5 resultados para Sapiens
em AMS Tesi di Dottorato - Alm@DL - Università di Bologna
Resumo:
La ricerca di Roberta Frigeni, svolta ad ampio spettro diacronico, è condotta su di una campionatura di specula principum - editi ed inediti - elaborati tra XII e XV secolo, e ne indaga il linguaggio quale referente privilegiato, rilevandone persistenze terminologiche e nuclei sintagmatici ricorrenti, al fine di individuare concetti utili a delineare un lessico politico proprio di questa testualità, in corrispondenza al sorgere dell’entità statale europea nel XIII secolo (con particolare riguardo all’area francese, ai regni di Luigi IX e Filippo il Bello). A partire da un’analisi critica delle tesi di Quentin Skinner circa la ‘ridefinizione paradiastolica’ del sistema delle virtù classiche entro il trattato De principatibus, lo studio innesca un percorso di indagine à rebours che - sondando il linguaggio - rintraccia nella trattatistica delle institutiones regum del XV secolo (Pontano, Patrizi, Carafa, Platina) e degli specula principum medievali (Elinando di Froidmont, Gilberto di Tournai, Vincenzo di Beauvais, Guglielmo Peraldo, Egidio Romano, Guido Vernani) una consonanza di motivi nella sintassi e nell’immaginario preposti ad illustrare le potenzialità semantiche del nome di prudentia, individuata quale unica virtù sopravvissuta alla ‘ridescrizione’ del codice etico operata da Machiavelli. Indagando i progressivi ampliamenti del campo semantico sorto attorno al nome della virtù di prudenza entro la letteratura speculare, la ricerca mostra come il dialettico rapporto con i lessemi di sapientia, astutia, fides ed experientia abbia avuto un ruolo determinante per il sorgere di un’immagine del principe emancipata dalla figura biblica del “rex sapiens”, e per la formazione di un lessico ospitale delle manifestazioni concrete del vivere politico ed economico. I processi di dilatazione e rarefazione del bacino semantico di prudentia sono, infatti, funzionali ad illustrare come il linguaggio della testualità speculare registri l’acquisizione di nuove strumentazioni teoriche grazie al rinnovamento delle fonti a disposizione lungo il secolo XIII, che - sostituendo progressivamente il più recente dossier aristotelico al solo apparato veterotestamentario - permettono di integrare la concezione delle virtù in senso operativo, adattandola alle esigenze politico-economiche dei nuovi contesti istituzionali monarchici.
Resumo:
The continuous increase of genome sequencing projects produced a huge amount of data in the last 10 years: currently more than 600 prokaryotic and 80 eukaryotic genomes are fully sequenced and publically available. However the sole sequencing process of a genome is able to determine just raw nucleotide sequences. This is only the first step of the genome annotation process that will deal with the issue of assigning biological information to each sequence. The annotation process is done at each different level of the biological information processing mechanism, from DNA to protein, and cannot be accomplished only by in vitro analysis procedures resulting extremely expensive and time consuming when applied at a this large scale level. Thus, in silico methods need to be used to accomplish the task. The aim of this work was the implementation of predictive computational methods to allow a fast, reliable, and automated annotation of genomes and proteins starting from aminoacidic sequences. The first part of the work was focused on the implementation of a new machine learning based method for the prediction of the subcellular localization of soluble eukaryotic proteins. The method is called BaCelLo, and was developed in 2006. The main peculiarity of the method is to be independent from biases present in the training dataset, which causes the over‐prediction of the most represented examples in all the other available predictors developed so far. This important result was achieved by a modification, made by myself, to the standard Support Vector Machine (SVM) algorithm with the creation of the so called Balanced SVM. BaCelLo is able to predict the most important subcellular localizations in eukaryotic cells and three, kingdom‐specific, predictors were implemented. In two extensive comparisons, carried out in 2006 and 2008, BaCelLo reported to outperform all the currently available state‐of‐the‐art methods for this prediction task. BaCelLo was subsequently used to completely annotate 5 eukaryotic genomes, by integrating it in a pipeline of predictors developed at the Bologna Biocomputing group by Dr. Pier Luigi Martelli and Dr. Piero Fariselli. An online database, called eSLDB, was developed by integrating, for each aminoacidic sequence extracted from the genome, the predicted subcellular localization merged with experimental and similarity‐based annotations. In the second part of the work a new, machine learning based, method was implemented for the prediction of GPI‐anchored proteins. Basically the method is able to efficiently predict from the raw aminoacidic sequence both the presence of the GPI‐anchor (by means of an SVM), and the position in the sequence of the post‐translational modification event, the so called ω‐site (by means of an Hidden Markov Model (HMM)). The method is called GPIPE and reported to greatly enhance the prediction performances of GPI‐anchored proteins over all the previously developed methods. GPIPE was able to predict up to 88% of the experimentally annotated GPI‐anchored proteins by maintaining a rate of false positive prediction as low as 0.1%. GPIPE was used to completely annotate 81 eukaryotic genomes, and more than 15000 putative GPI‐anchored proteins were predicted, 561 of which are found in H. sapiens. In average 1% of a proteome is predicted as GPI‐anchored. A statistical analysis was performed onto the composition of the regions surrounding the ω‐site that allowed the definition of specific aminoacidic abundances in the different considered regions. Furthermore the hypothesis that compositional biases are present among the four major eukaryotic kingdoms, proposed in literature, was tested and rejected. All the developed predictors and databases are freely available at: BaCelLo http://gpcr.biocomp.unibo.it/bacello eSLDB http://gpcr.biocomp.unibo.it/esldb GPIPE http://gpcr.biocomp.unibo.it/gpipe
Resumo:
The vast majority of known proteins have not yet been experimentally characterized and little is known about their function. The design and implementation of computational tools can provide insight into the function of proteins based on their sequence, their structure, their evolutionary history and their association with other proteins. Knowledge of the three-dimensional (3D) structure of a protein can lead to a deep understanding of its mode of action and interaction, but currently the structures of <1% of sequences have been experimentally solved. For this reason, it became urgent to develop new methods that are able to computationally extract relevant information from protein sequence and structure. The starting point of my work has been the study of the properties of contacts between protein residues, since they constrain protein folding and characterize different protein structures. Prediction of residue contacts in proteins is an interesting problem whose solution may be useful in protein folding recognition and de novo design. The prediction of these contacts requires the study of the protein inter-residue distances related to the specific type of amino acid pair that are encoded in the so-called contact map. An interesting new way of analyzing those structures came out when network studies were introduced, with pivotal papers demonstrating that protein contact networks also exhibit small-world behavior. In order to highlight constraints for the prediction of protein contact maps and for applications in the field of protein structure prediction and/or reconstruction from experimentally determined contact maps, I studied to which extent the characteristic path length and clustering coefficient of the protein contacts network are values that reveal characteristic features of protein contact maps. Provided that residue contacts are known for a protein sequence, the major features of its 3D structure could be deduced by combining this knowledge with correctly predicted motifs of secondary structure. In the second part of my work I focused on a particular protein structural motif, the coiled-coil, known to mediate a variety of fundamental biological interactions. Coiled-coils are found in a variety of structural forms and in a wide range of proteins including, for example, small units such as leucine zippers that drive the dimerization of many transcription factors or more complex structures such as the family of viral proteins responsible for virus-host membrane fusion. The coiled-coil structural motif is estimated to account for 5-10% of the protein sequences in the various genomes. Given their biological importance, in my work I introduced a Hidden Markov Model (HMM) that exploits the evolutionary information derived from multiple sequence alignments, to predict coiled-coil regions and to discriminate coiled-coil sequences. The results indicate that the new HMM outperforms all the existing programs and can be adopted for the coiled-coil prediction and for large-scale genome annotation. Genome annotation is a key issue in modern computational biology, being the starting point towards the understanding of the complex processes involved in biological networks. The rapid growth in the number of protein sequences and structures available poses new fundamental problems that still deserve an interpretation. Nevertheless, these data are at the basis of the design of new strategies for tackling problems such as the prediction of protein structure and function. Experimental determination of the functions of all these proteins would be a hugely time-consuming and costly task and, in most instances, has not been carried out. As an example, currently, approximately only 20% of annotated proteins in the Homo sapiens genome have been experimentally characterized. A commonly adopted procedure for annotating protein sequences relies on the "inheritance through homology" based on the notion that similar sequences share similar functions and structures. This procedure consists in the assignment of sequences to a specific group of functionally related sequences which had been grouped through clustering techniques. The clustering procedure is based on suitable similarity rules, since predicting protein structure and function from sequence largely depends on the value of sequence identity. However, additional levels of complexity are due to multi-domain proteins, to proteins that share common domains but that do not necessarily share the same function, to the finding that different combinations of shared domains can lead to different biological roles. In the last part of this study I developed and validate a system that contributes to sequence annotation by taking advantage of a validated transfer through inheritance procedure of the molecular functions and of the structural templates. After a cross-genome comparison with the BLAST program, clusters were built on the basis of two stringent constraints on sequence identity and coverage of the alignment. The adopted measure explicity answers to the problem of multi-domain proteins annotation and allows a fine grain division of the whole set of proteomes used, that ensures cluster homogeneity in terms of sequence length. A high level of coverage of structure templates on the length of protein sequences within clusters ensures that multi-domain proteins when present can be templates for sequences of similar length. This annotation procedure includes the possibility of reliably transferring statistically validated functions and structures to sequences considering information available in the present data bases of molecular functions and structures.
Resumo:
The Notch signalling is a cellular pathway that results conserved from Drosophila to Homo sapiens controlling a wide range of cellular processes in development and in differentiated organs. It induces cell proliferation or differentiation, increased survival or apoptosis, and it is involved in stemness maintainance. These functions are conserved, but exerted with a high tissue and cellular context specificity. Signalling activation determs nuclear translocation of the receptor’s cytoplasmic domain and activation of target genes transcription. As many developmental pathway, Notch deregulation is involved in cancer, leading to oncogenic or tumour suppressive role depending on the functions exerted in normal tissue. Notch1 and Notch3 resulted aberrantly expressed in human hepatocellular carcinoma (HCC) that is the more frequent tumour of the liver and the sixth most common tumour worldwide. This thesis has the aim to investigate the role of the signalling in HCC, with particular attention to dissect common and uncommon regulatory pathways between Notch1 and Notch3 and to define the role of the signalling in HCC. Nocth1 and Notch3 were analysed on their regulation on Hes1 target and involvement in cell cycle control. They showed to regulate CDKN1C/p57kip2 expression through Hes1 target. CDKN1C/p57kip2 induces not only cell cycle arrest, but also senescence in HCC cell lines. Moreover, the involvement of Notch1 in cancer progression and epithelial to mesenchymal transition was investigated. Notch1 showed to induce invasion of HCC, regulating EMT and E- Cadherin expression. Moreover, Notch3 showed specific regulation on p53 at post translational levels. In vitro and ex vivo analysis on HCC samples suggests a complex role of both receptors in regulate HCC, with an oncogenic role but also showing tumour suppressive effects, suggesting a complex and deep involvement of this signalling in HCC.
Resumo:
The interaction between disciplines in the study of human population history is of primary importance, profiting from the biological and cultural characteristics of humankind. In fact, data from genetics, linguistics, archaeology and cultural anthropology can be combined to allow for a broader research perspective. This multidisciplinary approach is here applied to the study of the prehistory of sub-Saharan African populations: in this continent, where Homo sapiens originally started his evolution and diversification, the understanding of the patterns of human variation has a crucial relevance. For this dissertation, molecular data is interpreted and complemented with a major contribution from linguistics: linguistic data are compared to the genetic data and the research questions are contextualized within a linguistic perspective. In the four articles proposed, we analyze Y chromosome SNPs and STRs profiles and full mtDNA genomes on a representative number of samples to investigate key questions of African human variability. Some of these questions address i) the amount of genetic variation on a continental scale and the effects of the widespread migration of Bantu speakers, ii) the extent of ancient population structure, which has been lost in present day populations, iii) the colonization of the southern edge of the continent together with the degree of population contact/replacement, and iv) the prehistory of the diverse Khoisan ethnolinguistic groups, who were traditionally understudied despite representing one of the most ancient divergences of modern human phylogeny. Our results uncover a deep level of genetic structure within the continent and a multilayered pattern of contact between populations. These case studies represent a valuable contribution to the debate on our prehistory and open up further research threads.