The Development of Microdosimetric Instrumentation for Quality Assurance in Heavy Ion Therapy, Boron Neutron Capture Therapy and Fast Neutron Therapy

This thesis presents research for the development of new microdosimetric instrumentation for use with solid-state microdosimeters in order to improve their portability for radioprotection purposes and for QA in various hadron therapy modalities. Monte Carlo simulation applications are developed and benchmarked, pertaining to the context of the relevant therapies considered. The simulation and experimental findings provide optimisation recommendations relating to microdosimeter performance and possible radioprotection risks by activated materials. The first part of this thesis is continuing research into the development of novel Silicon-on-Insulator (SOI) microdosimeters in the application of hadron therapy QA. This relates specifically to the optimisation of current microdosimeters, development of Monte Carlo applications for experimental validation, assessment of radioprotection risks during experiments and advanced Monte Carlo modelling of various accelerator beamlines. Geant4 and MCNP6 Monte Carlo codes are used extensively in this thesis, with rigorous benchmarking completed in the context of experimental verification, and evaluation of the similarities and differences when simulating relevant hadron therapy facilities. The second part of this thesis focuses on the development of a novel wireless microdosimetry system - the Radiodosimeter, to improve the operation efficiency and minimise any radioprotection risks. The successful implementation of the wireless Radiodosimeter is considered as an important milestone in the development of a microdosimetry system that can be operated by an end-user with no prior knowledge

A parallel genetic algorithm for single class pattern classification and its application for gene expression profiling in Streptomyces coelicolor

BACKGROUND: Identification of coordinately regulated genes according to the level of their expression during the time course of a process allows for discovering functional relationships among genes involved in the process. RESULTS: We present a single class classification method for the identification of genes of similar function from a gene expression time series. It is based on a parallel genetic algorithm which is a supervised computer learning method exploiting prior knowledge of gene function to identify unknown genes of similar function from expression data. The algorithm was tested with a set of randomly generated patterns; the results were compared with seven other classification algorithms including support vector machines. The algorithm avoids several problems associated with unsupervised clustering methods, and it shows better performance then the other algorithms. The algorithm was applied to the identification of secondary metabolite gene clusters of the antibiotic-producing eubacterium Streptomyces coelicolor. The algorithm also identified pathways associated with transport of the secondary metabolites out of the cell. We used the method for the prediction of the functional role of particular ORFs based on the expression data. CONCLUSION: Through analysis of a time series of gene expression, the algorithm identifies pathways which are directly or indirectly associated with genes of interest, and which are active during the time course of the experiment

Signes cliniques, taux d'infestation journaliers, modifications hématologiques et pathomorphologiques sur du bétail infesté artificiellement par <em>Trypanosoma vivax</em>

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Nonlinear differential equation model for quantification of transcriptional regulation applied to microarray data of Saccharomyces cerevisiae

Microarray studies are capable of providing data for temporal gene expression patterns of thousands of genes simultaneously, comprising rich but cryptic information about transcriptional control. However available methods are still not adequate in extraction of useful information about transcriptional regulation from these data. This study presents a dynamic model of gene expression which allows for identification of transcriptional regulators using time series of gene expression. The algorithm was applied for identification of transcriptional regulators controlling 40 cell cycle regulated genes of Saccharomyces cerevisiae. The presented algorithm uses a dynamic model of time continuous gene expression with the assumption that the target gene expression profile results from the action of the upstream regulator. The goal is to apply the model to putative regulators to estimate the transcription pattern of a target gene using a least squares minimization procedure. The procedure iteratively tests all possible transcription factors and selects those that best approximate the target gene expression profile. Results were compared with independently published data and good agreement between the published and identified transcriptional regulators was found

Numerical modelling of microRNA-mediated mRNA decay identifies novel mechanism of microRNA controlled mRNA downregulation

Post-transcriptional control of mRNA by micro-RNAs (miRNAs) represents an important mechanism of gene regulation. miRNAs act by binding to the 3′ untranslated region (3′UTR) of an mRNA, affecting the stability and translation of the target mRNA. Here, we present a numerical model of miRNA-mediated mRNA downregulation and its application to analysis of temporal microarray data of HepG2 cells transfected with miRNA-124a. Using the model our analysis revealed a novel mechanism of mRNA accumulation control by miRNA, predicting that specific mRNAs are controlled in a digital, switch-like manner. Specifically, the contribution of miRNAs to mRNA degradation is switched from maximum to zero in a very short period of time. Such behaviour suggests a model of control in which mRNA is at a certain moment protected from binding of miRNA and further accumulates with a basal rate. Genes associated with this process were identified and parameters of the model for all miRNA-124a affected mRNAs were computed

Broad spectrum thiopeptide recognition specificity of the Streptomyces lividans TipAL protein and its role in regulating gene expression.

Microbial metabolites isolated in screening programs for their ability to activate transcription of the tipA promoter (ptipA) in Streptomyces lividans define a class of cyclic thiopeptide antibiotics having dehydroalanine side chains ("tails"). Here we show that such compounds of heterogeneous primary structure (representatives tested: thiostrepton, nosiheptide, berninamycin, promothiocin) are all recognized by TipAS and TipAL, two in-frame translation products of the tipA gene. The N-terminal helix-turn-helix DNA binding motif of TipAL is homologous to the MerR family of transcriptional activators, while the C terminus forms a novel ligand-binding domain. ptipA inducers formed irreversible complexes in vitro and in vivo (presumably covalent) with TipAS by reacting with the second of the two C-terminal cysteine residues. Promothiocin and thiostrepton derivatives in which the dehydroalanine side chains were removed lost the ability to modify TipAS. They were able to induce expression of ptipA as well as the tipA gene, although with reduced activity. Thus, TipA required the thiopeptide ring structure for recognition, while the tail served either as a dispensable part of the recognition domain and/or locked thiopeptides onto TipA proteins, thus leading to an irreversible transcriptional activation. Construction and analysis of a disruption mutant showed that tipA was autogenously regulated and conferred thiopeptide resistance. Thiostrepton induced the synthesis of other proteins, some of which did not require tipA

A connection between stress and development in the multicelular prokaryote Streptomyces coelicolor

Morphological changes leading to aerial mycelium formation and sporulation in the mycelial bacterium Streptomyces coelicolor rely on establishing distinct patterns of gene expression in separate regions of the colony. sH was identified previously as one of three paralogous sigma factors associated with stress responses in S. coelicolor. Here, we show that sigH and the upstream gene prsH (encoding a putative antisigma factor of sH) form an operon transcribed from two developmentally regulated promoters, sigHp1 and sigHp2. While sigHp1 activity is confined to the early phase of growth, transcription of sigHp2 is dramatically induced at the time of aerial hyphae formation. Localization of sigHp2 activity using a transcriptional fusion to the green fluorescent protein reporter gene (sigHp2–egfp) showed that sigHp2 transcription is spatially restricted to sporulating aerial hyphae in wild-type S. coelicolor. However, analysis of mutants unable to form aerial hyphae (bld mutants) showed that sigHp2 transcription and sH protein levels are dramatically upregulated in a bldD mutant, and that the sigHp2–egfp fusion was expressed ectopically in the substrate mycelium in the bldD background. Finally, a protein possessing sigHp2 promoter-binding activity was purified to homogeneity from crude mycelial extracts of S. coelicolor and shown to be BldD. The BldD binding site in the sigHp2 promoter was defined by DNase I footprinting. These data show that expression of sH is subject to temporal and spatial regulation during colony development, that this tissue-specific regulation is mediated directly by the developmental transcription factor BldD and suggest that stress and developmental programmes may be intimately connected in Streptomyces morphogenesis

Virtual Mutagenesis of the Yeast Cyclins Genetic Network Reveals Complex Dynamics of Transcriptional Control Networks

Study of genetic networks has moved from qualitative description of interactions between regulators and regulated genes to the analysis of the interaction dynamics. This paper focuses on the analysis of dynamics of one particular network – the yeast cyclins network. Using a dedicated mathematical model of gene expression and a procedure for computation of the parameters of the model from experimental data, a complete numerical model of the dynamics of the cyclins genetic network was attained. The model allowed for performing virtual experiments on the network and observing their influence on the expression dynamics of the genes downstream in the regulatory cascade. Results show that when the network structure is more complicated, and the regulatory interactions are indirect, results of gene deletion are highly unpredictable. As a consequence of quantitative behavior of the genes and their connections within the network, causal relationship between a regulator and target gene may not be discovered by gene deletion. Without including the dynamics of the system into the network, its functional properties cannot be studied and interpreted correctly

The suboptimal structures find the optimal RNAs: homology search for bacterial non-coding RNAs using suboptimal RNA structures

Non-coding RNAs (ncRNAs) are regulatory molecules encoded in the intergenic or intragenic regions of the genome. In prokaryotes, biocomputational identification of homologs of known ncRNAs in other species often fails due to weakly evolutionarily conserved sequences, structures, synteny and genome localization, except in the case of evolutionarily closely related species. To eliminate results from weak conservation, we focused on RNA structure, which is the most conserved ncRNA property. Analysis of the structure of one of the few well-studied bacterial ncRNAs, 6S RNA, demonstrated that unlike optimal and consensus structures, suboptimal structures are capable of capturing RNA homology even in divergent bacterial species. A computational procedure for the identification of homologous ncRNAs using suboptimal structures was created. The suggested procedure was applied to strongly divergent bacterial species and was capable of identifying homologous ncRNAs

Supervised inference of gene-regulatory networks

<p>Abstract</p> <p>Background</p> <p>Inference of protein interaction networks from various sources of data has become an important topic of both systems and computational biology. Here we present a supervised approach to identification of gene expression regulatory networks.</p> <p>Results</p> <p>The method is based on a kernel approach accompanied with genetic programming. As a data source, the method utilizes gene expression time series for prediction of interactions among regulatory proteins and their target genes. The performance of the method was verified using Saccharomyces cerevisiae cell cycle and DNA/RNA/protein biosynthesis gene expression data. The results were compared with independent data sources. Finally, a prediction of novel interactions within yeast gene expression circuits has been performed.</p> <p>Conclusion</p> <p>Results show that our algorithm gives, in most cases, results identical with the independent experiments, when compared with the YEASTRACT database. In several cases our algorithm gives predictions of novel interactions which have not been reported.</p