Journal Club

In this talk, we discuss the paper "Quantifying information accumulation encoded in the dynamics of biochemical signaling" by Y. Tang, et.al, Nature Communications, 2021. Abstract Cellular responses to environmental changes are encoded in the complex temporal patterns of signaling proteins. However, quantifying the accumulation of information over time to direct cellular decision-making remains an unsolved …

In this talk, we discuss the paper "The Large Language Models on Biomedical Data Analysis: A Survey" by Wei Lan et.al, IEEE J. Biomedical and Health Informatics, 2025, at the Journal Club. Abstract  With the rapid development of Large Language Model (LLM) technology, it has become an indispensable force in biomedical data analysis research. However, …

In this talk, we discuss the paper "A biological model of nonlinear dimensionality reduction" by K. Yoshida and T. Toyoizumi, Science Advances, 2025, at the Journal Club. Abstract Obtaining appropriate low-dimensional representations from high-dimensional sensory inputs in an unsupervised manner is essential for straightforward downstream processing. Although nonlinear dimensionality reduction methods such as t-distributed stochastic neighbor …

In this talk, we discuss the paper "Designing microplastic-binding peptides with a variational quantum circuit–based hybrid quantum-classical approach" by R.C. Vendrell et.al., Sci. Adv. 2024 at the Journal Club. Abstract De novo peptide design exhibits great potential in materials engineering, particularly for the use of plastic-binding peptides to help remediate microplastic pollution. There are no …

In this talk, we discuss the paper "Frequency-Dependent Covariance Reveals Critical Spatiotemporal Patterns of Synchronized Activity in the Human Brain" by Rubén Calvo et al., Physical Review Letters 2024, at the Journal Club. Abstract Recent analyses, leveraging advanced theoretical techniques and high-quality data from thousands of simultaneously recorded neurons across regions in the brain, compellingly …

In this talk, we discuss the paper "Accurate predictions on small data with a tabular foundation model" by Noah Hollmann et al., Nature (2025). Abstract Tabular data, spreadsheets organized in rows and columns, are ubiquitous across scientific fields, from biomedicine to particle physics to economics and climate science1,2. The fundamental prediction task of filling in …

In this talk, we discuss the paper, "Entrainment and multi-stability of the p53 oscillator in human cells" by Alba Jiménez et al., Cell Systems, 2024. Abstract  The tumor suppressor p53 responds to cellular stress and activates transcription programs critical for regulating cell fate. DNA damage triggers oscillations in p53 levels with a robust period. Guided by …

In this talk, we discuss the paper "Identifying key drivers in a stochastic dynamical system through estimation of transfer entropy between univariate and multivariate time series" by Julian Lee, Physical Review E, 2025. Abstract  Transfer entropy (TE) is a widely used tool for quantifying causal relationships in stochastic dynamical systems. Traditionally, TE and its conditional …

In this talk, we discuss the paper "Boolean modelling as a logic-based dynamic approach in systems medicine" by Ahmed Abdelmonem Hemedan et al., Computational and Structural biotechnology journal (2022). Abstract  Molecular mechanisms of health and disease are often represented as systems biology diagrams, and the coverage of such representation constantly increases. These static diagrams can …

In this talk, we discuss the paper "Network inference from short, noisy, low time-resolution, partial measurements: Application to C. elegans neuronal calcium dynamics" by Amitava Banerjee, Sarthak Chandra, and Edward Ott, PNAS, 2023. Abstract Network link inference from measured time series data of the behavior of dynamically interacting network nodes is an important problem with wide-ranging applications, e.g., estimating synaptic …

In this talk, we discuss the paper "Direct Estimation of Parameters in ODE Models Using WENDy: Weak-Form Estimation of Nonlinear Dynamics" by David M. Bortz, Daniel A. Messenger, and Vanja Dukic, Bulletin of Mathematical Biology, 2023. Abstract We introduce the Weak-form Estimation of Nonlinear Dynamics (WENDy) method for estimating model parameters for non-linear systems of …

In this talk, we discuss the paper "Deep learning for universal linear embeddings of nonlinear dynamics" by B. Lusch, J. N. Kutz, and S. Brunton, Nat. Comm. 2018. Abstract  Identifying coordinate transformations that make strongly nonlinear dynamics approximately linear has the potential to enable nonlinear prediction, estimation, and control using linear theory. The Koopman operator …