Hyeongjun Jang, Generalized Michaelis–Menten rate law with time-varying molecular concentrations

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

We will discuss about “Generalized Michaelis–Menten rate law with time-varying molecular concentrations”, Lim, Roktaek, et al.,bioRxiv (2022): 2022-01   Abstract The Michaelis–Menten (MM) rate law has been the dominant paradigm of modeling biochemical rate processes for over a century with applications in biochemistry, biophysics, cell biology, and chemical engineering. The MM rate law and its

Dongju Lim, Exact solution of a three-stage model of stochastic gene expression including cell-cycle dynamics

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

We will discuss about “Exact solution of a three-stage model of stochastic gene expression including cell-cycle dynamics”, Wang, Yiling, et al., bioRxiv (2023): 2023-08.   Abstract The classical three-stage model of stochastic gene expression predicts the statistics of single cell mRNA and protein number fluctuations as a function of the rates of promoter switching, transcription, translation,

Eui Min Jung, Antithetic proportional-integral feedback for reduced variance and improved control performance of stochastic reaction networks

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

We will discuss about “Antithetic proportional-integral feedback for reduced variance and improved control performance of stochastic reaction networks ”,Briat, Corentin, Ankit Gupta, and Mustafa Khammash., Journal of The Royal Society Interface 15.143 (2018): 20180079 Abstract The ability of a cell to regulate and adapt its internal state in response to unpredictable environmental changes is called

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

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

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

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

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

Dongju Lim, An accurate probabilistic step finder for time-series analysis

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

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

Eui Min Jung, Hard Limits and Performance Tradeoffs in a Class of Antithetic Integral Feedback Networks

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

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

Olive Cawiding, Time delays modulate the stability of complex ecosystems

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

We will discuss about “Time delays modulate the stability of complex ecosystems” Nature Ecology & Evolution 7.10 (2023): 1610-1619. Abstract What drives the stability, or instability, of complex ecosystems? This question sits at the heart of community ecology and has motivated a large body of theoretical work exploring how community properties shape ecosystem dynamics. However, the

Yun Min Song, Pulsed stimuli entrain p53 to synchronize single cells and modulate cell-fate determination

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

We will discuss about “Pulsed stimuli entrain p53 to synchronize single cells and modulate cell-fate determination” bioRxiv (2023): 2023-10. Abstract Entrainment to an external stimulus enables a synchronized oscillatory response across a population of cells, increasing coherent responses by reducing cell-to-cell heterogeneity. It is unclear whether the property of entrainability extends to systems where responses are

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