Cell functional enviromics: applications to Pichia pastoris

The present PhD thesis develops the cell functional enviromics (CFE) method to investigate the relationship between environment and cellular physiology. CFE may be defined as the envirome-wide cellular function reconstruction through the collection and systems-level analysis of dynamic envirome data. Throughout the thesis, CFE is illustrated by two main applications to cultures of a constitutive P. pastoris X33 strain expressing a scFv antibody fragment. The first application addresses the challenge of culture media development. A dataset was built from 26 shake flask experiments, with variations in trace elements concentrations and basal medium dilution based on the standard BSM+PTM1. Protein yield showed high sensitivity to culture medium variations, while biomass was essentially determined by BSM dilution. High scFv yield was associated with high overall metabolic fluxes through central carbon pathways concomitantly with a relative shift of carbon flux from biosynthetic towards energy-generating pathways. CFE identified three cellular functions (growth, energy generation and by-product formation) that together described 98.8% of the variance in observed fluxes. Analyses of how medium factors relate to identified cellular functions showed iron and manganese at concentrations close to PTM1 inhibit overall metabolic activity. The second application addresses bioreactor operation. Pilot 50 L fed-batch cultivations, followed by 1H-NMR exometabolite profiling, allowed the acquisition of data for 21 environmental factors over time. CFE identified five major metabolic pathway groups that are frequently activated by the environment. The resulting functional enviromics map may serve as template for future optimization of media composition and feeding strategies for Pichia pastoris. The present PhD thesis is a step forward towards establishing the foundations of CFE that is still at its infancy. The methods developed herein are a contribution for changing the culture media and process development paradigm towards a holistic and systematic discipline in the future.

Gold nanoparticle to antibody conjugates for diagnosis applications: molecular interactions and immunoassay development

Thesis for the Master degree in Structural and Functional Biochemistry

Novel approaches for culturing hepatocytes for drug testing applications

Dissertation presented to obtain the Ph.D degree in Biochemisry, Biotechnology

Quantum dots: synthesis, functionalization and bioconjugation for biological applications

Dissertation presented to obtain the Ph.D degree in Engineering Sciences and Technology

Printed electronics for ubiquitous computing applications

Dissertação para obtenção do Grau de Doutor em Química

Web Processing Services for Forestry and Environmental Applications

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Genetic and functional analysis of the bacillus subtilis BSP1 gam cluster

Mannans (linear mannan, glucomannan, galactomannan and galactoglucomannan) are the major constituents of the hemicellulose fraction in softwoods and show great importance as a renewable resource for fuel or feedstock applications. As complex polysaccharides, mannans can only be degraded through a synergistic action of different mannan-degrading enzymes, mannanases. Microbial mannanases are mainly extracellular enzymes that can act in wide range of pH and temperature, contributing to pulp and paper, pharmaceutical, food and feed, oil and textile successful industrial applications. Knowing and controlling these microbial mannan-degrading enzymes are essential to take advantage of their great biotechnological potential. The genome of the laboratory 168 strain of Bacillus subtilis carries genes gmuA-G dedicated to the degradation and utilization of glucomannan, including an extracellular -mannanase. Recently, the genome sequence of an undomesticated strain of B. subtilis, BSP1, was determined. In BSP1, the gmuA-G operon is maintained, interestingly, however, a second cluster of genes was found (gam cluster), which comprise a second putative extracellular β-mannanase, and most likely specify a system for the degradation and utilization of a different mannan polymer, galactoglucomannan. The genetic organization and function of the gam cluster, and whether its presence in BSP1 strain results in new hemicellulolytic capabilities, compared to those of the laboratory strain, was address in this work. In silico and in vivo mRNA analyses performed in this study revealed that the gam cluster, comprising nine genes, is organized and expressed in at least six different transcriptional units. Furthermore, cloning, expression, and production of Bbsp2923 in Escherichia coli was achieved and preliminary characterization shows that the enzyme is indeed a β-mannanase. Finally, the high hemicellulolytic capacity of the undomesticated B. subtilis BSP1, demonstrated in this work by qualitative analyses, suggests potential to be used in the food and feed industries.

Minimal computation structures for visual information applications based on printed electronics

In the early nineties, Mark Weiser wrote a series of seminal papers that introduced the concept of Ubiquitous Computing. According to Weiser, computers require too much attention from the user, drawing his focus from the tasks at hand. Instead of being the centre of attention, computers should be so natural that they would vanish into the human environment. Computers become not only truly pervasive but also effectively invisible and unobtrusive to the user. This requires not only for smaller, cheaper and low power consumption computers, but also for equally convenient display solutions that can be harmoniously integrated into our surroundings. With the advent of Printed Electronics, new ways to link the physical and the digital worlds became available. By combining common printing techniques such as inkjet printing with electro-optical functional inks, it is starting to be possible not only to mass-produce extremely thin, flexible and cost effective electronic circuits but also to introduce electronic functionalities into products where it was previously unavailable. Indeed, Printed Electronics is enabling the creation of novel sensing and display elements for interactive devices, free of form factor. At the same time, the rise in the availability and affordability of digital fabrication technologies, namely of 3D printers, to the average consumer is fostering a new industrial (digital) revolution and the democratisation of innovation. Nowadays, end-users are already able to custom design and manufacture on demand their own physical products, according to their own needs. In the future, they will be able to fabricate interactive digital devices with user-specific form and functionality from the comfort of their homes. This thesis explores how task-specific, low computation, interactive devices capable of presenting dynamic visual information can be created using Printed Electronics technologies, whilst following an approach based on the ideals behind Personal Fabrication. Focus is given on the use of printed electrochromic displays as a medium for delivering dynamic digital information. According to the architecture of the displays, several approaches are highlighted and categorised. Furthermore, a pictorial computation model based on extended cellular automata principles is used to programme dynamic simulation models into matrix-based electrochromic displays. Envisaged applications include the modelling of physical, chemical, biological, and environmental phenomena.

Improvement of a photoautotrophic chassis robustness for synthetic biology applications

Cyanobacteria are photoautotrophic microorganisms with great potential for the biotechnological industry due to their low nutrient requirements, photosynthetic capacities and metabolic plasticity. In biotechnology, the energy sector is one of the main targets for their utilization, especially to produce the so called third generation biofuels, which are regarded as one of the best replacements for petroleum-based fuels. Although, several issues could be solved, others arise from the use of cyanobacteria, namely the need for high amounts of freshwater and contamination/predation by other microorganisms that affect cultivation efficiencies. The cultivation of cyanobacteria in seawater could solve this issue, since it has a very stable and rich chemical composition. Among cyanobacteria, the model microorganism Synechocystis sp. PCC 6803 is one of the most studied with its genome fully sequenced and genomic, transcriptomic and proteomic data available to better predict its phenotypic behaviors/characteristics. Despite suitable for genetic engineering and implementation as a microbial cell factory, Synechocystis’ growth rate is negatively affected by increasing salinity levels. Therefore, it is important to improve. To achieve this, several strategies involving the constitutive overexpression of the native genes encoding the proteins involved in the production of the compatible solute glucosylglycerol were implemented, following synthetic biology principles. A preliminary transcription analysis of selected mutants revealed that the assembled synthetic devices are functional at the transcriptional level. However, under different salinities, the mutants did not show improved robustness to salinity in terms of growth, compared with the wild-type. Nevertheless, some mutants carrying synthetic devices appear to have a better physiological response under seawater’s NaCl concentration than in 0% (w/v) NaCl.

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.

Development of biocompatible and “smart” porous structures using CO2-assisted processes

Dissertação apresentada para a obtenção do grau de Doutor em Engenharia Química, especialidade Engenharia da Reacção Química, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Functional role of macrobenthos in estuarine sediment dynamics

Thesis submitted to the Universidade Nova de Lisboa,Faculdade de Ciências e Tecnologia for the degree of Doctor of Philosophy in Environmental Engineering

New quasi-orthogonal BCH-derived sequences for CDMA applications

European Transactions on Telecommunications, vol. 18

A CMOS variable width short-pulse generator circuit for UWB RADAR applications

IEEE International Symposium on Circuits and Systems, pp. 2713 – 2716, Seattle, EUA

Functional domains of Bacillus subtilis transcription factor AraR and identification of aminoacids important for nucleoprotein complex assembly and effector-binding.

Journal of Bacteriology (Apr 2006) 3024-3036