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X-WR-CALNAME:Institute for Quantitative and Computational Biosciences
X-ORIGINAL-URL:https://qcb.ucla.edu
X-WR-CALDESC:Events for Institute for Quantitative and Computational Biosciences
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DTSTART;TZID=America/Los_Angeles:20220511T120000
DTEND;TZID=America/Los_Angeles:20220511T123000
DTSTAMP:20260517T170754
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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DTSTART;TZID=America/Los_Angeles:20220511T123000
DTEND;TZID=America/Los_Angeles:20220511T130000
DTSTAMP:20260517T170754
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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