co-bound sequences. Despite the variety of computational tools aimed at solving the problem of motif discovery, their ability to correctly detect known DNA motifs is still limited. The motifs are usually short and many times degenerate, which makes them difficult to distinguish from genomic background. We believe the most efficient strategy for improving the performance of motif discovery is not to use increasingly complex computational and statistical methods and models, but to incorporate more of the biology into the computational techniques, in a principled manner. To this end, we propose a novel motif discovery algorithm: PRIORITY. Based on a general Gibbs sampling framework, PRIORITY has a major advantage over other motif discovery tools: it can incorporate different types of biological information (e.g., nucleosome positioning information) to guide the search for DNA binding sites toward regions where these sites are more likely to occur (e.g., nucleosome-free regions). </p><p>We use transcription factor (TF) binding data from yeast chromatin immunoprecipitation (ChIP-chip) experiments to test the performance of our motif discovery algorithm when incorporating three types of biological information: information about nucleosome positioning, information about DNA double-helical stability, and evolutionary conservation information. In each case, incorporating additional biological information has proven very useful in increasing the accuracy of motif finding, with the number of correctly identified motifs increasing with up to 52%. PRIORITY is not restricted to TF binding data. In this work, we also analyze origin recognition complex (ORC) binding data and show that PRIORITY can utilize DNA structural information to predict the binding specificity of the yeast ORC. </p><p>Despite the improvement obtained using additional biological information, the success of motif discovery algorithms in identifying known motifs is still limited, especially when applied to sequences bound in vivo (such as those of ChIP-chip) because the observed protein-DNA interactions are not necessarily direct. Some TFs associate with DNA only indirectly via protein partners, while others exhibit both direct and indirect binding. We propose a novel method to distinguish between direct and indirect TF-DNA interactions, integrating in vivo TF binding data, in vivo nucleosome occupancy data, and in vitro motifs from protein binding microarrays. When applied to yeast ChIP-chip data, our method reveals that only 48% of the ChIP-chip data sets can be readily explained by direct binding of the profiled TF, while 16% can be explained by indirect DNA binding. In the remaining 36%, we found that none of the motifs used in our analysis was able to explain the ChIP-chip data, either because the data was too noisy or because the set of motifs was incomplete. As more in vitro motifs become available, our method can be used to build a complete catalog of direct and indirect TF-DNA interactions.</p> / Dissertation