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PRODID:-//Biomedical Mathematics Group - ECPv6.15.20//NONSGML v1.0//EN
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X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20200101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210401T120000
DTEND;TZID=Asia/Seoul:20210401T130000
DTSTAMP:20260427T212439
CREATED:20210331T003338Z
LAST-MODIFIED:20210406T075108Z
UID:4352-1617278400-1617282000@www.ibs.re.kr
SUMMARY:Yun Min Song\, A stochastic oscillator model simulates the entrainment of vertebrate cellular clocks by light
DESCRIPTION:We will discuss about “A stochastic oscillator model simulates the entrainment of vertebrate cellular clocks by light”\, Kumpost et al.\, bioRxiv (2021) \nThe circadian clock is a cellular mechanism that synchronizes various biological processes with respect to the time of the day. While much progress has been made characterizing the molecular mechanisms underlying this clock\, it is less clear how external light cues influence the dynamics of the core clock mechanism and thereby entrain it with the light-dark cycle. Zebrafish-derived cell cultures possess clocks that are directly light-entrainable\, thus providing an attractive laboratory model for circadian entrainment. Here\, we have developed a stochastic oscillator model of the zebrafish circadian clock\, which accounts for the core clock negative feedback loop\, light input\, and the proliferation of single-cell oscillator noise into population-level luminescence recordings. The model accurately predicts the entrainment dynamics observed in bioluminescent clock reporter assays upon exposure to a wide range of lighting conditions. Furthermore\, we have applied the model to obtain refitted parameter sets for cell cultures exposed to a variety of pharmacological treatments and predict changes in single-cell oscillator parameters. Our work paves the way for model-based\, large-scale screens for genetic or pharmacologically-induced modifications to the entrainment of circadian clock function.
URL:https://www.ibs.re.kr/bimag/event/2021-04-02/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210409T120000
DTEND;TZID=Asia/Seoul:20210409T130000
DTSTAMP:20260427T212439
CREATED:20210323T105030Z
LAST-MODIFIED:20210407T041048Z
UID:4304-1617969600-1617973200@www.ibs.re.kr
SUMMARY:Highly accurate fluorogenic DNA sequencing with information theory–based error correction
DESCRIPTION:We will discuss about “Highly accurate fluorogenic DNA sequencing with information theory–based error correction”\, Chen et al.\, Nature Biotechnology (2017) \nEliminating errors in next-generation DNA sequencing has proved challenging. Here we present error-correction code (ECC) sequencing\, a method to greatly improve sequencing accuracy by combining fluorogenic sequencing-by-synthesis (SBS) with an information theory–based error-correction algorithm. ECC embeds redundancy in sequencing reads by creating three orthogonal degenerate sequences\, generated by alternate dual-base reactions. This is similar to encoding and decoding strategies that have proved effective in detecting and correcting errors in information communication and storage. We show that\, when combined with a fluorogenic SBS chemistry with raw accuracy of 98.1%\, ECC sequencing provides single-end\, error-free sequences up to 200 bp. ECC approaches should enable accurate identification of extremely rare genomic variations in various applications in biology and medicine. \n  \n 
URL:https://www.ibs.re.kr/bimag/event/2021-04-09/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210416T120000
DTEND;TZID=Asia/Seoul:20210416T130000
DTSTAMP:20260427T212439
CREATED:20210412T110458Z
LAST-MODIFIED:20210412T110458Z
UID:4423-1618574400-1618578000@www.ibs.re.kr
SUMMARY:Synthetic multistability in mammalian cells
DESCRIPTION:We will discuss about “Synthetic multistability in mammalian cells”\, Zhu et al.\, bioRxiv (2021) \nIn multicellular organisms\, gene regulatory circuits generate thousands of molecularly distinct\, mitotically heritable states\, through the property of multistability. Designing synthetic multistable circuits would provide insight into natural cell fate control circuit architectures and allow engineering of multicellular programs that require interactions among cells in distinct states. Here we introduce MultiFate\, a naturally-inspired\, synthetic circuit that supports long-term\, controllable\, and expandable multistability in mammalian cells. MultiFate uses engineered zinc finger transcription factors that transcriptionally self-activate as homodimers and mutually inhibit one another through heterodimerization. Using model-based design\, we engineered MultiFate circuits that generate up to seven states\, each stable for at least 18 days. MultiFate permits controlled state-switching and modulation of state stability through external inputs\, and can be easily expanded with additional transcription factors. Together\, these results provide a foundation for engineering multicellular behaviors in mammalian cells. \n  \n 
URL:https://www.ibs.re.kr/bimag/event/2021-04-16/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210422T120000
DTEND;TZID=Asia/Seoul:20210422T130000
DTSTAMP:20260427T212439
CREATED:20210417T101617Z
LAST-MODIFIED:20210419T021327Z
UID:4477-1619092800-1619096400@www.ibs.re.kr
SUMMARY:A Simple and Flexible Computational Framework for Inferring Sources of Heterogeneity from Single-Cell Dynamics
DESCRIPTION:We will discuss about “A Simple and Flexible Computational Framework for\nInferring Sources of Heterogeneity from Single-Cell\nDynamics”\, Dharmarajan et al.\, Cell Systems (2019) \nSingle-cell time-lapse data provide the means for disentangling sources of cell-to-cell and intra-cellular variability\, a key step for understanding heterogeneity in cell populations. However\, single-cell analysis with dynamic models is a challenging open problem: current inference methods address only single-gene expression or neglect parameter correlations. We report on a simple\, flexible\, and scalable method for estimating cell-specific and population-average parameters of non-linear mixed-effects models of cellular networks\, demonstrating its accuracy with a published model and dataset. We also propose sensitivity analysis for identifying which biological sub-processes quantitatively and dynamically contribute to cell-to-cell variability. Our application to endocytosis in yeast demonstrates that dynamic models of realistic size can be developed for the analysis of single-cell data and that shifting the focus from single reactions or parameters to nuanced and time-dependent contributions of sub-processes helps biological interpretation. Generality and simplicity of the approach will facilitate customized extensions for analyzing single-cell dynamics
URL:https://www.ibs.re.kr/bimag/event/2021-04-22/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210429T120000
DTEND;TZID=Asia/Seoul:20210429T130000
DTSTAMP:20260427T212439
CREATED:20210425T180554Z
LAST-MODIFIED:20210425T180554Z
UID:4499-1619697600-1619701200@www.ibs.re.kr
SUMMARY:Introduction to Bayesian ML/DL\, with Application to Parameter Inference of Coupled Non-linear ODEs - Part 1
DESCRIPTION:In this talk\, the speaker will present introductory materials about Bayesian Machine Learning. \nAbstract\nGaussian process(GP) is a stochastic process such that the joint distribution of an arbitrary finite subset of the random variables is a multivariate normal. It plays a fundamental role in Bayesian machine learning as it can be interpreted as a prior over functions (Rasmussen and Williams\, 2006)\, hence providing a nonparametric approach to various tasks. In the first part\, I will introduce the general framework of GP and some underlying theory\, accompanied by an illustrative example of GP regression\, also known as Kringing. In the second part\, I will introduce some recent works on applying GP to parameter inference of coupled non-linear ODEs arising in various biological contexts.
URL:https://www.ibs.re.kr/bimag/event/introduction-to-bayesian-ml-dl-with-application-to-parameter-inference-of-coupled-non-linear-odes-part-1/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
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