As information is becoming more available online and will forevermore be part of any business, the true value of the large amounts of stored data is in the discovery of hidden and unknown relations and connections or traits in the data. The acquisition of these hidden relations can influence strategic decisions which have an impact on the success of a business. Data clustering is one of many methods to partition data into different groups in such a way that data patterns within the same group share some common trait compared to patterns across different groups. This thesis proposes a new artificial immune model for the problem of data clustering. The new model is inspired by the network theory of immunology and differs from its network based predecessor models in its formation of artificial lymphocyte networks. The proposed model is first applied to data clustering problems in stationary environments. Two different techniques are then proposed which enhances the proposed artificial immune model to dynamically determine the number of clusters in a data set with minimal to no user interference. A technique to generate synthetic data sets for data clustering of non-stationary environments is then proposed. Lastly, the original proposed artificial immune model and the enhanced version to dynamically determine the number of clusters are then applied to generated synthetic non-stationary data clustering problems. The influence of the parameters on the clustering performance is investigated for all versions of the proposed artificial immune model and supported by empirical results and statistical hypothesis tests. AFRIKAANS: Soos wat inligting meer aanlyn toeganglik raak en vir altyd meer deel vorm van enige besigheid, is die eintlike waarde van groot hoeveelhede data in die ontdekking van verskuilde en onbekende verwantskappe en konneksies of eienskappe in die data. Die verkryging van sulke verskuilde verwantskappe kan die strategiese besluitneming van ’n besigheid beinvloed, wat weer ’n impak het op die sukses van ’n besigheid. Data groepering is een van baie metodes om data op so ’n manier te groepeer dat data patrone wat deel vorm van dieselfde groep ’n gemeenskaplike eienskap deel in vergelyking met patrone wat verspreid is in ander groepe. Hierdie tesis stel ’n nuwe kunsmatige immuun model voor vir die probleem van data groepering. Die nuwe model is geinspireer deur die netwerk teorie in immunologie en verskil van vorige netwerk gebaseerde modelle deur die model se formasie van kunsmatige limfosiet netwerke. Die voorgestelde model word eers toegepas op data groeperingsprobleme in statiese omgewings. Twee verskillende tegnieke word dan voorgestel wat die voorgestelde kunsmatige immuun model op so ’n manier verbeter dat die model die aantal groepe in ’n data stel dinamies kan bepaal met minimum tot geen gebruiker invloed. ’n Tegniek om kunsmatige data stelle te genereer vir data groepering in dinamiese omgewings word dan voorgestel. Laastens word die oorspronklik voorgestelde model sowel as die verbeterde model wat dinamies die aantal groepe in ’n data stel kan bepaal toegepas op kunsmatig genereerde dinamiese data groeperingsprobleme. Die invloed van die parameters op die groepering prestasie is ondersoek vir alle weergawes van die voorgestelde kunsmatige immuun model en word toegelig deur empiriese resultate en statistiese hipotese toetse. / Thesis (PhD)--University of Pretoria, 2011. / Computer Science / unrestricted

Background: Clonal competition in cancer describes the process in which the progeny of a cell clone supersedes or succumbs to other competing clones due to differences in their functional characteristics, mostly based on subsequently acquired mutations. Even though the patterns of those mutations are well explored in many tumors, the dynamical process of clonal selection is underexposed. Methods: We studied the dynamics of clonal competition in a BcrAbl-induced leukemia using a γ-retroviral vector library encoding the oncogene in conjunction with genetic barcodes. To this end, we studied the growth dynamics of transduced cells on the clonal level both in vitro and in vivo in transplanted mice. Results: While we detected moderate changes in clonal abundancies in vitro, we observed monoclonal leukemias in 6/30 mice after transplantation, which intriguingly were caused by only two different BcrAbl clones. To analyze the success of these clones, we applied a mathematical model of hematopoietic tissue maintenance, which indicated that a differential engraftment capacity of these two dominant clones provides a possible explanation of our observations. These findings were further supported by additional transplantation experiments and increased BcrAbl transcript levels in both clones. Conclusion: Our findings show that clonal competition is not an absolute process based on mutations, but highly dependent on selection mechanisms in a given environmental context.

Background: Clonal competition in cancer describes the process in which the progeny of a cell clone supersedes or succumbs to other competing clones due to differences in their functional characteristics, mostly based on subsequently acquired mutations. Even though the patterns of those mutations are well explored in many tumors, the dynamical process of clonal selection is underexposed. Methods: We studied the dynamics of clonal competition in a BcrAbl-induced leukemia using a γ-retroviral vector library encoding the oncogene in conjunction with genetic barcodes. To this end, we studied the growth dynamics of transduced cells on the clonal level both in vitro and in vivo in transplanted mice. Results: While we detected moderate changes in clonal abundancies in vitro, we observed monoclonal leukemias in 6/30 mice after transplantation, which intriguingly were caused by only two different BcrAbl clones. To analyze the success of these clones, we applied a mathematical model of hematopoietic tissue maintenance, which indicated that a differential engraftment capacity of these two dominant clones provides a possible explanation of our observations. These findings were further supported by additional transplantation experiments and increased BcrAbl transcript levels in both clones. Conclusion: Our findings show that clonal competition is not an absolute process based on mutations, but highly dependent on selection mechanisms in a given environmental context.

Die Selektion klonal expandierender Zellen mit einzigartigen, somatisch rekombinierten Anti-gen-Rezeptoren und die Langlebigkeit der daraus hervorgehenden Gedächtniszellen sind definierende Eigenschaften adaptiver Immunität. Dahingegen ist das angeborene Immunsystem da-rauf programmiert, mittels einer breiten Palette konservierter Rezeptoren möglichst schnell auf Pathogene zu reagieren und wird dabei auf Populationsebene epigenetisch geprägt. In dieser Arbeit möchte ich dieses Paradigma auf der Basis von Natürlichem Killer (NK) Zell-Gedächtnis an das humane Zytomegalievirus (HCMV) als Beispiel für Pathogen-spezifische Anpassung innerhalb des angeborenen Immunsystems herausfordern. Indem wir multiparametrische Einzel-zellanalysen zur Kartierung von ex vivo NK Zellen mit endogenen Barcodes in Form von soma-tischen Mutationen in mitochondrialer DNA (mtDNA) verknüpfen, können wir drastische klonale Expansionen adaptiver NK Zellen in HCMV+ Spendern nachweisen. NK-Zell-Klonotypen waren durch eine ihnen gemeinsame, inflammatorische Gedächtnissignatur mit AP1 Motiven gekennzeichnet, die eine Reihe einzigartiger Chromatin-Regionen mit Klon-spezifischer Zugänglichkeit überlagerte. NK-Zell-Klone wurden über einen Zeitraum von bis zu 19 Monaten stabil aufrechterhalten und behielten dabei ihre charakteristischen, Klon-spezifischen epigenetischen Signaturen, was die entscheidende Rolle klonaler Vererbung von Chromatin-Zugänglichkeit für die Prägung des epigenetischen Gedächtnis-Repertoires unterstreicht. Insgesamt identifiziert diese Arbeit zum ersten Mal klonale Expansion und Persistenz innerhalb des angeborenen Immunsystems im Menschen und deutet daraufhin, dass diese zentralen Mechanismen zur Ausbildung von immunologischem Gedächtnis evolutionär unabhängig von diversifizierten Antigen-Rezeptoren entstanden sind. / A hallmark of adaptive immunity is the clonal selection and expansion of cells with somatically diversified receptors and their long-term maintenance as memory cells. The innate immune system, in contrast, is wired to rapidly respond to pathogens via a broad set of germline-encoded receptors, acquiring epigenetic imprinting at the population level. The presented work challenges this paradigm by studying Natural Killer (NK) cell memory to human Cytomegalovirus (HCMV) infection as an example of pathogen-specific adaptation within the innate immune system. Leveraging single-cell multi-omic maps of ex vivo NK cells and somatic mitochondrial DNA (mtDNA) mutations as endogenous barcodes, we reveal drastic clonal expansions of adaptive NK cells in HCMV+ individuals. NK cell clonotypes were characterized by a convergent inflammatory memory signature driven by AP1 transcription factor activity, superimposed on a private set of clone-specific accessible chromatin regions. Strikingly, NK cell clones were stably maintained in their specific epigenetic states for up to 19 months, revealing that clonal inheritance of chromatin accessibility shapes the epigenetic memory repertoire. Together, this work presents the first identification of clonal expansion and persistence within the human innate immune system, suggesting these central mechanisms of immune memory have evolved independently of antigen-receptor diversification.

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