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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:20220112T113000
DTEND;TZID=America/Los_Angeles:20220112T120000
DTSTAMP:20260517T210233
CREATED:20211209T201215Z
LAST-MODIFIED:20220118T163732Z
UID:20116-1641987000-1641988800@qcb.ucla.edu
SUMMARY:QCBio Research Seminar: Evan Maltz (Wollman)\, Graduate Student in BMSB (Biochemistry\, Molecular and Structural Biology)
DESCRIPTION:TITLE: “Phenotypic consequences of gene expression variability.” \nABSTRACT: The central dogma of biology proposes that information flows from genes to RNA to protein\, determining protein identity and abundance. Single cell transcriptome measurements provide ample information about RNA abundance in cells. While the expression of individual genes clearly matters for determining phenotype\, they do not work independently. How the information in RNA abundances is combined to determine cellular phenotype remains unclear. Here we address this question for a single cellular phenotype\, the dynamic Ca2+ signaling response to a ligand. Using an information theory approach\, we estimated the mutual information between the expression of subsets of 83 genes in the Ca2+ signaling network and the dynamic Ca2+ response in the same cell. We found a high degree of dependency that corresponds to 60% of Ca2+ signal entropy\, yet most genes have redundant information. Furthermore\, a relatively small group of genes preserve most of the dependency. Our results demonstrate a high degree of regulatory elasticity with small changes in RNA abundance informing an emergent cellular phenotype. \n\nhttps://qcb.ucla.edu/wp-content/uploads/sites/14/2021/12/Evan-Maltz-edited.mp4\n  \nVideos can also be viewed on the QCBio YouTube Channel
URL:https://qcb.ucla.edu/event/qcbio-research-seminar-evan-maltz-wollman-graduate-student/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:Research Seminars
ATTACH;FMTTYPE=image/png:https://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2021/12/Malt-Evan.png
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DTSTART;TZID=America/Los_Angeles:20220112T123000
DTEND;TZID=America/Los_Angeles:20220112T130000
DTSTAMP:20260517T210233
CREATED:20211202T150300Z
LAST-MODIFIED:20220114T205155Z
UID:20052-1641990600-1641992400@qcb.ucla.edu
SUMMARY:QCBio Research Seminar: Dongyuan Song (Li JJ)\, Graduate Student in Bioinformatics
DESCRIPTION:TITLE: “scDesign3: an all-in-one statistical framework that generates realistic single-cell omics data and infers cell heterogeneity structure.” \nABSTRACT: The generation of realistic synthetic data is essential for benchmarking numerous computation tools developed for single-cell omics data. Here we propose an all-in-one statistical framework that generates single-cell omics data from various cell heterogeneity structures\, including discrete cell types\, continuous cell trajectories\, and spatial cell locations. Our framework uses a unified probabilistic model with accessible likelihood. This probabilistic formulation is advantageous in that it enables a straightforward discernment of the heterogeneity structure that best fits a single-cell omics dataset\, by leveraging the statistical model selection principle. \nhttps://qcb.ucla.edu/wp-content/uploads/sites/14/2021/12/Dongyuan-Song-edited.mp4\n 
URL:https://qcb.ucla.edu/event/qcbio-research-seminar-dongyuan-song-li-jj-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/14/2021/12/DongyuanISMB2021.jpg
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