Development of principle culture medium formulations for Saccharomyces cerevisiae

Saccharomyces cerevisiae as well as other microorganisms are frequently used in industry with the purpose of obtain different kind of products that can be applied in several areas (research investigation, pharmaceutical compounds, etc.). In order to obtain high yields for the desired product, it is necessary to make an adequate medium supplementation during the growth of the microorganisms. The higher yields are typically reached by using complex media, however the exact formulation of these media is not known. Moreover, it is difficult to control the exact composition of complex media, leading to batch-to-batch variations. So, to overcome this problem, some industries choose to use defined media, with a defined and known chemical composition. However these kind of media, many times, do not reach the same high yields that are obtained by using complex media. In order to obtain similar yield with defined media the addition of many different compounds has to be tested experimentally. Therefore, the industries use a set of empirical methods with which it is tried to formulate defined media that can reach the same high yields as complex media. In this thesis, a defined medium for Saccharomyces cerevisiae was developed using a rational design approach. In this approach a given metabolic network of Saccharomyces cerevisiae is divided into a several unique and not further decomposable sub networks of metabolic reactions that work coherently in steady state, so called elementary flux modes. The EFMtool algorithm was used in order to calculate the EFM’s for two Saccharomyces cerevisiae metabolic networks (amino acids supplemented metabolic network; amino acids non-supplemented metabolic network). For the supplemented metabolic network 1352172 EFM’s were calculated and then divided into: 1306854 EFM’s producing biomass, and 18582 EFM’s exclusively producing CO2 (cellular respiration). For the non-supplemented network 635 EFM’s were calculated and then divided into: 215 EFM’s producing biomass; 420 EFM’s producing exclusively CO2. The EFM’s of each group were normalized by the respective glucose consumption value. After that, the EFMs’ of the supplemented network were grouped again into: 30 clusters for the 1306854 EFMs producing biomass and, 20 clusters for the 18582 EFM’s producing CO2. For the non-supplemented metabolic network the respective EFM’s of each metabolic function were grouped into 10 clusters. After the clustering step, the concentrations of the other medium compounds were calculated by considering a reasonable glucose amount and by accounting for the proportionality between the compounds concentrations and the glucose ratios. The approach adopted/developed in this thesis may allow a faster and more economical way for media development.

A systems biology framework for pathway level culture media engineering: pplication to Pichia pastoris cultures

Dissertação para obtenção do Grau de Doutor em Engenharia Química e Bioquímica

Development of process control strategies exploiting knowledge from systems biology: application to MDCK suspension cells

Madine Darby Canine Kidney (MDCK) cell lines have been extensively evaluated for their potential as host cells for influenza vaccine production. Recent studies allowed the cultivation of these cells in a fully defined medium and in suspension. However, reaching high cell densities in animal cell cultures still remains a challenge. To address this shortcoming, a combined methodology allied with knowledge from systems biology was reported to study the impact of the cell environment on the flux distribution. An optimization of the medium composition was proposed for both a batch and a continuous system in order to reach higher cell densities. To obtain insight into the metabolic activity of these cells, a detailed metabolic model previously developed by Wahl A. et. al was used. The experimental data of four cultivations of MDCK suspension cells, grown under different conditions and used in this work came from the Max Planck Institute, Magdeburg, Germany. Classical metabolic flux analysis (MFA) was used to estimate the intracellular flux distribution of each cultivation and then combined with partial least squares (PLS) method to establish a link between the estimated metabolic state and the cell environment. The validation of the MFA model was made and its consistency checked. The resulted PLS model explained almost 70% of the variance present in the flux distribution. The medium optimization for the continuous system and for the batch system resulted in higher biomass growth rates than the ones obtained experimentally, 0.034 h-1 and 0.030 h-1, respectively, thus reducing in almost 10 hours the duplication time. Additionally, the optimal medium obtained for the continuous system almost did not consider pyruvate. Overall the proposed methodology seems to be effective and both proposed medium optimizations seem to be promising to reach high cell densities.

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.

Chemical analysis of 17th century Millefiori glasses excavated in the Monastery of Sta. Clara‐a‐Velha: comparison with Venetian and façon‐de‐Venise production

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Conservação e Restauro,Área de especialização Cerâmica e Vidro

Chemical reaction network of flavylium ions in heterogeneous media

Dissertação apresentada para a obtenção do Grau de Doutor em Química, especialidade em Química-Física, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Real fábrica de vidros de Coina – Chemical analysis of archaeological glass fragments

Actas do 17º Congresso da Associação Internacional para a História do Vidro

Biological activity of well defined hydantoin derivatives on efflux pump systems of bacteria and cancer cells

A multi-resistência a antibióticos e medicamentos usados em quimioterapia é um dos grandes problemas com os quais as instituições de saúde se debatem hoje em dia. A acção provocada por bombas de efluxo é uma das suas causas. Estas bombas têm uma importância fundamental, uma vez que, ao expelirem todo o tipo de tóxicos para o exterior das células, também expelem medicamentos, fazendo com que estes não tenham o efeito desejado dentro delas. As bombas de efluxo são transportadores que se encontram nas membranas de todo o tipo de células. Existem dois grandes tipos de bombas de efluxo: as primárias e as secundárias. As primeiras conferem multi-resistência principalmente em células eucariotas, como as células do cancro em humanos, tendo como função a mediação da repulsa de substâncias tóxicas por intermédio da hidrólise de ATP. A primeira a ser descoberta e mais estudada destas bombas foi a ABCB1 que é o gene que codifica a glicoproteína-P (P de permeabilidade). Enquanto as secundárias, que são a maior fonte de multi-resistência em bactérias, promovem a extrusão de substâncias tóxicas através da força motriz de protões. Neste tipo de bombas são conhecidas quatro famílias principais, das quais uma das mais importantes é a superfamília RND, uma vez que inclui a bomba AcrAB-TolC, que é muito importante no metabolismo xenobiótico de bactérias Gramnegativas, nomeadamente a E.coli. Com o objectivo de reverter a multi-resistência, tanto em células eucariotas como procariotas, têm-se desenvolvido estratégias de combate que envolvem a descoberta de substâncias que inibam as bombas de efluxo. Assim sendo, ao longo dos tempos têm sido descobertas variadas substâncias que cumprem este objectivo. É o caso, por exemplo, dos derivados de fluoroquinolonas usados como inibidores de bombas de efluxo em bactérias ou do Tamoxifen, utilizado na terapia de pacientes com cancro da mama. Um dos grupos de substâncias estudados para o desenvolvimento de possíveis compostos que actuem como reversores de multi-resistência são os compostos derivados de hidantoínas. Estes, são conhecidos por possuírem uma grande variedade de propriedades bioquímicas e farmacológicas, sendo portanto usados para tratarem algumas doenças em humanos, como a epilepsia. Nestes, estão englobados compostos com actividade anti-convulsão que constitui a sua grande mais-valia e, dependente da substituição no anel que os constitui, uma grande variedade de outras propriedades farmacológicas como a anti-fungica, a anti-arritmica, a anti-viral, a anti-diabética ou por exemplo a antagonização de determinados receptores, como os da serotonina. Apesar de pouco usados em estudos experimentais para desenvolver substâncias anti-carcinogénicas, existem alguns estudos com este efeito. Objectivos: O presente projecto envolve o estudo de bombas de efluxo primárias e secundárias, em células eucariotas e procariotas, respectivamente. Em bactérias, foram usados quatro modelos experimentais: Staphylococcus aureus ATCC 25923, Enterococcus faecalis ATCC 29212, E. coli AG 100 e Salmonella Enteritidis NCTC 13349. Em células de cancro foram usadas, células T de linfoma de rato parentais e células T de linfoma de rato transfectadas com o gene humano MDR-1. O principal objectivo deste estudo foi a pesquisa de novos moduladores de bombas de efluxo presentes em bactérias e células do cancro, tentando assim contribuir para o desenvolvimento de novos agentes farmacológicos que consigam reverter a multi-resistência a medicamentos. Assim sendo foram testados trinta compostos derivados de hidantoínas: SZ-2, SZ-7, LL-9, BS-1, JH-63, MN-3, TD-7k, GG-5k, P3, P7, P10, P11, RW-15b, AD-26, RW-13, AD-29, KF-2, PDPH-3, Mor-1, KK-XV, Thioam-1, JHF-1, JHC-2, JHP-1, Fur-2, GL-1, GL-7, GL-14, GL-16, GL-18. Como forma de atingir estes objectivos, a actividade biológica dos trinta compostos derivados de hidantoínas foi avaliada nas quatro estirpes de bactérias da seguinte forma: foram determinadas as concentrações mínimas inibitórias dos trinta compostos como forma de definir as concentrações em que os compostos seriam utilizados. Os compostos foram posteriormente testadas com um método fluorométrico de acumulação de brometo de etídeo, que é um substrato comum em bombas de efluxo bacterianas, desenvolvido por Viveiros et al. A actividade biológica dos compostos derivados de hidantoínas nas células de cancro foi demonstrada por diferentes métodos. O efeito anti-proliferativo e citotóxico dos trinta compostos foi avaliado nas células T de linfoma de rato transfectadas com o gene humano MDR-1 pelo método de thiazolyl de tetrazólio (MTT). Como o brometo de etídeo também é expelido pelos transportadores ABC, estes compostos foram posteriormente testados com um método fluorométrico de acumulação de brometo de etídeo desenvolvido por Spengler et al nos dois diferentes tipos de células eucariotas. Resultados: A maioria dos compostos derivados de hidantoínas foi eficaz na modulação de bombas de efluxo, nas duas estirpes de bactérias Gram-negativas e nos dois diferentes tipos de células T de linfoma. Em contraste com estes resultados, nas duas estirpes de células Gram-positivas, a maioria dos compostos tiveram pouco efeito na inibição de bombas de efluxo ou até nenhum, em muitos dos casos. De uma maneira geral os melhores compostos nas diferentes estirpes de bactérias foram: Thioam-1, SZ-2, P3, Rw-15b, AD-26, AD-29, GL-18, GL-7, KF-2, SZ-7, MN-3, GL-16 e GL- 14. Foram portanto estes os compostos que provocaram maior acumulação de brometo de etídeo, inibindo assim com maior eficácia as bombas de efluxo. No presente estudo, a maioria dos compostos conseguiu inibir a resistência provocada pela bomba de efluxo ABCB1, tanto nas células parentais bem como nas células que sobre-expressam esta bomba, causando a acumulação de brometo de etídeo dentro das células. As células que sobreexpressam a bomba ABCB1 foram posteriormente testadas com citometria de fluxo que é a técnica padrão para pesquisa de inibidores de bombas de efluxo. Os compostos que foram mais efectivos na inibição da bomba ABCB1, causando assim maior acumulação de brometo de etídeo nas células que sobre-expressam esta bomba foram: PDPH-3, GL-7, KK-XV, AD-29, Thioam-1, SZ-7, KF-2, MN-3, RW-13, LL-9, P3, AD-26, JH-63 e RW- 15b. Este facto não corroborou totalmente os resultados da citometria de fluxo uma vez que os moduladores que provocaram maior inibição da bomba ABCB1 foram o MN-3, JH-63 e o BS-1, sendo que o último não foi seleccionado como um bom composto usando o método fluorométrico de acumulação de brometo de etídeo. Conclusão: Os compostos derivados de hidantoínas testados tiveram maior efeito nas estirpes de bactérias Gram-negativas do que nas Gram-positivas. Relativamente às células eucariotas, as estruturas mais activas apresentam substituintes aromáticos bem como alguns fragmentos aminicos terciários.

Expression of antibodies and retroviralVectors from defined chromosomal sites: strategies towards reliable production systems

Dissertation presented to obtain a Ph.D. degree in Sciences of Engineering and Technology, Cell Technology, at the Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa

Chlorella sp. coagulation‐flocculation by inducing a modification on the pH broth medium

Dissertação apresentada à Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Bioenergia

The role of halouracils in radiotherapy studied by electron transfer in atom-molecule collisions experiments

Dissertação para obtenção do Grau de Doutor em Engenharia Física

Gd(III) chelates as NMR probes of protein-protein interactions. Case study: rubredoxin and cytochrome c3

Inorganic Chemistry 50(21):10600-7

Chemical approaches to ubiquitous computing

Dissertação apresentada para obtenção do Grau de Doutor em Química, perfil de Química Física, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Volume hospitalar e outcomes : uma análise a dados administrativos portugueses

RESUMO - Objetivos: Estudos anteriores demonstraram a existência de uma relação inversa entre melhores outcomes e volume hospitalar. Tendo este fenómeno em consideração, o objetivo deste estudo foi analisar esta relação a um nível populacional em Portugal. Métodos: Análise da base de dados Portuguesa dos GDH relativos ao ano financeiro de 2009. Todas as STA e STJ foram contabilizadas e critérios de exclusão determinados, para selecionar duas amostras homogéneas, analisadas por volume hospitalar. O volume hospitalar foi definido em três grupos: HVB, HVM, HVE de acordo com o volume anual de procedimentos efetuado por cada hospital individual durante o período do estudo. Os outcomes avaliados incluíram a mortalidade, presença de pelo menos uma complicação cirúrgica, estadia prolongada assim como a média de dias de internamento. A idade e sexo foram controlados como possíveis fatores de confundimento. Resultados: A investigação principal consistiu na análise de 4615 STA e 5904 STJ. Os resultados indicam que os eventos adversos (mortalidade e complicações cirúrgicas) são pouco comuns nestes procedimentos, produzindo resultados sem significância estatística. A estadia prolongada estabeleceu uma clara e significativa relação inversa com o volume hospitalar. A estadia prolongada foi superior em hospitais de volume baixo (STA OR 2.71; STJ OR 2.17) e hospitais de volume médio (STA OR 1.72; STJ OR 1.73) quando comparados com hospitais de volume elevado. Os dias de internamento médios produziram uma associação semelhante, sendo possível estimar quase oito milhões e meio de euros de custos evitáveis no ano de 2009, em ambos os procedimentos. Conclusões: O volume hospitalar não apresentou relações significativas com a mortalidade e complicações cirúrgicas, devido ao baixo número de eventos adversos registados. Verificou-se, no entanto, uma associação significativa com os dias de internamento e estadia prolongada. As STA e STJ realizadas em hospitais de volume elevado poderão reduzir custos, ao diminuir os dias de internamento.