Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

Assessment of climate change statistical downscaling methods: Application and comparison of two statistical methods to a single site in Lisbon

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia do Ambiente

New fluorescent chemosensors based on bio-inspired ligands and macrocycles: from single molecules to nanoparticles

Dissertação apresentada para obtenção do grau de Doutor em Biotecnologia pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia. A presente dissertação foi preparada no âmbito do protocolo de acordo bilateral de educação avançada (ERASMUS) entre a Universidade de Vigo e a Universidade Nova de Lisboa

Produção de SCO (Single Cell Oil) para biodiesel a partir da microalga Chlorella protothecoides em diversos bioreactores com troca simbiótica de correntes gasosas

Dissertação para obtenção do Grau de Mestre em Biotecnologia

Single operation multiple data - paralelismo de dados ao nível da sub-rotina

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Development of novel human cellular models for neurotoxicity studies

Dissertation to obtain master degree in Genética Molecular e Biomedicina

Caracterização de estirpes Mycobacterium Tuberculosis da família Lisboa: análise de regiões de diferença e single nucleotide polymorphisms

Dissertação apresentada para a obtenção do Grau de Mestre em Genética Molecular e Biomedicina, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Functional and structural studies of two enzymes: membrane bound kinase and Z-DNA/Z-RNA-binding protein

Dissertação para obtenção do Grau de Doutor em Sistemas de Bioengenharia

A nanopore-based stochastic detection method: Single molecule characterisation of nanoparticles using ()- hemolysin

Dissertation presented to obtain the Ph.D degree in Chemistry.

Caracterização de múltiplos polimorfismos de Pneumocystis Jirovecii por multiplex-PCR/Single base extension

Pneumocystis jirovecii é conhecido por causar infecções específicas no aparelho respiratório de seus hospedeiros, principalmente em doentes imunocomprometidos, manifestando-se por uma pneumonia grave e por vezes fatal, normalmente designada por pneumonia por Pneumocystis. A caracterização da diversidade genética de P. jirovecii tem demonstrado que determinados polimorfismos de base única poderão ser reconhecidos como marcadores moleculares de eleição para o estudo da distribuição geográfica, vias de transmissão, resistência/susceptibilidade a fármacos, factores de virulência e genética populacional de subtipos genéticos. Este estudo teve como objectivo a caracterização de polimorfismos de P. jirovecii, através da metodologia PCR multiplex/Extensão de base única (do inglês single base extension), com a principal finalidade de constatar eventuais associações entre polimorfismos de base única, genótipos multilocus, e dados clínicos e demográficos da infecção. Sessenta e seis espécimes pulmonares, previamente considerados positivos para P. jirovecii, obtidos entre 2001 e 2012, a partir de doentes portugueses imunocomprometidos, foram seleccionados de forma aleatória para este estudo multilocus. PCR multiplex foi utilizada para a amplificação simultânea de três regiões genómicas: subunidade grande do rRNA mitocondrial, superóxido dismutase e dihidropteroato sintetase. Cinco polimorfismos de base única, previamente correlacionados com parâmetros da doença, foram genotipados por extensão de base única: mt85, SOD110, SOD215, DHPS165 e DHPS171. Um total de 330 polimorfismos de base única e 29 genótipos multilocus putativos de P. jirovecii foram identificados e caracterizados nos espécimes pulmonares analisados. Os padrões de distribuição dos polimorfismos foram analisados, sendo considerada a variação temporal e/ou geográfica das suas formas alélicas. Constatou-se grande diversidade genotípica entre os isolados de P. jirovecii que poderá ter influência a nível epidemiológico. Foram observadas associações estatísticas entre mt85/genótipos multilocus e parâmetros demográficos e clínicos. A correlação mais importante verificou-se entre mt85C e cargas parasitárias baixas a moderadas, enquanto mt85T foi associado com cargas parasitárias altas; MLG5, MLG9 e MLG13 foram associados com cargas parasitárias baixas, moderadas e altas, respectivamente. Tais associações demonstram que potenciais marcadores moleculares da infecção por P. jirovecii poderão existir e que polimorfismos/genótipos específicos poderão determinar perfis epidemiológicos da pneumonia por Pneumocystis. A análise genética cruzada permitiu verificar associações entre polimorfismos de base única. Os polimorfismos SOD110T e SOD215C, SOD110C e SOD215T, DHPS165A e DHPS171C, DHPS165G e DHPS171T foram associados estatisticamente. Os genótipos multilocus mais prevalentes foram considerados para o teste recombinatório d1. Dois genótipos multilocus (MLG7 e MLG9) foram observados com elevada frequência, e a análise genética indicou que estes se encontravam sobre-representados na população de P. jirovecii estudada. Estas evidências indicam que o fenómeno de desequilíbrio de ligação e a propagação clonal de subtipos genéticos é frequente, considerando que a espécie P. jirovecii poderá ser representada por uma população com estrutura epidémica. O presente trabalho confirmou a importância do estudo de polimorfismos em P. jirovecii, sugerindo que a caracterização multilocus poderá fornecer informação relevante para a compreensão dos padrões, causas e controlo da infecção, melhorando assim a investigação deste importante patogéneo.

NAV Portugal's performance within the single European Sky Initiative

NAV Portugal is the Air Navigation Service Provider in Portugal, providing air traffic control services in the airspace under the country’s responsibility. Recently, the company has been included in an initiative launched by the European Commission, called the Single European Sky. This aims for a unification of the European airspace, improving it in four main pillars: safety, capacity, environment, and cost-efficiency. To each of them, Key Performance Indicators need to be computed and monitored, all having pre-defined targets. The presented work project will be analyzing how NAV Portugal is doing in the pillar of capacity, proving suggestions if needed.

Control of a white organic light emitting diode’s emission parameters using a single doped RGB active layer

White Color tuning is an attractive feature that Organic Light Emitting Diodes (OLEDs) offer. Up until now, there hasn’t been any report that mix both color tuning abilities with device stability. In this work, White OLEDs (W-OLEDs) based on a single RGB blend composed of a blue emitting N,N′-Di(1-naphthyl)-N,N′-diphenyl-(1,1′-biphenyl)-4,4′-diamine (NPB) doped with a green emitting Coumarin-153 and a red emitting 4-(Dicyanomethylene)-2-methyl-6-(4-dimethylaminostyryl)-4H-pyran (DCM1) dyes were produced. The final device structure was ITO/Blend/Bathocuproine (BCP)/ Tris(8-hydroxyquinolinato)aluminium (Alq3)/Al with an emission area of 0.25 cm2. The effects of the changing in DCM1’s concentration (from 0.5% to 1% wt.) allowed a tuning in the final white color resulting in devices capable of emitting a wide range of tunes – from cool to warm – while also keeping a low device complexity and a high stabilitty. Moreover, an explanation on the optoelectrical behavior of the device is presented. The best electroluminescense (EL) points toward 160 cd/m2 of brightness and 1.1 cd/A of efficiency, both prompted to being enhanced. An Impedance Spectroscopy (IS) analysis allowed to study both the effects of BCP as a Hole Blocking Layer and as an aging probe of the device. Finally, as a proof of concept, the emission was increased 9 and 64 times proving this structure can be effectively applied for general lighting.

Dynamics of serotonergic neurons revealed by fiber photometry

This work was developed in the context of the MIT Portugal Program, area of Bioengineering Systems, in collaboration with the Champalimaud Research Programme, Champalimaud Center for the Unknown, Lisbon, Portugal. The project entitled Dynamics of serotonergic neurons revealed by fiber photometry was carried out at Instituto Gulbenkian de Ciência, Oeiras, Portugal and at the Champalimaud Research Programme, Champalimaud Center for the Unknown, Lisbon, Portugal

Targeting Heme with Single Domain Antibodies

Heme, i.e. iron (Fe) protoporphyrin IX, functions as a prosthetic group in a variety of hemoproteins that participate in vital biologic functions essential to sustain life. Heme is a highly reactive molecule, participating in redox reactions, and presumably for this reason it must be sequestered within the heme pockets of hemoproteins, controlling its reactivity. However, under biological stress conditions, hemoproteins can release their prosthetic groups, generating “free heme”, which binds loosely to proteins or to other molecules and presumably acquires unfettered redox activity. Moreover, a growing body of evidence supports the notion that “free heme” can act in a vasoactive, pro-inflammatory and cytotoxic manner when released from a subset of these hemoproteins, such as extracellular hemoglobin, generated during hemolytic conditions. (...)