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X-WR-CALDESC:Events for Institute for Quantitative and Computational Biosciences
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220511T120000
DTEND;TZID=America/Los_Angeles:20220511T123000
DTSTAMP:20260517T170143
CREATED:20220505T154246Z
LAST-MODIFIED:20220512T161022Z
UID:21666-1652270400-1652272200@qcb.ucla.edu
SUMMARY:QCBio Research Seminar: Keunseok Park (Park)\, Grad Student\, Chemical and Bimolecular Engineering
DESCRIPTION:TITLE: “G-Flux: a metabolic flux and free energy analysis software for interpreting 13C\, 2H\, 18O\, and 15N isotope tracing data.” \nABSTRACT: Metabolic fluxes offer insights into pathway utilization\, kinetics\, and thermodynamics. Stable isotope tracing and metabolic footprinting are widely used for inferring metabolic fluxes. However\, quantitative flux measurement across broad metabolism remains challenging due to our inability to convert multiple isotope distributions into fluxes and integrate exometabolomics data. Here we develop a software application\, G-Flux\, for computing metabolic fluxes by tracing 13C\, 2H\, 18O\, and 15N. Using the ratio of forward to backward fluxes\, G-Flux can compute reaction Gibbs free energies (ΔG). We use G-Flux to compute fluxes and ΔG in E. coli and mammalian cells. Using these results and G-Flux’s ability to simulate isotope labeling\, we find that [6-18O1]glucose is ideal for quantifying ΔG of glycolytic reactions and dual tracing of [U-13C6]glucose and [U-15N2]glutamine for quantifying fluxes and ΔG of transaminase and amino acid degradation. Thus\, G-Flux facilitates comprehensive metabolic flux and free energy analysis. \n\nhttps://qcb.ucla.edu/wp-content/uploads/sites/14/2022/05/Keunseok-Park-edited.mp4
URL:https://qcb.ucla.edu/event/qcbio-research-seminar-keunseok-park-park-grad-student-chemical-and-bimolecular-engineering/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:Research Seminars
ATTACH;FMTTYPE=image/png:https://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2022/05/KEUNSEOK-PARK-PHOTO.png
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220511T123000
DTEND;TZID=America/Los_Angeles:20220511T130000
DTSTAMP:20260517T170143
CREATED:20220504T232818Z
LAST-MODIFIED:20220512T161538Z
UID:21658-1652272200-1652274000@qcb.ucla.edu
SUMMARY:QCBio Research Seminar: Heather Zhou (Li JJ)\, Graduate Student in Statistics
DESCRIPTION:TITLE: “PCA outperforms popular hidden variable inference methods for QTL mapping.” \nABSTRACT: Estimating and accounting for hidden variables is widely practiced as an important step in quantitative trait locus (QTL) analysis for improving the power of QTL identification. However\, few benchmark studies have been performed to evaluate the efficacy of the various methods developed for this purpose. Here we benchmark popular hidden variable inference methods including surrogate variable analysis (SVA)\, probabilistic estimation of expression residuals (PEER)\, and hidden covariates with prior (HCP) against principal component analysis (PCA)—a well-established dimension reduction and factor discovery method—via 362 synthetic and 110 real data sets. We show that PCA not only underlies the statistical methodology behind the popular methods but is also orders of magnitude faster\, better-performing\, and much easier to interpret and use. The preprint is available at https://www.biorxiv.org/content/10.1101/2022.03.09.483661v1. \nhttps://qcb.ucla.edu/wp-content/uploads/sites/14/2022/05/Heather-Zhou-edited.mp4
URL:https://qcb.ucla.edu/event/qcbio-research-seminar-heather-zhou-li-jj-graduate-student-in-statistics/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:Research Seminars
ATTACH;FMTTYPE=image/jpeg:https://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2022/05/Heather-Zhou.jpeg
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220518T120000
DTEND;TZID=America/Los_Angeles:20220518T123000
DTSTAMP:20260517T170143
CREATED:20220502T135343Z
LAST-MODIFIED:20220519T170741Z
UID:21628-1652875200-1652877000@qcb.ucla.edu
SUMMARY:QCBio Research Seminar: Yue Wang (Chou)\, Postdoc\, Department of Computational Medicine
DESCRIPTION:TITLE: “Stochastic Model and Optimization of SELEX.” \nABSTRACT: Systematic Evolution of Ligands by EXponential enrichment (SELEX) is a process to select the best aptamer sequence in a huge aptamer library that binds a specified target molecule with the highest affinity. There has been a deterministic model of SELEX\, and we develop a fully discrete stochastic model to obtain more accurate results when the mass action law does not hold. Specifically\, we find that the optimal SELEX protocol in the stochastic model differs from that predicted by the deterministic model. \nhttps://qcb.ucla.edu/wp-content/uploads/sites/14/2022/05/Yue-Wang-edited.mp4
URL:https://qcb.ucla.edu/event/qcbio-research-seminar-yue-wang-chou-postdoc-department-of-computational-medicine/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:Research Seminars
ATTACH;FMTTYPE=image/png:https://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2022/05/Yue.png
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220518T123000
DTEND;TZID=America/Los_Angeles:20220518T130000
DTSTAMP:20260517T170143
CREATED:20220502T135810Z
LAST-MODIFIED:20220519T173324Z
UID:21633-1652877000-1652878800@qcb.ucla.edu
SUMMARY:QCBio Research Seminar: Jackson Chin (Meyer)\, Graduate Student in Bioengineering
DESCRIPTION:TITLE: “Tensor Factorization for Interpreting the Mechanisms of MRSA Persistence.” \nABSTRACT: \nMethicillin-resistant Staphylococcus aureus (MRSA) bacteria is an increasingly common and life-threatening infection. While some antibiotics resolve MRSA infections in vitro\, these same antibiotics often fail to clear an infection when used to treat patients\, suggesting that MRSA persistence is a confluence of both host and bacterial factors. While recent research has identified critical genetic and proteomic determinants of MRSA persistence\, the mechanisms that drive MRSA persistence are still poorly understood. Here\, to better understand these mechanisms\, we implement tensor-based factorization to integrate genetic and proteomic data collected from two cohorts of patients with MRSA infections. We find that our factorization process identifies patterns across biological modalities and is able to explain 75% of the variance observed in genetic and proteomic data with just 8 components. Additionally\, this data integration improves persistence prediction accuracy as prediction models trained on these integrated factors demonstrate accuracies as high as 80% over subsets of the cohorts. Interpretation of these components further reveals mechanisms that drive the infection response and highlight processes critical for MRSA persistence. Overall\, these results suggest that tensor-based factorization can identify the mechanisms underlying MRSA persistence across host factors and improve our ability to predict and understand MRSA persistence. \nhttps://qcb.ucla.edu/wp-content/uploads/sites/14/2022/05/Jackson-Chin-edited.mp4
URL:https://qcb.ucla.edu/event/qcbio-research-seminar-jackson-chin-meyer-graduate-student-in-bioengineering/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:Research Seminars
ATTACH;FMTTYPE=image/jpeg:https://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2022/05/20201202_135158_headshot.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220525T113000
DTEND;TZID=America/Los_Angeles:20220525T120000
DTSTAMP:20260517T170143
CREATED:20220519T183319Z
LAST-MODIFIED:20220601T233916Z
UID:21718-1653478200-1653480000@qcb.ucla.edu
SUMMARY:QCBio Research Seminar: Connor Razma (Hoffmann)\, BS/MS Student
DESCRIPTION:TITLE: “Baseline MEthylation Patterns prior to flu vaccination.” \nABSTRACT: Influenza affects millions worldwide each year with responses varying from individual to individual. Influenza can be broken down into subtypes specifically H1N1\, H3N2\, Yamagata\, and Victoria. One way to measure the immune response to influenza is to measure a person’s antibody response to influenza. To measure how many antibodies are present in a sample\, a hemagglutination inhibition assay (HAI) is used. DNA methylation is an epigenetic mechanism used to regulate gene expression in cells. Its mechanism of action is the addition of a methyl group to cytosine at a cytosine-guanine pair. DNA methylation has been shown to change in response to stimuli such as viral or bacterial infections. DNA methylation can be measured by bisulfite sequencing\, specifically reduced representation bisulfite sequencing in our case. In this study\, data was taken from patients who had the flu vaccination. Their antibody data was measured using the HAI assay by the University of Georgia and their methylation data was measured using reduced representation bisulfite sequencing by the Pellegrini and Reed lab at UCLA. Using various statistical learning algorithms we were able to find methylated sites that were good predictors of vaccine response. Elastic net regression proved to be a particularly good predictor of vaccine response\, and after further analysis\, it was revealed that the best prediction happened with only a few significant sites. Some of these significant sites seem to be involved in regulating immune response and membrane function. Further work will be done to determine the prediction accuracy of these algorithms with just these sites. Ideally\, after this future work and other experiments\, these methylated sites can be used as biomarkers to indicate response to flu vaccination. \nhttps://qcb.ucla.edu/wp-content/uploads/sites/14/2022/05/Connor-Razma-edited.mp4
URL:https://qcb.ucla.edu/event/qcbio-research-seminar-connor-razma-hoffmann-undergraduate-bioinformatics-researcher/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:Research Seminars
ATTACH;FMTTYPE=image/jpeg:https://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2022/05/281170460_1052299975682517_1062133480470997117_n-copy.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220525T120000
DTEND;TZID=America/Los_Angeles:20220525T123000
DTSTAMP:20260517T170143
CREATED:20220517T233629Z
LAST-MODIFIED:20220602T000036Z
UID:21686-1653480000-1653481800@qcb.ucla.edu
SUMMARY:QCBio Research Seminar: Ha Vu (Ernst)\, Graduate Student in Bioinformatics
DESCRIPTION:TITLE: “Universal annotation of the human genome through integration of over a thousand epigenomic datasets.” \nABSTRACT: Genome-wide maps of chromatin marks such as histone modifications and open chromatin sites provide valuable information for annotating the non-coding genome\, including identifying regulatory elements. Computational approaches such as ChromHMM have been applied to discover and annotate chromatin states defined by combinatorial and spatial patterns of chromatin marks within the same cell type. An alternative ‘stacked modeling’ approach was previously suggested\, where chromatin states are defined jointly from datasets of multiple cell types to produce a single universal genome annotation based on all datasets. Despite its potential benefits for applications that are not specific to one cell type\, such an approach was previously applied only for small-scale specialized purposes. Large-scale applications of stacked modeling have previously posed scalability challenges.\nUsing a version of ChromHMM enhanced for large-scale applications\, we apply the stacked modeling approach to produce a universal chromatin state annotation of the human genome using over 1000 datasets from more than 100 cell types\, with the learned model denoted as the full-stack model. The full-stack model states show distinct enrichments for external genomic annotations\, which we use in characterizing each state. Compared to per-cell-type annotations\, the full-stack annotations directly differentiate constitutive from cell type specific activity and is more predictive of locations of external genomic annotations.\nThe full-stack ChromHMM model provides a universal chromatin state annotation of the genome and a unified global view of over 1000 datasets. We expect this to be a useful resource that complements existing per-cell-type annotations for studying the non-coding human genome. \nhttps://qcb.ucla.edu/wp-content/uploads/sites/14/2022/05/Ha-Vu-edited.mp4
URL:https://qcb.ucla.edu/event/qcbio-research-seminar-ha-vu-ernst-graduate-student-in-bioinformatics/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:Research Seminars
ATTACH;FMTTYPE=image/jpeg:https://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2022/05/havu_portrait.jpg
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