Chemical Entities of Biological Interest: an update

Chemical Entities of Biological Interest (ChEBI) is a freely available dictionary of molecular entities focused on ‘small’ chemical compounds. The molecular entities in question are either natural products or synthetic products used to intervene in the processes of living organisms. Genome-encoded macromolecules (nucleic acids, proteins and peptides derived from proteins by cleavage) are not as a rule included in ChEBI. In addition to molecular entities, ChEBI contains groups (parts of molecular entities) and classes of entities. ChEBI includes an ontological classification, whereby the relationships between molecular entities or classes of entities and their parents and/or children are specified. ChEBI is available online at http://www.ebi.ac.uk/chebi/. This article reports on new features in ChEBI since the last NAR report in 2007, including substructure and similarity searching, a submission tool for authoring of ChEBI datasets by the community and a 30-fold increase in the number of chemical structures stored in ChEBI

Adaptive Approach for Modelling Variability in Pharmacokinetics

We present an improved adaptive approach for studying systems of ODEs affected by parameter variability and state space uncertainty. Our approach is based on a reformulation of the ODE problem as a transport problem of a probability density describing the evolution of the ensemble of systems in time. The resulting multidimensional problem is solved by representing the probability density w.r.t. an adaptively chosen Galerkin ansatz space of Gaussian distributions. Due to our improvements in adaptivity control, we substantially improved the overall performance of the original algorithm and moreover inherited the theoretical property that the number of Gaussian distribution stays constant for linear ODEs to the numerical scheme. We illustrate the approach in application to dynamical systems describing the pharmacokinetics of drugs and xenobiotics, where variability in physiological parameters is important to be considered

Thermodynamics and structure of methionine enkephalin using the statistical temperature molecular dynamics algorithm

Kim, Straub, and Keyes introduced the statistical temperature molecular dynamics (STMD) algorithm to overcome broken ergodicity by sampling a non­-Boltzmann flat energy histogram as noted in Kim, Straub, and Keyes, Phys. Rev. Lett. 97: 050601 (2007). Canonical averages are calculated via reweighting to the desired temperature. While STMD is promising, its application has been almost entirely to simple or model systems. In this dissertation the implementation of STMD into the biosimulation package CHARMM is used to simulate the methionine enkephalin pentamer peptide with a methione terminal cap in a droplet of CHARMM TIP3P water molecules. Chain thermodynamics is analyzed from the novel perspective of the statistical temperature as a function of potential energy, $TS(U), automatically generated by STMD. Both the minimum in the slope of$TS(U), and the peak in the heat capacity as a function of temperature, calculated via reweighting, indicate a collapse transition at Tθ ≈ 253K. Distributions of dihedral angles are obtained as a function of temperature. Rotamer regions found in the literature are reproduced, along with unique regions not found previously, including with advanced algorithms, indicating the power of STMD enhanced sampling

A framework for knowledge discovery within business intelligence for decision support

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Business Intelligence (BI) techniques provide the potential to not only efficiently manage but further analyse and apply the collected information in an effective manner. Benefiting from research both within industry and academia, BI provides functionality for accessing, cleansing, transforming, analysing and reporting organisational datasets. This provides further opportunities for the data to be explored and assist organisations in the discovery of correlations, trends and patterns that exist hidden within the data. This hidden information can be employed to provide an insight into opportunities to make an organisation more competitive by allowing manager to make more informed decisions and as a result, corporate resources optimally utilised. This potential insight provides organisations with an unrivalled opportunity to remain abreast of market trends. Consequently, BI techniques provide significant opportunity for integration with Decision Support Systems (DSS). The gap which was identified within the current body of knowledge and motivated this research, revealed that currently no suitable framework for BI, which can be applied at a meta-level and is therefore tool, technology and domain independent, currently exists. To address the identified gap this study proposes a meta-level framework: - ‘KDDS-BI’, which can be applied at an abstract level and therefore structure a BI investigation, irrespective of the end user. KDDS-BI not only facilitates the selection of suitable techniques for BI investigations, reducing the reliance upon ad-hoc investigative approaches which rely upon ‘trial and error’, yet further integrates Knowledge Management (KM) principles to ensure the retention and transfer of knowledge due to a structured approach to provide DSS that are based upon the principles of BI. In order to evaluate and validate the framework, KDDS-BI has been investigated through three distinct case studies. First KDDS-BI facilitates the integration of BI within ‘Direct Marketing’ to provide innovative solutions for analysis based upon the most suitable BI technique. Secondly, KDDS-BI is investigated within sales promotion, to facilitate the selection of tools and techniques for more focused in store marketing campaigns and increase revenue through the discovery of hidden data, and finally, operations management is analysed within a highly dynamic and unstructured environment of the London Underground Ltd. network through unique a BI solution to organise and manage resources, thereby increasing the efficiency of business processes. The three case studies provide insight into not only how KDDS-BI provides structure to the integration of BI within business process, but additionally the opportunity to analyse the performance of KDDS-BI within three independent environments for distinct purposes provided structure through KDDS-BI thereby validating and corroborating the proposed framework and adding value to business processes

A framework for knowledge discovery within business intelligence for decision support

Business Intelligence (BI) techniques provide the potential to not only efficiently manage but further analyse and apply the collected information in an effective manner. Benefiting from research both within industry and academia, BI provides functionality for accessing, cleansing, transforming, analysing and reporting organisational datasets. This provides further opportunities for the data to be explored and assist organisations in the discovery of correlations, trends and patterns that exist hidden within the data. This hidden information can be employed to provide an insight into opportunities to make an organisation more competitive by allowing manager to make more informed decisions and as a result, corporate resources optimally utilised. This potential insight provides organisations with an unrivalled opportunity to remain abreast of market trends. Consequently, BI techniques provide significant opportunity for integration with Decision Support Systems (DSS). The gap which was identified within the current body of knowledge and motivated this research, revealed that currently no suitable framework for BI, which can be applied at a meta-level and is therefore tool, technology and domain independent, currently exists. To address the identified gap this study proposes a meta-level framework: - ‘KDDS-BI’, which can be applied at an abstract level and therefore structure a BI investigation, irrespective of the end user. KDDS-BI not only facilitates the selection of suitable techniques for BI investigations, reducing the reliance upon ad-hoc investigative approaches which rely upon ‘trial and error’, yet further integrates Knowledge Management (KM) principles to ensure the retention and transfer of knowledge due to a structured approach to provide DSS that are based upon the principles of BI. In order to evaluate and validate the framework, KDDS-BI has been investigated through three distinct case studies. First KDDS-BI facilitates the integration of BI within ‘Direct Marketing’ to provide innovative solutions for analysis based upon the most suitable BI technique. Secondly, KDDS-BI is investigated within sales promotion, to facilitate the selection of tools and techniques for more focused in store marketing campaigns and increase revenue through the discovery of hidden data, and finally, operations management is analysed within a highly dynamic and unstructured environment of the London Underground Ltd. network through unique a BI solution to organise and manage resources, thereby increasing the efficiency of business processes. The three case studies provide insight into not only how KDDS-BI provides structure to the integration of BI within business process, but additionally the opportunity to analyse the performance of KDDS-BI within three independent environments for distinct purposes provided structure through KDDS-BI thereby validating and corroborating the proposed framework and adding value to business processes.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Interrogation d'un réseau sémantique de documents : l'intertextualité dans l'accès à l'information juridique

A collection of documents is generally represented as a set of documents but this simple representation does not take into account cross references between documents, which often defines their context of interpretation. This standard document model is less adapted for specific professional uses in specialized domains in which documents are related by many various references and the access tools need to consider this complexity. We propose two models based on formal and relational concept analysis and on semantic web techniques. Applied on documentary objects, these two models represent and query in a unified way documents content descriptors and documents relations.Une collection documentaire est généralement représentée comme un ensemble de documents mais cette modélisation ne permet pas de rendre compte des relations intertextuelles et du contexte d'interprétation d'un document. Le modèle documentaire classique trouve ses limites dans les domaines spécialisés où les besoins d'accès à l'information correspondent à des usages spécifiques et où les documents sont liés par de nombreux types de relations. Ce travail de thèse propose deux modèles permettant de prendre en compte cette complexité des collections documentaire dans les outils d'accès à l'information. Le premier modèle est basée sur l'analyse formelle et relationnelle de concepts, le deuxième est basée sur les technologies du web sémantique. Appliquées sur des objets documentaires ces modèles permettent de représenter et d'interroger de manière unifiée les descripteurs de contenu des documents et les relations intertextuelles qu'ils entretiennent

Investigation of the Iron(III)-Siderophore Binding Properties of Three Bacterial Periplasmic Binding Proteins

Bacteria possess complex machinery for uptake of essential iron. Studying iron-uptake provides insight into requirements for bacterial growth, and for development of applications, including novel antibiotics and diagnostic tools. The presented project investigates the binding of periplasmic binding proteins CeuE, FepB and VctP with a range of iron(III)-siderophore and siderophore-mimic compounds. CeuE was shown to bind iron(III)-n-LICAM (n= 6, 8) (N,N'-hexane-1,6-diylbis (2,3-dihydroxybenzamide and N,N'-octane-1,8-diylbis (2,3-dihydroxybenzamide)) tetradentate siderophore mimics, with Λ-configuration in both crystal and solution phase, with binding constants of 33 ±8 and 58 ± 8 nM respectively. Comparing these results to those for iron(III)-n-LICAM (n= 4, 5) (N,N'-butane-1,4-diylbis (2,3-dihydroxybenzamide) and N,N'-pentane-1,5-diylbis (2,3-dihydroxybenzamide)) revealed that the highest affinity was found for a five-atom linker. Mutagenesis of His 227 and Tyr 288 that coordinate the iron(III)-centre, proved that Tyr 288 is essential for iron(III)-n-LICAM binding to CeuE. Binding affinity is slightly reduced for all iron(III)-n-LICAM (n= 4, 5, 6, 8) ligands with mutation to His 227. CeuE-H227L-iron(III)-5-LICAM crystal structure determination revealed that iron(III)-5-LICAM bound in the Λ-configuration with one aqua-ligand. Salmochelin mimic siderophores Sal-n-LICAM (n= 4, 5) were synthesised and iron(III)-binding established via Job plot. For both compounds, equilibria of 1:1 and 3:2 ligand:metal ratios were observed, with 3:2 predominating over time. CeuE bound both iron(III)-complexes weakly in the Λ-configuration. Iron(III)-Sal-5-LICAM bound with higher affinity (511 ±76 nM) than iron(III)-Sal-4-LICAM (15.6 ± 2.3 µM). FepB was overexpressed, purified, and its siderophore-binding profile compared with that of CeuE and VctP. It was shown that FepB bound iron(III)-enterobactin with nanomolar affinity, and had micromolar affinity for tetradentate catecholate complexes. CeuE bound tetradentate catecholate complexes with nanomolar affinity, and iron(III)-enterobactin with micromolar affinity. VctP bound tetradentate catecholate complexes with picomolar affinity, iron(III)-enterobactin with mid-nanomolar affinity, and iron(III)-Sal-n-LICAM (n= 4, 5) with low-nanomolar affinity. All three proteins bound iron(III)-MECAM (1,3,5-N,N',N″-tris-(2,3-dihydroxybenzoyl)-triaminomethylbenzene) with low nanomolar affinity