기초과학연구원 의생명수학그룹
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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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We will discuss about "An accurate probabilistic step finder for time-series analysis." bioRxiv (2023): 2023-09. Abstract Noisy time-series data is commonly collected from sources including Förster Resonance Energy Transfer experiments, patch clamp and force spectroscopy setups, among many others. Two of the most common paradigms for the detection of discrete transitions in such time-series data … |
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We will discuss about "Hard limits and performance tradeoffs in a class of antithetic integral feedback networks." Cell systems 9.1 (2019): 49-63. Abstract Feedback regulation is pervasive in biology at both the organismal and cellular level. In this article, we explore the properties of a particular biomolecular feedback mechanism called antithetic integral feedback, which can … |
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