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X-WR-CALDESC:Events for Institute for Quantitative and Computational Biosciences
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DTSTART;TZID=America/Los_Angeles:20220518T120000
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DTSTAMP:20260517T170755
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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. \n\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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DTSTART;TZID=America/Los_Angeles:20220518T123000
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DTSTAMP:20260517T170755
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
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