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  • Yun Min Song, A data-driven approach for timescale decomposition of biochemical reaction networks

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

    We will discuss about “A data-driven approach for timescale decomposition of biochemical reaction networks”, Amir Akbari, Zachary B. Haiman, Bernhard O. Palsson, bioRxiv (2023) Abstract Understanding the dynamics of biological systems in evolving environments is a challenge due to their scale and complexity. Here, we present a computational framework for timescale decomposition of biochemical reaction …

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  • Olive Cawiding, Power spectral estimate for discrete data

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

    We will discuss about “Power spectral estimate for discrete data”, Nobert Marwan and Tobias Braun, Chaos (2023).   Abstract The identification of cycles in periodic signals is a ubiquitous problem in time series analysis. Many real-world datasets only record a signal as a series of discrete events or symbols. In some cases, only a sequence …

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  • Tetsuya J. Kobayashi, Optimality of Biological Information Processing

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

    Abstract: Almost all biological systems possess the ability to gather environmental information and modulate their behaviors to adaptively respond to changing environments. While animals excel at sensing odors, even simple bacteria can detect faint chemicals using stochastic receptors. They then navigate towards or away from the chemical source by processing this sensed information through intracellular …

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  • Hyeontae Jo, AutoScore:A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records

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

    We will discuss about "AutoScore:A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records", Xie, Feng, et al., JMIR medical informatics 8.10 (2020): e21798. Abstract Background: Risk scores can be useful in clinical risk stratification and accurate allocations of medical resources, helping health providers improve patient care. Point-based scores …

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  • Hyun Kim, Significance analysis for clustering with single-cell RNA-sequencing data

    We will discuss about “Significance analysis for clustering with single-cell RNA-sequencing data”, Grabski, Isabella N., Kelly Street, and Rafael A. Irizarry., Nature Methods (2023): 1-7. Abstract Unsupervised clustering of single-cell RNA-sequencing data enables the identification of distinct cell populations. However, the most widely used clustering algorithms are heuristic and do not formally account for statistical …

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  • Eder Zavala, Quantitative analysis of high-resolution daily profiles of HPA axis hormones

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

    Abstract: The Hypothalamic-Pituitary-Adrenal (HPA) axis is the key regulatory pathway responsible for maintaining homeostasis under conditions of real or perceived stress. Endocrine responses to stressors are mediated by adrenocorticotrophic hormone (ACTH) and corticosteroid (CORT) hormones. In healthy, non-stressed conditions, ACTH and CORT exhibit highly correlated ultradian pulsatility with an amplitude modulated by circadian processes. Disruption …

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  • Matthew Simpson, Efficient prediction, estimation and identifiability analysis with mechanistic mathematical models

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

    Abstract: Interpreting data using mechanistic mathematical models provides a foundation for discovery and decision-making in all areas of science and engineering. Key steps in using mechanistic mathematical models to interpret data include: (i) identifiability analysis; (ii) parameter estimation; and (iii) model prediction. Here we present a systematic, computationally efficient likelihood-based workflow that addresses all three …

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  • Seokjoo Chae, Uncertainty quantified discovery of chemical reaction systems via Bayesian scientific machine learning

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

    We will discuss about "Uncertainty quantified discovery of chemical reaction systems via Bayesian scientific machine learning." bioRxiv (2023): 2023-09.   Abstract The recently proposed Chemical Reaction Neural Network (CRNN) discovers chemical reaction pathways from time resolved species concentration data in a deterministic manner. Since the weights and biases of a CRNN are physically interpretable, the …

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  • Samuel Isaacson, Spatial Particle Modeling of Immune Processes

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

    Abstract: Surface Plasmon Resonance (SPR) assays are a standard approach for quantifying kinetic parameters in antibody-antigen binding reactions. Classical SPR approaches ignore the bivalent structure of antibodies, and use simplified ODE models to estimate effective reaction rates for such interactions. In this work we develop a new SPR protocol, coupling a model that explicitly accounts …

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  • Alfio Quarteroni, Physics-based and data-driven numerical models for computational medicine

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

    Abstract: I will report on some recent results on modelling the heart, the external circulation, and their application to problems of clinical relevance. I will show that a proper integration between PDE-based and machine-learning algorithms can improve the computational efficiency and enhance the generality of our iHEART simulator.