Knowledge discovery from tree databases using balanced optimal search

This research is a step forward in discovering knowledge from databases of complex structure like tree or graph. Several data mining algorithms are developed based on a novel representation called Balanced Optimal Search for extracting implicit, unknown and potentially useful information like patterns, similarities and various relationships from tree data, which are also proved to be advantageous in analysing big data. This thesis focuses on analysing unordered tree data, which is robust to data inconsistency, irregularity and swift information changes, hence, in the era of big data it becomes a popular and widely used data model.

Eco-Taxonomic Insights into Actinomycete Symbionts of Termites for Discovery of Novel Bioactive Compounds

Termites play a major role in foraging and degradation of plant biomass as well as cultivating bioactive microorganisms for their defense. Current advances in “omics” sciences are revealing insights into function-related presence of these symbionts, and their related biosynthetic activities and genes identified in gut symbiotic bacteria might offer a significant potential for biotechnology and biodiscovery. Actinomycetes have been the major producers of bioactive compounds with an extraordinary range of biological activities. These metabolites have been in use as anticancer agents, immune suppressants, and most notably, as antibiotics. Insect-associated actinomycetes have also been reported to produce a range of antibiotics such as dentigerumycin and mycangimycin. Advances in genomics targeting a single species of the unculturable microbial members are currently aiding an improved understanding of the symbiotic interrelationships among the gut microorganisms as well as revealing the taxonomical identity and functions of the complex multilayered symbiotic actinofloral layers. If combined with target-directed approaches, these molecular advances can provide guidance towards the design of highly selective culturing methods to generate further information related to the physiology and growth requirements of these bioactive actinomycetes associated with the termite guts. This chapter provides an overview on the termite gut symbiotic actinoflora in the light of current advances in the “omics” science, with examples of their detection and selective isolation from the guts of the Sunshine Coast regional termite Coptotermes lacteus in Queensland, Australia

A transcriptomic analysis of the kidney tissue of Tra catfish (pangasianodon hypophthalmus) reared in saline condition: De novo assembly, annotation, SNP discovery

Pangasianodon hypophthalmus is a commercially important freshwater fish used in inland aquaculture in the Mekong Delta, Vietnam. The current study using Ion Torrent technology generated EST resources from the kidney for Tra catfish reared at a salinity level of 9 ppt. We obtained 2,623,929 reads after trimming and processing with an average length of 104 bp. De novo assemblies were generated using CLC Genomic Workbench, Trinity and Velvet/Oases with the best overall contig performance resulting from the CLC assembly. De novo assembly using CLC yielded 29,940 contigs, and allowing identification of 5,710 putative genes when comppared with NCBI non-redundant database. A large number of single nucleotide polymorphisms (SNPs) were also detected. The sequence collection generated in our study represents the most comprehensive transcriptomic resource for P. hypophthalmus available to date.

Drug discovery approaches utilizing three-dimensional cell culture

Historically, two-dimensional (2D) cell culture has been the preferred method of producing disease models in vitro. Recently, there has been a move away from 2D culture in favor of generating three-dimensional (3D) multicellular structures, which are thought to be more representative of the in vivo environment. This transition has brought with it an influx of technologies capable of producing these structures in various ways. However, it is becoming evident that many of these technologies do not perform well in automated in vitro drug discovery units. We believe that this is a result of their incompatibility with high-throughput screening (HTS). In this study, we review a number of technologies, which are currently available for producing in vitro 3D disease models. We assess their amenability with high-content screening and HTS and highlight our own work in attempting to address many of the practical problems that are hampering the successful deployment of 3D cell systems in mainstream research.

Pentitol phosphate dehydrogenases: Discovery, characterization and use in D-arabitol and xylitol production by metabolically engineered Bacillus subtilis

The ultimate goal of this study has been to construct metabolically engineered microbial strains capable of fermenting glucose into pentitols D-arabitol and, especially, xylitol. The path that was chosen to achieve this goal required discovery, isolation and sequencing of at least two pentitol phosphate dehydrogenases of different specificity, followed by cloning and expression of their genes and characterization of recombinant arabitol and xylitol phosphate dehydrogenases. An enzyme of a previously unknown specificity, D-arabitol phosphate dehydrogenase (APDH), was discovered in Enterococcus avium. The enzyme was purified to homogenity from E. avium strain ATCC 33665. SDS/PAGE revealed that the enzyme has a molecular mass of 41 ± 2 kDa, whereas a molecular mass of 160 ± 5 kDa was observed under non-denaturing conditions implying that the APDH may exist as a tetramer with identical subunits. Purified APDH was found to have narrow substrate specificity, converting only D-arabitol 1-phosphate and D-arabitol 5-phosphate into D-xylulose 5-phosphate and D-ribulose 5-phosphate, respectively, in the oxidative reaction. Both NAD+ and NADP+ were accepted as co-factors. Based on the partial protein sequences, the gene encoding APDH was cloned. Homology comparisons place APDH within the medium chain dehydrogenase family. Unlike most members of this family, APDH requires Mn2+ but no Zn2+ for enzymatic activity. The DNA sequence surrounding the gene suggests that it belongs to an operon that also contains several components of phosphotransferase system (PTS). The apparent role of the enzyme is to participate in arabitol catabolism via the arabitol phosphate route similar to the ribitol and xylitol catabolic routes described previously. Xylitol phosphate dehydrogenase (XPDH) was isolated from Lactobacillus rhamnosus strain ATCC 15820. The enzyme was partially sequenced. Amino acid sequences were used to isolate the gene encoding the enzyme. The homology comparisons of the deduced amino acid sequence of L. rhamnosus XPDH revealed several similar enzymes in genomes of various species of Gram-positive bacteria. Two enzymes of Clostridium difficile and an enzyme of Bacillus halodurans were cloned and their substrate specificities together with the substrate specificity of L. rhamnosus XPDH were compared. It was found that one of the XPDH enzymes of C. difficile and the XPDH of L. rhamnosus had the highest selectivity towards D-xylulose 5-phosphate. A known transketolase-deficient and D-ribose-producing mutant of Bacillus subtilis (ATCC 31094) was further modified by disrupting its rpi (D-ribose phosphate isomerase) gene to create D-ribulose- and D-xylulose-producing strain. Expression of APDH of E. avium and XPDH of L. rhamnosus and C. difficile in D-ribulose- and D-xylulose-producing strain of B. subtilis resulted in strains capable of converting D-glucose into D-arabitol and xylitol, respectively. The D-arabitol yield on D-glucose was 38 % (w/w). Xylitol production was accompanied by co-production of ribitol limiting xylitol yield to 23 %.

Next-generation sequencing: A frameshift in skeletal dysplasia gene discovery

In the last decade, huge breakthroughs in genetics - driven by new technology and different statistical approaches - have resulted in a plethora of new disease genes identified for both common and rare diseases. Massive parallel sequencing, commonly known as next-generation sequencing, is the latest advance in genetics, and has already facilitated the discovery of the molecular cause of many monogenic disorders. This article describes this new technology and reviews how this approach has been used successfully in patients with skeletal dysplasias. Moreover, this article illustrates how the study of rare diseases can inform understanding and therapeutic developments for common diseases such as osteoporosis. © International Osteoporosis Foundation and National Osteoporosis Foundation 2013.

Toward the Discovery of Vaccine Adjuvants: Coupling In Silico Screening and In Vitro Analysis of Antagonist Binding to Human and Mouse CCR4 Receptors

Background: Adjuvants enhance or modify an immune response that is made to an antigen. An antagonist of the chemokine CCR4 receptor can display adjuvant-like properties by diminishing the ability of CD4+CD25+ regulatory T cells (Tregs) to down-regulate immune responses. Methodology: Here, we have used protein modelling to create a plausible chemokine receptor model with the aim of using virtual screening to identify potential small molecule chemokine antagonists. A combination of homology modelling and molecular docking was used to create a model of the CCR4 receptor in order to investigate potential lead compounds that display antagonistic properties. Three-dimensional structure-based virtual screening of the CCR4 receptor identified 116 small molecules that were calculated to have a high affinity for the receptor; these were tested experimentally for CCR4 antagonism. Fifteen of these small molecules were shown to inhibit specifically CCR4-mediated cellmigration, including that of CCR4(+) Tregs. Significance: Our CCR4 antagonists act as adjuvants augmenting human T cell proliferation in an in vitro immune response model and compound SP50 increases T cell and antibody responses in vivo when combined with vaccine antigens of Mycobacterium tuberculosis and Plasmodium yoelii in mice.

Computational systems approach for drug target discovery

Importance of the field: The shift in focus from ligand based design approaches to target based discovery over the last two to three decades has been a major milestone in drug discovery research. Currently, it is witnessing another major paradigm shift by leaning towards the holistic systems based approaches rather the reductionist single molecule based methods. The effect of this new trend is likely to be felt strongly in terms of new strategies for therapeutic intervention, new targets individually and in combinations, and design of specific and safer drugs. Computational modeling and simulation form important constituents of new-age biology because they are essential to comprehend the large-scale data generated by high-throughput experiments and to generate hypotheses, which are typically iterated with experimental validation. Areas covered in this review: This review focuses on the repertoire of systems-level computational approaches currently available for target identification. The review starts with a discussion on levels of abstraction of biological systems and describes different modeling methodologies that are available for this purpose. The review then focuses on how such modeling and simulations can be applied for drug target discovery. Finally, it discusses methods for studying other important issues such as understanding targetability, identifying target combinations and predicting drug resistance, and considering them during the target identification stage itself. What the reader will gain: The reader will get an account of the various approaches for target discovery and the need for systems approaches, followed by an overview of the different modeling and simulation approaches that have been developed. An idea of the promise and limitations of the various approaches and perspectives for future development will also be obtained. Take home message: Systems thinking has now come of age enabling a `bird's eye view' of the biological systems under study, at the same time allowing us to `zoom in', where necessary, for a detailed description of individual components. A number of different methods available for computational modeling and simulation of biological systems can be used effectively for drug target discovery.

Automated Discovery of Structured Process Models: Discover Structured vs. Discover and Structure

This paper addresses the problem of discovering business process models from event logs. Existing approaches to this problem strike various tradeoffs between accuracy and understandability of the discovered models. With respect to the second criterion, empirical studies have shown that block-structured process models are generally more understandable and less error-prone than unstructured ones. Accordingly, several automated process discovery methods generate block-structured models by construction. These approaches however intertwine the concern of producing accurate models with that of ensuring their structuredness, sometimes sacrificing the former to ensure the latter. In this paper we propose an alternative approach that separates these two concerns. Instead of directly discovering a structured process model, we first apply a well-known heuristic technique that discovers more accurate but sometimes unstructured (and even unsound) process models, and then transform the resulting model into a structured one. An experimental evaluation shows that our “discover and structure” approach outperforms traditional “discover structured” approaches with respect to a range of accuracy and complexity measures.

Mass spectrometry and n-in-one analytics in early drug discovery: Combinatorial chemistry libraries, lipophilicity and absorption screening

This thesis describes current and past n-in-one methods and presents three early experimental studies using mass spectrometry and the triple quadrupole instrument on the application of n-in-one in drug discovery. N-in-one strategy pools and mix samples in drug discovery prior to measurement or analysis. This allows the most promising compounds to be rapidly identified and then analysed. Nowadays properties of drugs are characterised earlier and in parallel with pharmacological efficacy. Studies presented here use in vitro methods as caco-2 cells and immobilized artificial membrane chromatography for drug absorption and lipophilicity measurements. The high sensitivity and selectivity of liquid chromatography mass spectrometry are especially important for new analytical methods using n-in-one. In the first study, the fragmentation patterns of ten nitrophenoxy benzoate compounds, serial homology, were characterised and the presence of the compounds was determined in a combinatorial library. The influence of one or two nitro substituents and the alkyl chain length of methyl to pentyl on collision-induced fragmentation was studied, and interesting structurefragmentation relationships were detected. Two nitro group compounds increased fragmentation compared to one nitro group, whereas less fragmentation was noted in molecules with a longer alkyl chain. The most abundant product ions were nitrophenoxy ions, which were also tested in the precursor ion screening of the combinatorial library. In the second study, the immobilized artificial membrane chromatographic method was transferred from ultraviolet detection to mass spectrometric analysis and a new method was developed. Mass spectra were scanned and the chromatographic retention of compounds was analysed using extract ion chromatograms. When changing detectors and buffers and including n-in-one in the method, the results showed good correlation. Finally, the results demonstrated that mass spectrometric detection with gradient elution can provide a rapid and convenient n-in-one method for ranking the lipophilic properties of several structurally diverse compounds simultaneously. In the final study, a new method was developed for caco-2 samples. Compounds were separated by liquid chromatography and quantified by selected reaction monitoring using mass spectrometry. This method was used for caco-2 samples, where absorption of ten chemically and physiologically different compounds was screened using both single and nin- one approaches. These three studies used mass spectrometry for compound identification, method transfer and quantitation in the area of mixture analysis. Different mass spectrometric scanning modes for the triple quadrupole instrument were used in each method. Early drug discovery with n-in-one is area where mass spectrometric analysis, its possibilities and proper use, is especially important.

Essays on Market Microstructure: Price Discovery and Informed Trading

Market microstructure is “the study of the trading mechanisms used for financial securities” (Hasbrouck (2007)). It seeks to understand the sources of value and reasons for trade, in a setting with different types of traders, and different private and public information sets. The actual mechanisms of trade are a continually changing object of study. These include continuous markets, auctions, limit order books, dealer markets, or combinations of these operating as a hybrid market. Microstructure also has to allow for the possibility of multiple prices. At any given time an investor may be faced with a multitude of different prices, depending on whether he or she is buying or selling, the quantity he or she wishes to trade, and the required speed for the trade. The price may also depend on the relationship that the trader has with potential counterparties. In this research, I touch upon all of the above issues. I do this by studying three specific areas, all of which have both practical and policy implications. First, I study the role of information in trading and pricing securities in markets with a heterogeneous population of traders, some of whom are informed and some not, and who trade for different private or public reasons. Second, I study the price discovery of stocks in a setting where they are simultaneously traded in more than one market. Third, I make a contribution to the ongoing discussion about market design, i.e. the question of which trading systems and ways of organizing trading are most efficient. A common characteristic throughout my thesis is the use of high frequency datasets, i.e. tick data. These datasets include all trades and quotes in a given security, rather than just the daily closing prices, as in traditional asset pricing literature. This thesis consists of four separate essays. In the first essay I study price discovery for European companies cross-listed in the United States. I also study explanatory variables for differences in price discovery. In my second essay I contribute to earlier research on two issues of broad interest in market microstructure: market transparency and informed trading. I examine the effects of a change to an anonymous market at the OMX Helsinki Stock Exchange. I broaden my focus slightly in the third essay, to include releases of macroeconomic data in the United States. I analyze the effect of these releases on European cross-listed stocks. The fourth and last essay examines the uses of standard methodologies of price discovery analysis in a novel way. Specifically, I study price discovery within one market, between local and foreign traders.