In this talk, we discuss the paper "Dynamical Mean-Field Theory of Complex Systems on Sparse Directed Networks" by Fernando L. Metz, Phys. Rev. Letters, 2025. Abstract Although real-world complex systems typically interact through sparse and heterogeneous networks, analytic solutions of their dynamics are limited to models with all-to-all interactions. Here, we solve the dynamics of …

In this talk, we discuss the paper "Dosing Time of Day Impacts the Safety of Antiarrhythmic Drugs in a Computational Model of Cardiac Electrophysiology" by Ning Wei and Casey O Diekman, J. Biol. Rhythms, 2025.  Abstract Circadian clocks regulate many aspects of human physiology, including cardiovascular function and drug metabolism. Administering drugs at optimal times of …

In this talk, we discuss the paper "Principled PCA separates signal from noise in omics count data" by Jay S. Stanley III et al., bioarxiv, 2025.  Abstract Principal component analysis (PCA) is indispensable for processing high-throughput omics datasets, as it can extract meaningful biological variability while minimizing the influence of noise. However, the suitability of PCA …

In this talk, we discuss the paper "Homeostatic forces underlying the daily pattern of sleep propensity" by Vasili Kharchenko et al., SLEEP Advances, 2025. Abstract The mismatch between rising sleep need and the fluctuating ability to fall asleep underlies insomnia—the most common sleep disorder—yet remains poorly understood. While sleep need increases steadily with time awake, …

In this talk, we discuss the paper "Quantifying interventional causality by knockoff operation" by Xinyan Zhang and Luonan Chen, Science Advances, 2025. Abstract  Causal inference between measured variables is crucial to understand the underlying mechanism of complex biological processes at a network level but remains challenging in computational biology. We propose an innovative causal criterion, …

In this talk, we discuss the paper "FilterNet: Harnessing Frequency Filters for Time Series Forecasting" by Kun Yi et al., NeurIPS, 2024. Abstract Given the ubiquitous presence of time series data across various domains, precise forecasting of time series holds significant importance and finds widespread real-world applications such as energy, weather, healthcare, etc. While numerous …