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  • Candan Celik, The effect of microRNA on protein variability and gene expression fidelity

    B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

    We will discuss about “The effect of microRNA on protein variability and gene expression fidelity”, Hilfinger, Andreas, and Raymond Fan., Biophysical journal 122.3 (2023): 537a. Abstract Small regulatory RNA molecules such as microRNA modulate gene expression through inhibiting the translation of messenger RNA (mRNA). Such post-transcriptional regulation has been recently hypothesized to reduce the stochastic variability …

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  • (Rescheduled: 3/22 -> 3/24) Stefan Bauer, Neural Causal Models for Experimental Design

    ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)

    Abstract: Many questions in everyday life as well as in research are causal in nature: How would the climate change if we lower train prices or will my headache go away if I take an aspirin? Inherently, such questions need to specify the causal variables relevant to the question and their interactions. However, existing algorithms …

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  • Sungwoong Cho, HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork

    B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

    Fast and accurate predictions for complex physical dynamics are a big challenge across various applications. Real-time prediction on resource-constrained hardware is even more crucial in the real-world problems. The deep operator network (DeepONet) has recently been proposed as a framework for learning nonlinear mappings between function spaces. However, the DeepONet requires many parameters and has …

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  • George Karniadakis, BINNS: Biophysics-Informed Neural Networks

    ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)

    Abstract: We will present a new approach to develop a data-driven, learning-based framework for predicting outcomes of biophysical systems and for discovering hidden mechanisms and pathways from noisy data. We will introduce a deep learning approach based on neural networks (NNs) and on generative adversarial networks (GANs). Unlike other approaches that rely on big data, …

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  • Yun Min Song, The ups and downs of biological oscillators: A comparison of time-delayed negative feedback mechanisms

    B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

    We will discuss about “The ups and downs of biological oscillators: A comparison of time-delayed negative feedback mechanisms”,Rombouts, Jan, Sarah Verplaetse, and Lendert Gelens., bioRxiv (2023) Abstract Many biochemical oscillators are driven by the periodic rise and fall of protein concentrations or activities. A negative feedback loop underlies such oscillations. The feedback can act on …

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  • Hyun Kim, Comparison of transformations for single-cell RNA-seq data

    B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

    We will discuss about “Comparison of transformations for single-cell RNA-seq data”,Ahlmann-Eltze, Constantin, and Wolfgang Huber, Nature Methods (2023): 1-8. Abstract The count table, a numeric matrix of genes × cells, is the basic input data structure in the analysis of single-cell RNA-sequencing data. A common preprocessing step is to adjust the counts for variable sampling efficiency and …

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  • Seokjoo Chae, Improving gene regulatory network inference and assessment: The importance of using network structure

    B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

    We will discuss about “Improving gene regulatory network inference and assessment: The importance of using network structure”, Escorcia-Rodríguez, Juan M., et al., bioRxiv (2023): 2023-01. Abstract Gene regulatory networks are graph models representing cellular transcription events. Networks are far from complete due to time and resource consumption for experimental validation and curation of the interactions. Previous …

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  • Hans P.A. Van Dongen, Modeling the temporal dynamics of neurobehavioral performance impairment due to sleep loss and circadian misalignment

    ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)

    Abstract: The well-known two-process model of sleep regulation makes accurate predictions of sleep timing and duration, as well as neurobehavioral performance, for a variety of acute sleep deprivation and nap sleep scenarios, but it fails to predict the effects of chronic sleep restriction on neurobehavioral performance. The two-process model belongs to a broader class of …

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  • Understanding Trade-offs in Biological Information Processing

    B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

    High accuracy of major biological processes relies on the ability of the participating enzymatic molecules to preferentially select the correct substrate from a pool of chemically similar substrates by activating the so-called proofreading mechanisms. While the importance of such mechanisms is widely accepted, it is still unclear how evolution has optimized biological systems with respect …

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  • Uncovering the mechanisms of pattern formation and emergent collective behaviors in myxobacteria

    KAIST E6-1 1501 Auditorium 291 Daehak-ro, Yuseong-gu, Daejeon, Korea, Republic of

    Collective cell movement is critical to the emergent properties of many multicellular systems including microbial self-organization in biofilms, wound healing, and cancer metastasis. However, even the best-studied systems lack a complete picture of how diverse physical and chemical cues act upon individual cells to ensure coordinated multicellular behavior. Myxococcus xanthus is a model bacteria famous …

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  • Kyongwon Kim, On sufficient graphical models

    B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

    We introduce a sufficient graphical model by applying the recently developed nonlinear sufficient dimension reduction techniques to the evaluation of conditional independence. The graphical model is nonparametric in nature, as it does not make distributional assumptions such as the Gaussian or copula Gaussian assumptions. However, unlike a fully nonparametric graphical model, which relies on the …

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