Seminar

Name: Yun Min Song, RNA velocity of single cells
Start: 2024-08-02T14:00:00+09:00
End: 2024-08-02T16:00:00+09:00
Location: B232 Seminar Room, IBS

Events
Seminar

S Sun	M Mon	T Tue	W Wed	T Thu	F Fri	S Sat
	has 0 events, 1 0 events, 1	has 0 events, 2 0 events, 2	has 0 events, 3 0 events, 3	has 0 events, 4 0 events, 4	has 0 events, 5 0 events, 5	has 0 events, 6 0 events, 6
has 0 events, 7 0 events, 7	has 0 events, 8 0 events, 8	has 0 events, 9 0 events, 9	has 0 events, 10 0 events, 10	has 0 events, 11 0 events, 11	has 1 event, 12 1 event, 12 2:00 pm - 4:00 pm Seokjoo Chae, Holimap: an accurate and efficient method for solving stochastic gene network dynamics July 12, 2024 @ 2:00 pm - 4:00 pm KST Seokjoo Chae, Holimap: an accurate and efficient method for solving stochastic gene network dynamics In this talk, we discuss the paper "Holimap: an accurate and efficient method for solving stochastic gene network dynamics" by Chen Jia and Ramon Grima, bioRxiv, 2024. Abstract Gene-gene interactions are crucial to the control of sub-cellular processes but our understanding of their stochastic dynamics is hindered by the lack of simulation methods that can accurately and efficiently … Continue Reading	has 0 events, 13 0 events, 13
has 0 events, 14 0 events, 14	has 0 events, 15 0 events, 15	has 0 events, 16 0 events, 16	has 0 events, 17 0 events, 17	has 0 events, 18 0 events, 18	has 1 event, 19 1 event, 19 2:00 pm - 4:00 pm Dongju Lim, Stochastic representations of ion channel kinetics and exact stochastic simulation of neuronal dynamics. July 19, 2024 @ 2:00 pm - 4:00 pm KST Dongju Lim, Stochastic representations of ion channel kinetics and exact stochastic simulation of neuronal dynamics. In this talk, we discuss the paper "Stochastic representations of ion channel kinetics and exact stochastic simulation of neuronal dynamics" by D. F. Anderson, B. Ermentrout and P. J. Thomas, Journal of Computational Neuroscience, 2015. Abstract In this paper we provide two representations for stochastic ion channel kinetics, and compare the perfor- mance of exact … Continue Reading	has 0 events, 20 0 events, 20
has 0 events, 21 0 events, 21	has 0 events, 22 0 events, 22	has 0 events, 23 0 events, 23	has 0 events, 24 0 events, 24	has 0 events, 25 0 events, 25	has 1 event, 26 1 event, 26 2:00 pm - 4:00 pm Eui Min Jeong, Temperature compensation through kinetic regulation in biochemical oscillators. July 26, 2024 @ 2:00 pm - 4:00 pm KST Eui Min Jeong, Temperature compensation through kinetic regulation in biochemical oscillators. In this talk, we discuss the paper "Temperature compensation through kinetic regulation in biochemical oscillators" by HaochenFu, Chenyi Fei, Qi Ouyang, and Yuhai Tu, to appear in PNAS. Abstract Although individual kinetic rates in biochemical reactions are sensitive to temperature, most circadian clocks exhibit a relatively constant period across a wide range of temperatures, a phenomenon called … Continue Reading	has 0 events, 27 0 events, 27
has 0 events, 28 0 events, 28	has 0 events, 29 0 events, 29	has 0 events, 30 0 events, 30	has 2 events, 31 2 events, 31 10:30 am - 12:00 pm Summer Intern workshop 2024 July 31, 2024 @ 10:30 am - 12:00 pm KST Summer Intern workshop 2024 Presentor(s) Mentor Talk title Jaehun Jeong Gyuyoung Hwang Analyzing coupled SCN cell frequencies of mammals for multi-step transcriptional model Hyunsuk Choo, Yonghee Lee Seok Joo Chae Development of a data-driven causality detection method using Taken's Theorem Juhyeon Kim Dongju Lim Accurate initial condition for circadian pacemaker model estimating the circadian phase Kyeongtae Ko Dongju … Continue Reading, 4:00 pm - 5:00 pm Hyukpyo Hong, Koopman representation: Linear representation – not an approximation – of nonlinear dynamics July 31, 2024 @ 4:00 pm - 5:00 pm KST Hyukpyo Hong, Koopman representation: Linear representation – not an approximation – of nonlinear dynamics Abstract: A system of ordinary differential equations (ODEs) is one of the most widely used tools to describe a deterministic dynamical system. In general, such ODEs involve nonlinear equations, which make analysis of dynamical systems difficult. In this talk, we introduce Koopman theory, which offers a linear representation – not an approximation – of nonlinear dynamics. In particular, we present a data-driven algorithm to find such a linear representation