Browsing by Subject "Mammalian cell culture"
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Item Mining high-dimensional bioprocess and gene expression data for enhanced process performance(2012-07) Le, Huong Thi NgocOver the past few decades, recombinant protein therapeutics produced in cultured mammalian cells have fundamentally transformed modern medicine and improved millions of patients' lives. The drastic increase in product concentration and the number of products approved by the US Food and Drug Administration (FDA) have been attributed largely to the relentless efforts of the entire pharmaceutical community on multiple technological fronts. The remarkable advances of high-throughput genomic and process analytical tools in recent years have allowed us to extensively characterize almost all steps along a typical cell culture process. The massive amount of data generated by these technologies harbors vital information about the process, yet presents substantial challenges due to its exceptionally high dimensionality. This thesis research has applied advanced multivariate approaches to explore these sets of data and comprehend profound cellular changes during various development and manufacturing stages.Through mining a large set of manufacturing data, we uncovered a "memory" effect, suggesting that the final outcome of a production culture is primarily affected by the early seed culture. Several parameters related to lactate metabolism and cell growth were identified as having a pivotal influence on process performance. Furthermore, transcriptome analysis of cells undergoing selection and amplification was performed using multiple statistical, clustering, and functional analysis methods. Profound transcriptional changes were discerned, upon which a combined hyper-productivity gene set involving cell cycle control, signaling, and protein processing and secretion was derived. These differentially expressed genes present promising targets for cellular modulation to enhance process performance. We further developed a novel genetic tool to engineer the expression dynamics of these genes. A large number of genes with time dynamic expression trends were identified through mining time-series transcriptome data. The promoters of these genes offer effective means to drive the expression profiles of the targets in a dynamic manner. The systems approaches outlined in this research thus hold promise to deepen our understanding of process characteristics and open new avenues for process improvement.Item SAM Filtering Pipeline (SFP): Algorithm for the determination of integration sites from next generation sequencing data(2019-07-16) O'Brien, Sofie A; Hu, Wei-Shou; acre@umn.edu; Hu, Wei-ShouThe locus at which a vector harboring a product transgene integrates into the genome can have a profound effect on the transgene’s transcript level and the stability of the resulting cell line. In order to identify integration site(s) of a transfected vector from next generation genome sequencing data, the SAM filtering pipeline (SFP) was created. It is best suited for targeted sequence data, such as that from sequence capture of probed vector regions. However, it will also work for whole genome sequencing data, though the memory requirements are large (the more reads in your data set, the larger the memory requirements). A bwa-mem mapped .sam file is required as input to the pipeline.Item Systems analysis of complex biological data for bioprocess enhancement.(2008-12) Charaniya, Salim PyaraliRecent advances in data-driven knowledge discovery approaches, such as `omics' technologies, provide enormous opportunities to uncover the multifarious determinants of several pharmaceutically relevant biological traits. This work focuses on the challenges, which include: (i) Deciphering the regulation of antibiotic production in Streptomyces coelicolor, and (ii) Elucidating the attributes of high recombinant protein productivity in mammalian cell culture processes. The phenotypic complexity of Streptomycetes, which produce several clinically relevant antibiotics and other natural products, manifests in their diversity of secondary metabolism and morphological differentiation. To identify the dynamic gene regulatory networks that confer such complex phenotypes, the temporal transcriptomic characteristics of the model organism S. coelicolor, under more than twenty-five diverse genetic and environmental perturbations, were integrated with other functional and genomic features. A whole-genome operon map was also predicted, and a significant portion of the map was experimentally verified. Such a systems approach can reveal several insights about the functional processes relevant for antibiotics production. The therapeutic value of recombinant proteins has brought about a continuously rising demand that is met by development of hyper-producing mammalian cell lines. However, the molecular ingredients of high productivity are not well understood. The transcriptomes of several recombinant antibody-producing NS0 cell lines with a wide productivity range were surveyed in an attempt to identify the physiological functions that are modulated in high-producing cells. Cell culture process enhancement also entails an understanding of the process parameters and their interactions, which are critical determinants of high recombinant protein productivity. The comprehensive process archives of modern production plants present vast, underutilized resources containing information that, if unearthed, can enhance process robustness. The on-line and off-line process data of several production `trains' from a commercial manufacturing facility were investigated using kernel-based machine learning tools to elucidate predictive correlations between process parameters and the outcome. Together, such discovery strategies based on integrative data mining hold immense potential for enhancing our understanding of industrially relevant biological processes.Item Transcriptome analysis in mammalian cell culture: Applications in process development and characterization.(2009-07) Kantardjieff, AnneThe advent of recombinant protein therapeutics more than two decades ago fundamentally transformed healthcare paradigms and has since improved the quality of life of millions of people. The production of these complex products is typically carried out in cultured mammalian cell lines, with a few cell lines accounting for the majority of production. Chief among these is the Chinese hamster ovary (CHO) cell line. Despite its importance, little genomic information is currently available in the public domain for this cell line. Consequently, our lab has devoted significant efforts to the development of genomic tools for CHO, including custom Affymetrix microarrays. These tools enable the global study of cellular gene expression. This thesis research has applied these transcriptome analysis tools to further understand the process of recombinant protein production. The course of bringing a recombinant protein product to production scale involves a series of complex and often lengthy steps. As demand for these products continues to increase, there is a need to streamline process development efforts. One approach to facilitate this process is to increase our fundamental understanding of cell culture processes. Microarrays are well-suited to this application, and in this work, transcriptome analysis has been used to characterize multiple facets of cell culture process development, including cell line development and modulation of process parameters in fed-batch cultures. We found significant variation amongst clonal gene expression profiles during cell line development, an observation which could be exploited to develop gene expression-based clone screening protocols. We also uncovered the widespread impact process parameters can have on cellular gene expression. In particular, we found that raw material source, namely different hydrolysate lots, has a profound effect on the transcriptional signatures of fed-batch cell culture processes. As next-generation sequencing technologies become increasingly mature and cost-effective, they are now being applied to the study of gene expression. We have used ultra high-throughput sequencing to investigate the deep transcriptome of CHO cells. We found that the technology correlated well with microarrays, and displayed a significantly broader detection range. Through this analysis, we also identified a number of transcriptionally-active regions in the CHO genome. The unprecedented depth achievable through next-generation sequencing now allows us to set genome sequencing firmly in our sights.