In silico modeling of chemical and biological interactions at different scales

En les últimes dècades, molts països han imposat regulacions sobre els efectes potencials de les substàncies químiques envers la salut humana i els criteris mediambientals. A més a més, tenint en compte el temps necessari per a les proves d’avaluació dels efectes de gran nombre de productes químics i el seu cost ha produït un ràpid augment en el nombre de models computacionals, que relacionen l'estructura de les substàncies químiques amb la seva activitat biològica. Actualment existeixen els models de relació estructura-activitat (SAR) per a productes químics, utilitzant un enfocament similar s’ha desenvolupat un nou model i generat conjunts d'alertes metabòliques que es puguin utilitzar juntament amb els mètodes Q(SAR). Aquest treball presenta regles SAR per a la predicció de mutagenicitat in vitro, juntament amb alertes metabòliques per a la predicció in vivo. Permetent, obtenir una idea preliminar sobre si un producte químic exhibeix el mateix comportament mutagènic in vitro i in vivo. Entre els compostos químics, les nanopartícules, també s'estan utilitzant cada cop més a través de diferents classes de productes usats pels consumidors. En un context fisiològic, la corona de les proteïnes constitueix la interfície entre les nanopartícules i les cèl·lules. En aquest treball, s'han utilitzat les propietats fisicoquímiques de la corona de les proteïnes per tal de desenvolupar un model capaç de predir l'associació cel·lular. Finalment, aquesta tesi es centra en el tema de la resistència als fàrmacs en els bacteris, que s'ha convertit en un assumpte d'interès global. Amb l'augment de la resistència dels bacteris als antibiòtics, és important disposar d'informació sobre la resposta que les noves proteïnes bacterianes tindrien sobre els antibiòtics actualment disponibles. Pel qual, en aquest treball s'ha desenvolupat un mètode d'alineació lliure per millorar la classificació en perfils de resistència de les proteïnes bacterianes, en base a les seves propietats fisicoquímiques.En las últimas décadas, muchos países han impuesto regulaciones sobre los efectos potenciales de las sustancias químicas con respecto a la salud humana y a criterios medio ambientales. Además, el tiempo necesario para las pruebas de evaluación de los efectos de un gran número de productos químicos y su coste ha producido un rápido aumento en el número de modelos computacionales que relacionan la estructura de las sustancias químicas con su actividad biológica. Actualmente existen los modelos de relación estructura-actividad (SAR) para productos químicos, utilizando un enfoque similar se ha desarrollado un nuevo modelo para generar conjuntos de alertas metabólicas que puedan utilizarse junto con los métodos Q(SAR). Este trabajo presenta reglas SAR para la predicción de mutagenicidad in vitro, junto con alertas metabólicas para la predicción también in vivo. Permitiendo, además, obtener una idea preliminar de si un producto químico exhibe el mismo comportamiento mutagénico in vitro e in vivo. Entre los compuestos químicos, las nanopartículas, también se están utilizando cada vez más en diferentes clases de productos usados por los consumidores. En términos fisiológicos, la corona de las proteínas constituye la interfaz entre las nanopartículas y las células. En este trabajo se ha desarrollado un modelo con las propiedades físico-químicas de la corona de las proteínas para predecir la asociación celular. Por último, esta tesis se centra en el tema de la resistencia a los fármacos en las bacterias, que se ha convertido en un asunto de interés global. Con el aumento de la resistencia de las bacterias a los antibióticos, es importante disponer información sobre la respuesta que las nuevas proteínas bacterianas tendrán sobre los antibióticos actualmente disponibles. Por esto se ha desarrollado un método de alineación libre para mejorar la clasificación en perfiles de resistencia de las proteínas bacterianas en base a sus propiedades físico-químicas.In the past decades, government, society and industry at large have taken keen interest in the impact at different scales that exposure to chemicals has on humans and environment. Many countries governments have imposed regulations as per which it has become important to establish the potential effects of these chemical entities with respect to human health and environmental endpoints. Given the time taken by traditional tests, costs and large number of chemicals to be evaluated, there has been a rapid growth in the number of computational models that link the structure of chemicals to their biological activity. To extend the basis of knowledge that currently exists in Structure Activity Relationship (SAR) models for chemicals, a similar approach was used to develop a new model and generate sets of metabolic triggers which can be used together with Q(SAR) methods. This thesis presents SAR rules for prediction of mutagenicity in vitro, along with metabolic triggers for prediction of mutagenicity in vitro and in vivo. Along with chemical compounds, nanoparticles are also being used increasingly across different classes of consumers’ products. Since, in physiological context, the protein corona constitutes the interface between the nanoparticle and cells, it plays a fundamental role in nanoparticle-cell association. In this thesis, the physicochemical properties of protein corona were used to develop a model to predict cell association. Lastly, this thesis focuses on the topic of drug resistance in bacteria, which has become a matter of global concern. With bacteria growing resistant to antibiotics at a faster pace than discovery of new antibiotics, information on the response that new bacterial proteins would have to the currently available antibiotics, based on their similarity with the known antibiotic-resistant proteins is necessary. An alignment-free method was developed to improve the resistance profile classification of bacterial proteins based on their physicochemical properties

Mesoscopic descriptions of complex networks

[spa] El objetivo de la presente tesis es el estudio de las subestructuras que aparecen a un nivel de resolución mesoscópico en las redes complejas. Dichas subestructuras, que en el campo de las redes complejas son denominadas comunidades, intentan agrupar los nodos de una red de manera que los nodos que forman parte de una misma comunidad estén más conectados entre ellos que con el resto de nodos de la red. La importada del análisis de estas estructuras radica en que nos permiten comprender mejor las redes complejas dándonos información sobre la funcionalidad de las comunidades que las componen. Hemos llevado a cabo el estudio de estas estructuras mesoscópicas utilizando la información topológica de las redes, y en cuanto a los métodos empleados éstos se pueden agrupar en dos grandes familias conocidas habitualmente como clustering jerárquico y clustering modular. Dentro de la primera familia de métodos nos hemos fijado en la existencia de un problema de no unicidad en el clustering jerárquico aglomerativo, y hemos propuesto una solución a dicho problema basada en el uso de una nueva herramienta de clasificación que denominamos multidendrograma. A continuación, hemos aplicado el resultado de una clasificación jerárquica para resolver un problema dentro de las redes complejas financieras. Más concretamente, hemos aprovechado una partición en clusters para resolver de manera más eficiente el problema de optimizar una cartera de valores. Por lo que respecta a la segunda familia de métodos de clustering estudiados, ésta se basa en la optimización de una función objetivo llamada modularidad El inconveniente que presenta la optimización de la modularidad es su elevado coste computacional, la cual cosa nos ha llevado a idear una reducción analítica del tamaño de las redes complejas de manera que se conserva toda la información necesaria en la red original de cara a hallar la estructura de comunidades que optimice la modularidad. A continuación hemos podido utilizar dicha simplificación de los cálculos en el análisis de toda la mesoescala topológica de las redes complejas. Dicho mesoescala la hemos estudiado añadiendo un mismo valor a todos los nodos de una red que mide su resistencia a formar parte de comunidades, La optimización de la modularidad para estas nuevas instancias de la red original obtenidas a partir de unos valores de resistencia acotados analíticamente, nos permite analizar la mesoescala topológica de las redes. Por último, hemos propuesto una generalización de la función de modularidad donde los bloques constituyentes ya no son solamente arcos sino que pueden ser distintos tipos de motifs. Esto nos permite obtener descripciones más generales de grupos de nodos que incluyen como caso particular a las comunidades

Wavelet analysis of human DNA

This paper studies the human DNA in the perspective of signal processing. Six wavelets are tested for analyzing the information content of the human DNA. By adopting real Shannon wavelet several fundamental properties of the code are revealed. A quantitative comparison of the chromosomes and visualization through multidimensional and dendograms is developed

COalitions in COOperation Networks (COCOON):Social Network Analysis and Game Theory to Enhance Cooperation Networks

Sie, R. L. L. (2012). COalitions in COOperation Networks (COCOON): Social Network Analysis and Game Theory to Enhance Cooperation Networks (Unpublished doctoral dissertation). September, 28, 2012, Open Universiteit in the Netherlands (CELSTEC), Heerlen, The Netherlands.IdSpace, SIK

Measuring Expressive Music Performances: a Performance Science Model using Symbolic Approximation

Music Performance Science (MPS), sometimes termed systematic musicology in Northern Europe, is concerned with designing, testing and applying quantitative measurements to music performances. It has applications in art musics, jazz and other genres. It is least concerned with aesthetic judgements or with ontological considerations of artworks that stand alone from their instantiations in performances. Musicians deliver expressive performances by manipulating multiple, simultaneous variables including, but not limited to: tempo, acceleration and deceleration, dynamics, rates of change of dynamic levels, intonation and articulation. There are significant complexities when handling multivariate music datasets of significant scale. A critical issue in analyzing any types of large datasets is the likelihood of detecting meaningless relationships the more dimensions are included. One possible choice is to create algorithms that address both volume and complexity. Another, and the approach chosen here, is to apply techniques that reduce both the dimensionality and numerosity of the music datasets while assuring the statistical significance of results. This dissertation describes a flexible computational model, based on symbolic approximation of timeseries, that can extract time-related characteristics of music performances to generate performance fingerprints (dissimilarities from an ‘average performance’) to be used for comparative purposes. The model is applied to recordings of Arnold Schoenberg’s Phantasy for Violin with Piano Accompaniment, Opus 47 (1949), having initially been validated on Chopin Mazurkas.1 The results are subsequently used to test hypotheses about evolution in performance styles of the Phantasy since its composition. It is hoped that further research will examine other works and types of music in order to improve this model and make it useful to other music researchers. In addition to its benefits for performance analysis, it is suggested that the model has clear applications at least in music fraud detection, Music Information Retrieval (MIR) and in pedagogical applications for music education

Guanine Holes Are Prominent Targets for Mutation in Cancer and Inherited Disease

Albino Bacolla, Guliang Wang, Aklank Jain, Karen M. Vasquez, Division of Pharmacology and Toxicology, The University of Texas at Austin, Dell Pediatric Research Institute, Austin, Texas, United States of AmericaAlbino Bacolla, Nuri A. Temiz, Ming Yi, Joseph Ivanic, Regina Z. Cer, Duncan E. Donohue, Uma S. Mudunuri, Natalia Volfovsky, Brian T. Luke, Robert M., Stephens, Jack R. Collins, Advanced Biomedical Computing Center, SAIC-Frederick, Inc., Frederick National Laboratory for Cancer Research, Frederick, Maryland, United States of AmericaEdward V. Ball, David N. Cooper, Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, United KingdomSingle base substitutions constitute the most frequent type of human gene mutation and are a leading cause of cancer and inherited disease. These alterations occur non-randomly in DNA, being strongly influenced by the local nucleotide sequence context. However, the molecular mechanisms underlying such sequence context-dependent mutagenesis are not fully understood. Using bioinformatics, computational and molecular modeling analyses, we have determined the frequencies of mutation at G•C bp in the context of all 64 5′-NGNN-3′ motifs that contain the mutation at the second position. Twenty-four datasets were employed, comprising >530,000 somatic single base substitutions from 21 cancer genomes, >77,000 germline single-base substitutions causing or associated with human inherited disease and 16.7 million benign germline single-nucleotide variants. In several cancer types, the number of mutated motifs correlated both with the free energies of base stacking and the energies required for abstracting an electron from the target guanines (ionization potentials). Similar correlations were also evident for the pathological missense and nonsense germline mutations, but only when the target guanines were located on the non-transcribed DNA strand. Likewise, pathogenic splicing mutations predominantly affected positions in which a purine was located on the non-transcribed DNA strand. Novel candidate driver mutations and tissue-specific mutational patterns were also identified in the cancer datasets. We conclude that electron transfer reactions within the DNA molecule contribute to sequence context-dependent mutagenesis, involving both somatic driver and passenger mutations in cancer, as well as germline alterations causing or associated with inherited disease.This work was supported by grants from the NIH (CA097175 and CA093729) to KMV, NCI/NIH contract HHSN261200800001E to AB and the Frederick National Laboratory for Cancer Research, and CBIIT/caBIG ISRCE yellow task #09-260 to the Frederick National Laboratory for Cancer Research. DNC and EVB received financial support from BIOBASE GmbH through a license agreement (for HGMD) with Cardiff University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.PharmacyEmail: [email protected]

Exploratory data analysis using network based techniques

The aim of this document is to present the work done during the development of my master thesis. The work belongs to the field of complex networks, more concretely to the detection of communities in complex networks. Chapter 1 will be an introduction of the basic concepts and motivations of this work, mainly clarifying the fields of exploratory data analysis, data clustering and complex networks. As all the work is about the finding of communities in complex networks, Chapter 2 is devoted to explain the concepts of mesoscopic structure of networks and its importance in the analysis of real networks, along with the explanations of some of the most well-known techniques to perform this analysis. All the progress done during the master thesis relies on a method for detecting communities developed in the past years by the research group I belong to. This method is known as the AFG algorithm, named after the three authors Arenas, Fernández and Gómez, and it is explained in section 2.5.2 with special emphasis. The work that I have developed is composed of two separate problems: the first one consists in designing an application to make possible the use of the AFG community detection method to perform data clustering over real world multidimensional datasets, which is explained in Chapter 3. The second work consists in improving the AFG method to make possible the detection of communities even when the difference of sizes of the communities make their detection impossible for other community detection algorithms, which can be found in Chapter 4. Chapter 5 contains the conclusions and the future lines of research derived from the present work, and in the Appendix there is a list of publications that sustain the contents presented in this document

Frameworks and tools for risk assessment of manufactured nanomaterials

Commercialization of nanotechnologies entails a regulatory requirement for understanding their environmental, health and safety (EHS) risks. Today we face challenges to assess these risks, which emerge from uncertainties around the interactions of manufactured nanomaterials (MNs) with humans and the environment. In order to reduce these uncertainties, it is necessary to generate sound scientific data on hazard and exposure by means of relevant frameworks and tools. The development of such approaches to facilitate the risk assessment (RA) of MNs has become a dynamic area of research. The aim of this paper was to review and critically analyse these approaches against a set of relevant criteria. The analysis concluded that none of the reviewed frameworks were able to fulfill all evaluation criteria. Many of the existing modelling tools are designed to provide screening-level assessments rather than to support regulatory RA and risk management. Nevertheless, there is a tendency towards developing more quantitative, higher-tier models, capable of incorporating uncertainty into their analyses. There is also a trend towards developing validated experimental protocols for material identification and hazard testing, reproducible across laboratories. These tools could enable a shift from a costly case-by-case RA of MNs towards a targeted, flexible and efficient process, based on grouping and read-across strategies and compliant with the 3R (Replacement, Reduction, Refinement) principles. In order to facilitate this process, it is important to transform the current efforts on developing databases and computational models into creating an integrated data and tools infrastructure to support the risk assessment and management of MNs.Commercialization of nanotechnologies entails a regulatory requirement for understanding their environmental, health and safety (EHS) risks. Today we face challenges to assess these risks, which emerge from uncertainties around the interactions of manufactured nanomaterials (MNs) with humans and the environment. In order to reduce these uncertainties, it is necessary to generate sound scientific data on hazard and exposure by means of relevant frameworks and tools. The development of such approaches to facilitate the risk assessment (RA) of MNs has become a dynamic area of research. The aim of this paper was to review and critically analyse these approaches against a set of relevant criteria. The analysis concluded that none of the reviewed frameworks were able to fulfill all evaluation criteria. Many of the existing modelling tools are designed to provide screening level assessments rather than to support regulatory RA and risk management Nevertheless, there is a tendency towards developing more quantitative, higher-tier models, capable of incorporating uncertainty into their analyses. There is also a trend towards developing validated experimental protocols for material identification and hazard testing, reproducible across laboratories. These tools could enable a shift from a costly case-by-case RA of MNs towards a targeted, flexible and efficient process, based on grouping and read-across strategies and compliant with the 3R (Replacement, Reduction, Refinement) principles. In order to facilitate this process, it is important to transform the current efforts on developing databases and computational models into creating an integrated data and tools infrastructure to support the risk assessment and management of MNs. (C) 2016 Elsevier Ltd. All rights reserved

Theoretical-experimental study on protein-ligand interactions based on thermodynamics methods, molecular docking and perturbation models

The current doctoral thesis focuses on understanding the thermodynamic events of protein-ligand interactions which have been of paramount importance from traditional Medicinal Chemistry to Nanobiotechnology. Particular attention has been made on the application of state-of-the-art methodologies to address thermodynamic studies of the protein-ligand interactions by integrating structure-based molecular docking techniques, classical fractal approaches to solve protein-ligand complementarity problems, perturbation models to study allosteric signal propagation, predictive nano-quantitative structure-toxicity relationship models coupled with powerful experimental validation techniques. The contributions provided by this work could open an unlimited horizon to the fields of Drug-Discovery, Materials Sciences, Molecular Diagnosis, and Environmental Health Sciences