Journal Club

In this talk, we discuss the paper "High-order Michaelis-Menten equations allow inference of hidden kinetic parameters in enzyme catalysis" by Divya Singh et al., Nat. Comm., 2025. Abstract Single-molecule measurements provide a platform for investigating the dynamical properties of enzymatic reactions. To this end, the single-molecule Michaelis-Menten equation was instrumental as it asserts that the …

In this talk, we discuss the paper “Circadian rhythm profiles derived from accelerometer measures of the sleep-wake cycle in two cohort studies” by Sam vidil et al., Nature Communications, 2025. Abstract: Accelerometers allow objective measures of dimensions (rest-activity rhythm (RAR), daytime activity, sleep, and chronotype) of the bio-behavioural manifestation of circadian rhythm (CR) using multiple …

In this talk, we discuss the paper “Chaos Meets Attention: Transformers for Large-Scale Dynamical Prediction” by Yi He et al., ICML Poster, 2025. Abstract: Generating long-term trajectories of dissipative chaotic systems autoregressively is a highly challenging task. The inherent positive Lyapunov exponents amplify prediction errors over time. Many chaotic systems possess a crucial property — …

In this talk, we discuss the paper “Inferring circadian phases and quantifying biological desynchrony across single-cell transcriptomes” by Andrea Salati et al., bioRxiv, 2026.   Abstract: Single-cell RNA sequencing (scRNA-seq) reveals heterogeneity in circadian clock states across individual cells, yet accurately inferring circadian phase and distinguishing biological desynchrony from technical noise remains challenging. Here, we …

In this talk, we discuss the paper “Insulin resistance prediction from wearables and routine blood biomarkers” by Ahmed A. Metwally et al., Nature, 2026. Abstract: Insulin resistance (IR), a primary precursor to type 2 diabetes, is characterized by impaired insulin action in tissues1. However, diagnostic methods remain expensive and inaccessible, which hinders early intervention2,3. Here …

In this talk, we discuss the paper "A Metabolism-Informed Neural Network Identifies Pathways Influencing the Potency and Toxicity of Antimicrobial Combinations" by Harkirat Sigh Arora et al., npj drug discovery, 2026. Abstract: Antimicrobial resistance poses a major global threat, driven by diminishing efficacy of current treatments and limited new therapies. Combination therapy with existing drugs …

In this talk, we discuss the paper “Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA” by Zhuohan Yu et al., nature communications, 2023. Abstract: Single-cell RNA sequencing provides high-throughput gene expression information to explore cellular heterogeneity at the individual cell level. A major challenge in characterizing high-throughput gene expression …

In this talk, we discuss the paper "Topology identifies concurrent cyclic processes in single-cell transcriptomics and androgen receptor function" by Kelly Maggs et al., bioRxiv, 2025. Abstract: Standard single-cell RNA-seq analysis frameworks aggregate over-lapping biological processes and impose a single parametrization, conflating distinct programs. Here, we introduce a topological framework that detects and disentangles multiple …

In this talk, we discuss the paper “Fast training of accurate physics-informed neural networks without gradient descent” by Chinmay Datar et al., ICLR, 2026. Abstract: Solving time-dependent Partial Differential Equations (PDEs) is one of the most critical problems in computational science. While Physics-Informed Neural Networks (PINNs) offer a promising framework for approximating PDE solutions, their …

In this talk, we discuss the paper "AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML" by Patara Trirat et al, ICML, 2025. Abstract: Automated machine learning (AutoML) accelerates AI development by automating tasks in the development pipeline, such as optimal model search and hyperparameter tuning. Existing AutoML systems often require technical expertise to set up …

In this talk, we discuss the paper "Evolution of error correction through a need for speed" by Riccardo Ravasio et al., Science, 2026. Abstract: Kinetic proofreading is a class of error-correcting mechanisms in biology that expend energy to avoid mistakes during replication, transcription, and translation. Proofreading is typically assumed to evolve when selection for fidelity …

In this talk, we discuss the paper "Dynamical Causality Under Latent Confounders for Biological Network Reconstruction" by Jinling Yang et al, IEEE Transactions on Pattern Analysis and Machine Intelligence (2026). Abstract: Causal interaction inference is prone to spurious causal interactions, due to the substantial confounders in a biological system. While many existing methods attempt to address …