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  • Seokjoo Chae, Synthetic gene networks recapitulate dynamic signal decoding and differential gene expression

    KAIST E2-1 room 3221 E2-1 building, Daejeon, Daejeon, Korea, Republic of

    We will discuss about "Synthetic gene networks recapitulate dynamic signal decoding and differential gene expression", Benzinger et al., bioRxiv (2021) Cells live in constantly changing environments and employ dynamic signaling pathways to transduce information about the signals they encounter. However, the mechanisms by which dynamic signals are decoded into appropriate gene expression patterns remain poorly …

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  • Yun Min Song, On the quasi-steady-state approximation in an open Michaelis-Menten reaction mechanism

    KAIST E2-1 room 3221 E2-1 building, Daejeon, Daejeon, Korea, Republic of

    We will discuss about "On the quasi-steady-state approximation in an open Michaelis-Menten reaction mechanism", bioRxiv (2021). The conditions for the validity of the standard quasi-steady-state approximation in the Michaelis--Menten mechanism in a closed reaction vessel have been well studied, but much less so the conditions for the validity of this approximation for the system with …

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  • Hyukpyo Hong, Frequency Spectra and the Color of Cellular Noise

    KAIST E2-1 room 3221 E2-1 building, Daejeon, Daejeon, Korea, Republic of

    We will discuss about "Frequency Spectra and the Color of Cellular Noise",  bioRxiv (2020). The invention of the Fourier integral in the 19th century laid the foundation for modern spectral analysis methods. By decomposing a (time) signal into its essential frequency components, these methods uncovered deep insights into the signal and its generating process, precipitating …

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  • Eui Min Jeong, Pairing of segmentation clock genes drives robust pattern formation

    Tea Room, IBS Daejeon, Daejeon, Korea, Republic of

    We will discuss about "Pairing of segmentation clock genes drives robust pattern formation", Zinani et al., Nature (2021) Gene expression is an inherently stochastic process; however, organismal development and homeostasis require cells to coordinate the spatiotemporal expression of large sets of genes. In metazoans, pairs of co-expressed genes often reside in the same chromosomal neighbourhood, …

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  • Dae Wook Kim, Maximum Entropy Framework for Predictive Inference of Cell Population Heterogeneity and Responses in Signaling Networks

    Tea Room, IBS Daejeon, Daejeon, Korea, Republic of

    We will discuss about "Maximum Entropy Framework for Predictive Inference of Cell Population Heterogeneity and Responses in Signaling Networks", Dixit et al., Cell Systems (2020) Predictive models of signaling networks are essential for understanding cell population heterogeneity and designing rational interventions in disease. However, using computational models to predict heterogeneity of signaling dynamics is often …

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  • Seokjoo Chae, Unified rational protein engineering with sequence-based deep representation learning

    Tea Room, IBS Daejeon, Daejeon, Korea, Republic of

    In this presentation, we are going to discuss the paper, "Unified rational protein engineering with sequence-based deep representation learning" Abstract Rational protein engineering requires a holistic understanding of protein function. Here, we apply deep learning to unlabeled amino-acid sequences to distill the fundamental features of a protein into a statistical representation that is semantically rich …

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  • Yun Min Song, A stochastic oscillator model simulates the entrainment of vertebrate cellular clocks by light

    B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

    We will discuss about "A stochastic oscillator model simulates the entrainment of vertebrate cellular clocks by light", Kumpost et al., bioRxiv (2021) The 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, …

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  • Highly accurate fluorogenic DNA sequencing with information theory–based error correction

    B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

    We will discuss about "Highly accurate fluorogenic DNA sequencing with information theory–based error correction", Chen et al., Nature Biotechnology (2017) Eliminating 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 …

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  • Synthetic multistability in mammalian cells

    B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

    We will discuss about "Synthetic multistability in mammalian cells", Zhu et al., bioRxiv (2021) In 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 …

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  • A Simple and Flexible Computational Framework for Inferring Sources of Heterogeneity from Single-Cell Dynamics

    B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

    We will discuss about "A Simple and Flexible Computational Framework for Inferring Sources of Heterogeneity from Single-Cell Dynamics", Dharmarajan et al., Cell Systems (2019) Single-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 …

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  • Introduction to Bayesian ML/DL, with Application to Parameter Inference of Coupled Non-linear ODEs – Part 1

    B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

    In this talk, the speaker will present introductory materials about Bayesian Machine Learning. Abstract Gaussian 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 …

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  • Introduction to Bayesian ML/DL, with Application to Parameter Inference of Coupled Non-linear ODEs – Part 2

    B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

    In this talk, the speaker will present introductory materials about Bayesian Machine Learning. Abstract The problem of approximating the posterior distribution (or density estimation in general) is a crucial problem in Bayesian statistics, in which intractable integrals often become the computational bottleneck. MCMC sampling is the most widely used family of algorithms for approximating posteriors. …

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