The alternative complex III from Rhodothermus marinus - a prototype of a new family of quinol: electron acceptor oxidoreductase

Dissertation presented to obtain a PhD degree in Biochemistry at the Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa

Energy transduction by respiratory complex I

Dissertation presented to obtain a PhD degree in Biochemistry at the Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa

The role of Di-iron proteins in pathogen resistance

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

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

Inorganic Chemistry 50(21):10600-7

The first crystal structure of class III superoxide reductase from Treponema pallidum

J Biol Inorg Chem (2006) 11: 548–558 DOI 10.1007/s00775-006-0104-y

Structural and functional characterization of the gene products responsible for phototrophic iron oxidation by purple bacteria

Dissertação para a obtenção de grau de doutor em Bioquímica pelo Instituto de Tecnologia Química e Biológica. Universidade Nova de Lisboa

Human ceruloplasmin and neurotransmitters: complex stabilization and crystallization

Dissertation to obtain a Master Degree in Molecular Genetics and Biomedicine at Faculty of Sciences and Technology,Universidade Nova de Lisboa

Complex networks and data mining: toward a new perspective for the understanding of complex systems

Complex systems, i.e. systems composed of a large set of elements interacting in a non-linear way, are constantly found all around us. In the last decades, different approaches have been proposed toward their understanding, one of the most interesting being the Complex Network perspective. This legacy of the 18th century mathematical concepts proposed by Leonhard Euler is still current, and more and more relevant in real-world problems. In recent years, it has been demonstrated that network-based representations can yield relevant knowledge about complex systems. In spite of that, several problems have been detected, mainly related to the degree of subjectivity involved in the creation and evaluation of such network structures. In this Thesis, we propose addressing these problems by means of different data mining techniques, thus obtaining a novel hybrid approximation intermingling complex networks and data mining. Results indicate that such techniques can be effectively used to i) enable the creation of novel network representations, ii) reduce the dimensionality of analyzed systems by pre-selecting the most important elements, iii) describe complex networks, and iv) assist in the analysis of different network topologies. The soundness of such approach is validated through different validation cases drawn from actual biomedical problems, e.g. the diagnosis of cancer from tissue analysis, or the study of the dynamics of the brain under different neurological disorders.