This report shows the value of knowing your processes inside a company. The methods used in to do this have been first of all through process mapping and for in depth study, process cards were used to measure the process time and total lead time. This showed the location of bottlenecks and overall process capacity. With the data collected, suggestion on how to decrease storage, throughput time and total lead time is given.The study has been conducted at Intelbras in San Jose, Santa Catarina, Brazil. Intelbras is a telecommunication company which produces telephones, security cameras and switchboards. Two production processes is studied, the Telefone Sem fio, SFL, which produce cordless telephones and Central, CAL, which produce switchboards.Through simulation this paper shows improvement suggestions on how to better handle the flow of material by introducing sequencing into the production and FIFO in the storage handling, everything in order to decrease total lead time and increasing throughput time.Process mapping has proved to be a great tool in order to understand how a production process works and integrates with other departments. In supplement of process cards, valuable data is collected and used for analysing further improvements such as making a balancing program and calculating where and how big buffers is needed at different processes. / Program: Industriell ekonomi - arbetsorganisation och ledarskap

La disponibilité à la fois des données à grande échelle du transcriptome et du génome de tumeurs permet maintenant d'identifier assez facilement des oncogènes candidats, gènes qui sont surexprimés en conséquence de l'amplification d'ADN. Ces oncogènes candidats doivent alors être fonctionnellement validés et leur rôle dans la cellule normale et tumorale doit être étudié.Dans cette étude, nous nous sommes principalement focalisés sur le cancer du sein, le cancer le plus fréquent chez les femmes et la deuxième cause de décès par cancer chez les femmes à travers le monde. En France, 52.000 nouveaux cas avec 12.000 décès dus au cancer du sein ont été estimés en 2010 représentant 34% de tous les nouveaux cas de cancer chez les femmes. Les chromosomes les plus fréquemment altérés dans le cancer du sein sont les chromosomes 8, 11 et 17, qui contiennent les amplicons 17q12 (ERBB2) et 11q13 (CCND1). Le développement de «l 'herceptine" contre ERBB2 illustre le potentiel de la génomique fonctionnelle du cancer pour l'identification de cibles thérapeutiques. Plusieurs études ont identifié d'autres amplicons avec des oncogènes candidats. Cependant très peu d'études ont rapporté la validation fonctionnelle des candidats identifiés, mettant ainsi en évidence la nécessité des analyses fonctionnelles à grande échelle des différents amplicons dans le cancer du sein pour identifier de nouveaux gènes pilotes qui pourraient ensuite être utilisés pour le développement de stratégies thérapeutiques pour le cancer du sein. Ces dernières années, l'ARNi est devenu un outil de choix pour le criblage à haut débit pour caractériser la fonction des gènes dans des lignées cellulaires. Dans cette étude, nous avons effectué un criblage fonctionnel à moyen-débit basé sur l’utilisation de l'ARNi de 127 gènes amplifiés et surexprimés appartenant à 11 amplicons majeurs sur les chromosomes 8, 11 et 17 dans le cancer du sein. Ce crible à permis l'identification de 8 oncogènes au sein de 5 amplicons différents. En outre, la validation fonctionnelle de 5 de ces gènes a permis de démontrer que 4 gènes, RAD21, EIF3H, TANC2 et CHRAC1 au sein de 3 amplicons, régulent l'apoptose, la prolifération et la transformation cellulaire de cellule dérivées de carcinomes mammaires. Les régions d'altération génétique dans un cancer peuvent être également modifiées dans de multiples types d’autres cancers. L'amplicon 8p11-p12 a par exemple été décrit dans le cancer du sein, du pancréas, du poumon et de la vessie. Ces amplicons communs dans différents cancers peuvent contenir des oncogènes « pilote » communs. Pour vérifier cette hypothèse, nous avons évalué l'implication possible dans des lignées cellulaires dérivées de cancer du pancréas et du poumon présentant un amplicon en 8p11-p12 de deux oncogènes à savoir, PPAPDC1B et WHSC1L1 qui ont été décrits comme des gènes « driver » de l'amplicon 8p11-p12 dans le cancer du sein et également dans le cancer du poumon pour WHSC1L1. L'inhibition de ces deux gènes réduit la survie cellulaire et la croissance indépendante de l'ancrage à un support de lignées tumorales du pancréas et du poumon présentant une amplification en 8p11-p12. Cette constatation met en évidence l'importance de ces deux gènes dans de multiples cancers et l'intérêt thérapeutique potentiel d'inhiber ces enzymes dans les cancers présentant un amplicon en 8p11-p12. Des modèles de souris transgéniques permettent d’étudier la fonction de gènes candidats in vivo. Pour évaluer in vivo le rôle de PPAPDC1B, nous avons établi des souris transgéniques sur-exprimant PPAPDC1B sous la dépendance du promoteur de la kératine 5 permettant de cibler les épithéliums pluri ou pseudo stratifiés. Les souris transgéniques développent deux phénotypes inattendus, le développement de poils le long des incisives et une inflammation aiguë des glandes salivaires, des ganglions lymphatiques, de la vessie et du pancréas. / Availability of both large scale transcriptomic and genomic data of tumours now allows to identify relatively easily candidate oncogenes that are over-expressed as a consequence of DNA amplification. These candidate oncogenes have then to be functionally validated and studied for their role in the normal and cancer cell.In this study, we mainly focused on breast cancer, the most common cancer among women and the second leading cause of cancer deaths in women around the world. In France, 52,000 new cases with 12,000 deaths of breast cancer were estimated in 2010 accounting for 34% of all new cases of cancer in women. In breast cancer the main altered chromosomes include chromosome 8, 11 and 17 which contain the 17q12 (ERBB2) and the 11q13 (CCND1) amplicons. Development of “herceptin” against ERBB2 illustrates the potential of cancer genomics in identifying therapeutic targets. Several studies have identified other amplicons with candidate oncogenes. However very few studies reported functional validation of identified candidates, thus highlighting the need of large scale functional analyses of different amplicons in breast cancer to identify new driver genes which may be used for development of therapeutic strategies for breast cancer. In recent years, RNAi has become a tool of choice for high-throughput screening to characterize gene function in cultured cells. In this study we performed high-throughput RNAi based functional screening of 127 amplified and over-expressed genes from 11 major amplicons on chromosome 8, 11 and 17 in breast cancer. This resulted in the identification of 8 driver genes from 5 amplicons. Further functional validation of 5 of these genes demonstrated that 4 genes, RAD21, EIF3H, TANC2 and CHRAC1 from 3 amplicons, regulate breast cancer cell proliferation, apoptosis and transformation. Regions of genetic alteration in one cancer may be altered in multiple cancer types. One such example includes the 8p11-p12 amplicon which has been reported to be amplified in breast, pancreatic, lung and bladder cancer. Also common amplicons from different cancers may harbor common driver oncogenes. To investigate this hypothesis we evaluated the possible involvement in 8p11-12 amplified pancreatic and lung cancer cell lines of two oncogenes namely, PPAPDC1B and WHSC1L1 that have been described to be driver genes of the 8p11-12 amplicon in breast cancer and furthermore in lung cancer for WHSC1L1. Inhibition of both genes reduced cell survival and anchorage independent growth in amplified pancreatic and lung cancer cell lines. This finding highlights the importance of these two genes in multiple cancers and therapeutic potential interest to inhibit these enzymes in multiple cancers with 8p11-p12 amplification.Transgenic mouse models play an important role to investigate in vivo function of candidate genes. To evaluate in vivo role of PPAPDC1B, we established a transgenic mouse model over-expressing PPAPDC1B under the Keratin 5 promoter. Transgenic mice developed two unexpected phenotypes including development of hair follicles along front teeth and acute inflammation of salivary glands, lymph nodes, bladder and pancreas. This is an ongoing study that may help to understand the mechanism of action of PPAPDC1B in vivo.

The identification of drug treatments that are useful in diverse therapeutic settings is a significant driving force in biomedical research [Macarron et al., 2011], [Poureetezadi et al., 2014], [Lamb, 2007]. Typical means for measuring the efficacy of a drug for a given clinical application include protein-protein interactions, cell death, mitochondrial respiration and cell growth as well as broader measurements of absorption, distribution, metabolism, excretion and toxicity (ADMET), specifically related the the drug or drugs being tested [Szakcs et al., 2008]. A wide array of methods are routinely employed to perform these screens, from ligand binding assays [Wagner et al., 2016] to high-throughput proteomics [Verheul, 2014]. One method that is currently underutilized in small-molecule drug screens and drug discovery is high-throughput transcriptome sequencing, such as RNA-Seq. Although RNA-Seq is routinely used to profile patterns of genetic changes following perturbations such as drug treatment [Young et al., 2014], it has not, to my knowledge, yet been used as the primary readout of a drug screen.

The budding yeast S. cerevisiae is widely used as a model organism to study biological processes that are conserved among eukaryotes. Di fferent genomic approaches have been applied successfully to interrogate the mode of action of small molecules and their combinations. In this thesis, these technologies were applied to di fferent sets of chemical compounds in the context of two collaborative projects. In addition to insight into the mode of action of these molecules, novel approaches for analysis of chemical-genetic pro files to integrate GO annotation, genetic interactions and protein complex data have been developed. The fi rst project was motivated by a pressing need to design novel therapeutic strategies to combat infections caused by opportunistic fungal pathogens. Systematic screens of 1180 FDA approved drugs identifi ed 148 small molecules that exhibit synergy in combination with uconcazole, a widely used anti-fungal drug (Wright lab, McMaster University, Canada). Genome-wide chemical-genetic profiles for 6 of these drugs revealed two di fferent modes of action of synergy. Five of the compounds a ffected membrane integrity; these chemical-genetic interactions were supported by microscopy analysis and sorbitol rescue assays. The sixth compound targets a distinct membrane-associated pathway, sphingolipid biosynthesis. These results not only give insight into the mechanism of the synergistic interactions, they also provide starting points for the prediction of synergistic anti-fungal combinations with potential clinical applications. The second project characterised compounds that aff ected melanocytes in a chemical screen in zebra fish (Patton lab, Edinburgh). Chemical-genetic screens in S.cerevisiae enabled us to show that melanocyte pigmentation reducing compounds do so by interfering with copper metabolism. Further, we found that defects in intracellular AP1 and AP3 trafficking pathways cause sensitivity to low copper conditions. Surprisingly, we observed that the widely-used MAP-kinase inhibitor U0126 a ffects copper metabolism. A nitrofuran compound was found to speci fically promote melanocyte cell death in zebrafi sh. This enabled us to study off -target eff ects of these compounds that are used to treat trypanosome infections. Nifurtimox is a nitrofuran prodrug that is activated by pathogen-specifi c nitroreductases. Using yeast and zebra fish we were able to show that nitrofurans are also bioactivated by host-specifi c aldehyde dehydrogenases suggesting that a combination therapy with an aldehyde dehydrogenase inhibitor might reduce side e ffects associated with nifurtimox.

No description available.

A enorme quantidade de dados que vem sendo gerada por tecnologias modernas de biologia representam um grande desafio para áreas como a bioinformática. Há uma série de programas disponíveis para a análise destes dados, mas que nem sempre são compreendidos o suficiente para serem corretamente aplicados, ou ainda, há problemas que requerem o desenvolvimento de novas soluções. Neste trabalho, nós apresentamos a análise de dados de duas das principais fontes de dados em larga escala: microarrays e sequenciamento. Na primeira, avaliamos se a estatística do método Rank Products (RP) é adequada para a identificação de genes diferencialmente expressos em estudos de doenças complexas, cujo uma das características é a heterogeneidade genética entre indivíduos com o mesmo fenótipo. Na segunda, desenvolvemos uma ferramenta chamada hunT para buscar por genes alvos do fator de transcrição T - um importante marcador de mesoderma com papel chave no desenvolvimento de vertebrados -, através da identificação de sítios de ligação para o T em suas sequências reguladoras. O desempenho do RP foi testado usando dados simulados e dados reais de um estudo de fissura lábio-palatina não-sindrômica, de autismo e também de um estudo que avalia o efeito da privação do sono em humanos. Nossos resultados mostraram que o RP é uma solução eficiente para detectar genes consistentemente desregulados em somente um subgrupo de pacientes, que esta habilidade é mantida com poucas amostras, mas que o seu desempenho é prejudicado quando são analisados poucos genes. Obtivemos fortes evidências biológicas da eficiência do método nos estudos com dados reais através da identificação de genes e vias previamente associados às doenças e da validação de novos genes candidatos através da técnica de PCR quantitativo em tempo real. Já o programa hunT identificou 4.602 genes de camundongo com o sítio de ligação para o domínio do T, sendo alguns deles já demonstrados experimentalmente. Identificamos 32 destes genes com expressão alterada em um estudo onde avaliamos o transcriptoma da diferenciação in vitro de células tronco embrionárias de camundongo para mesoderma, sugerindo a participação destes genes neste processo sendo regulados pelo T / The large amount of data generated by modern technologies of biology provides a big challenge for areas such as bioinformatics. In order to analyze these data there are several computer programs available; however these are not always well understood enough to be correctly applied. Moreover, there are problems that require the development of new solutions. In this work, we present the data analysis of two main high-throughput data sources: microarrays and sequencing. Firstly, we evaluated whether the statistic of Rank Products method (RP) is suitable for the identification of differentially expressed genes in studies of complex diseases, which are characterized by the vast genetic heterogeneity among the individuals affected. Secondly, we developed a tool named hunT to search for target genes of T transcription factor - an important mesodermal marker that plays a key role in the vertebrate development -, by identifying binding sites for T in their regulatory sequences. The RP performance was tested using both simulated and real data from three different studies: non-syndromic cleft lip and palate, autism and sleep deprivation effect in Humans. Our results have shown that RP is an effective solution for the identification of consistently deregulated genes in a subgroup of patients, this ability is maintained even with few samples, however its performance is impaired when only few genes are analyzed. We have obtained strong biological of effectiveness of the method in the studies with real data by not only identifying genes and pathways previously associated with diseases but also corroborating the behavior of novel candidate genes with the real-time PCR technique. The hunT program has identified 4,602 mouse genes containing the binding site for the T domain, some of which have already been demonstrated experimentally. We identified 32 of these genes with altered expression in a study which evaluated the transcriptome of in vitro differentiation of mouse embryonic stem cells to mesoderm, suggesting the involvement of these genes in this process regulated by T

RNA sequencing (RNA-seq) technologies facilitate the characterisation of genes and transcripts in different cell types as well as their expression analysis across various conditions. Due to its ability to provide in-depth insights into transcription and post-transcription mechanisms, RNA-seq has been extensively used in functional genetics and transcriptomics, system biology and developmental biology in animals, plants, diseases, etc. The aim of this project is to use mathematical and computational models to integrate big genomic and transcriptomic data from high-throughput technologies in plant biology and develop new methods to identify which genes or transcripts have significant expression variation across experimental conditions of interest, then to interpret the regulatory causalities of these expression changes by distinguishing the effects from the transcription and alternative splicing. We performed a high resolution ultra-deep RNA-seq time-course experiment to study Arabidopsis in response to cold treatment where plants were grown at 20^oC and then the temperature was reduced to 4^oC. We have developed a high quality <i>Arabidopsis thaliana</i> Reference Transcript Dataset (AtRTD2) transcriptome for accurate transcript and gene quantification. This high quality time-series dataset was used as the benchmark for novel method development and downstream expression analysis. The main outcomes of this project include three parts. i) A pipeline for differential expression (DE) and differential alternative splicing (DAS) analysis at both gene and transcript levels. Firstly, we implemented data pre-processing to reduce the noise/low expression, batch effects and technical biases of read counts. Then we used the limma-voom pipeline to compare the expression at corresponding time-points of 4^oC to the time-points of 20^oC. We identified 8,949 genes with altered expression of which 2,442 showed significant DAS and 1,647 were only regulated by AS. Compared with current publications, 3,039 of these genes were novel cold-responsive genes. In addition, we identified 4,008 differential transcript usage (DTU) transcripts of which the expression changes were significantly different to their cognate DAS genes. ii) A TSIS R package for time-series transcript isoform switch (IS) analysis was developed. IS refers to the time-points when a pair of transcript isoforms from the same gene reverse their relative expression abundances. By using a five metric scheme to evaluate robustly the qualities of each switch point, we identified 892 significant ISs between the high abundance transcripts in the DAS genes and about 57% of these switches occurred very rapidly between 0-6h following transfer to 4^oC. iii) A RLowPC R package for co-expression network construction was generated. The RLowPC method uses a two-step approach to select the high-confidence edges first by reducing the search space by only picking the top ranked genes from an initial partial correlation analysis, and then computes the partial correlations in the confined search space by only removing the linear dependencies from the shared neighbours, largely ignoring the genes showing lower association. In future work, we will construct dynamic transcriptional and AS regulatory networks to interpret the causalities of DE and DAS. We will study the coupling and de-coupling of expression rhythmicity to the Arabidopsis circadian clock in response to cold. We will develop new methods to improve the statistical power of expression comparative analysis, such as by taking into account the missing values of expression and by distinguishing the technical and biological variabilities.

Advances in water resources modeling are improving the information that can be supplied to support decisions that affect the safety and sustainability of society, but these advances result in models being more computationally demanding. To facilitate the use of cost- effective computing resources to meet the increased demand through high-throughput computing (HTC) and cloud computing in modeling workflows and web applications, I developed a comprehensive Python toolkit that provides the following features: (1) programmatic access to diverse, dynamically scalable computing resources; (2) a batch scheduling system to queue and dispatch the jobs to the computing resources; (3) data management for job inputs and outputs; and (4) the ability for jobs to be dynamically created, submitted, and monitored from the scripting environment. To compose this comprehensive computing toolkit, I created two Python libraries (TethysCluster and CondorPy) that leverage two existing software tools (StarCluster and HTCondor). I further facilitated access to HTC in web applications by using these libraries to create powerful and flexible computing tools for Tethys Platform, a development and hosting platform for web-based water resources applications. I tested this toolkit while collaborating with other researchers to perform several modeling applications that required scalable computing. These applications included a parameter sweep with 57,600 realizations of a distributed, hydrologic model; a set of web applications for retrieving and formatting data; a web application for evaluating the hydrologic impact of land-use change; and an operational, national-scale, high- resolution, ensemble streamflow forecasting tool. In each of these applications the toolkit was successful in automating the process of running the large-scale modeling computations in an HTC environment.

To identify novel therapeutic targets for basal-like breast cancer (BLBC) subtype, we investigated several DNA repair mechanisms associated with maintenance of high genomic instability for cell survival in cancer cells. We identified that the mismatch repair proteins, MSH2 and MSH6 (referred to as MSH2/6 hereafter), are highly elevated across BLBC samples. High expression level of MSH2/6 in BLBC is associated with worse prognosis and survivability for patients. Therefore, we knocked out MSH2 in BLBC cell lines and performed in vivo xenograft and syngeneic mice model studies to find significant attenuation of tumor growth in MSH2 KO group. Also, MSH2-deficient BLBC cells have increased rate of new mutations. Additionally, we tested the efficacy of conventional chemotherapeutics and radiation treatment that would further tip the genomic instability in MSH2-deficient BLBC cells towards cell death, but found them to be ineffective. Next, we performed high-throughput screening of 1280 FDA-approved compounds to discover that calcium channel blockers preferentially kill MSH2-deficient BLBC cells. This was likely due to association of significantly mutated pathways that involved calcium ion binding and calmodulin binding sites. Here we provide evidence of an alternative therapeutic strategy targeting DNA repair genes in BLBC patients utilizing bioinformatics analysis, high-throughput drug screening, in vitro,and vivoexperimentalmodels.

Regulator of G-protein Signaling (RGS) proteins temporally regulate the G protein signaling cascades initiated by GPCR activation. Reports have established dysregulation of RGS expression in a variety of disease states including several cancers. Additionally, use of genetic ablation techniques has implicated RGS proteins in a variety of other disease states through the native action of the RGS i.e. not a consequence of dysregulation of RGS expression. Therefore identification and optimization of small molecule lead compounds that alter RGS protein function has emerged as a promising therapeutic strategy. In this thesis, we use high throughput screening to interrogate small molecule libraries targeting two RGS proteins, RGS6 and RGS17. RGS6 has been reported as an essential mediator of doxorubicin induced cardiotoxicity, alcohol induced cardio and hepatotoxicity, anxiety, depression, and alcohol dependence. RGS17 has largely been implicated in a variety of cancer pathogenesis, with reported over expression in prostate, lung, breast, and hepatocellular carcinomas. Chapter 2 of this work focuses on the screening efforts targeting RGS6. Three separate screening campaigns interrogating over 20K compounds led to the identification of 3 small molecules that inhibit the RGS6: Gαo protein protein interaction with appreciable selectivity over control assays. The development of a cell based protein interaction assay is discussed, and the compounds were investigated using this system. All compounds tested did not appreciably alter signal over control, meaning that the cellular activity of these compounds remains ambiguous. Chapter 3 details the screening and follow up efforts targeting RGS17. The primary screening and/or follow up of four separate screening campaigns interrogating over 110K compounds is discussed. In total, 10 identified leads and a panel of analogs were subjected to significant follow up evaluation. All compounds were found to be cysteine dependent. The second generation RGS17 inhibitors (UI series) were determined to be both cytostatic and cytotoxic against lung and prostate cancer cell lines in culture, although whether this is due to RGS17 dependent mechanisms or due to general promiscuity of the compounds remains to be determined. Lead compounds from a library provided by the NCI were found to have cellular activity and were subjected to an investigation of structure activity relationships via commercially available compounds. The active form of three of these compounds was found to be a degradation product, which is likely due to decomposition of furan or methyl furan moieties that these compounds shared. One compound demonstrated robust SAR which allowed for the generation of schemes detailing putative inhibitory mechanisms. Finally, the role of RGS17 in the transition from epithelial to mesenchymal phenotypes is investigated. RGS17 was found to cause a sub population of PC3 cells to shift to mesenchymal phenotype, indicating that RGS17 may indeed play a role in this transition. Chapter 4 focuses on efforts to investigate variable potencies of published RGS4 inhibitors against a panel of RGS proteins, with the goal of gleaning insight in to structural characteristics that influence the inhibitability of RGS proteins. Most compounds tested were found to be more potent inhibitors of RGS14 rather than RGS4 in biochemical assays. We developed the NanoBit protein complementation assay to assess the interaction of RGS proteins with either Gαi1 or Gαq in a cellular context, and used this system to investigate compound selectivity in a cellular context. The compounds tested showed selectivity for RGS2, RGS4, and RGS14 over the other RGS proteins tested. The structural differences between the RGS proteins is discussed. Chapter 5 focuses on the future directions the lab may take with respect to the projects outlined in the previous chapters. This includes the screening of more targeted libraries or even virtual screening for RGS6, the development of in vivo assessment tools for RGS17, and an expanded structural examination of RGS proteins including NMR and crystal structure analysis. Additionally, the development of the NanoBit system to interrogate RGS protein interactions that are not RGS: Gα interactions is discussed.