Journal Club

In this talk, we discuss the paper "scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction" by Z. Liang et al., arxiv, 2025. Abstract This paper introduces the Single-Cell Perturbation Prediction Diffusion Model (scPPDM), the first diffusion-based framework for single-cell drug-response prediction from scRNA-seq data. scPPDM couples two condition channels, pre-perturbation state and drug with dose, …

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, "A wearable-based aging clock associates with disease and behavior" by A. C. Miller et al., Nature Comm, 2025. Abstract  Aging biomarkers play a vital role in understanding longevity, with the potential to improve clinical decisions and interventions. Existing aging clocks typically use blood, vitals, or imaging collected in …

In this talk, we discuss the paper "Generic Temperature Response of Large Biochemical Networks" by Julian B. Voits and Ulrich S. Schwarz, PRX Life, 2025. Abstract  Biological systems are remarkably susceptible to relatively small temperature changes. The most obvious example is fever, when a modest rise in body temperature of only few Kelvin has strong …

In this talk, we discuss the paper "Multi-Marginal Flow Matching with Adversarially Learnt Interpolants" by O. Kviman et al., 2025, arxiv. Abstract Learning the dynamics of a process given sampled observations at several time points is an important but difficult task in many scientific applications. When no ground-truth trajectories are available, but one has only …

In this talk, we discuss the paper "Intelligent in-cell electrophysiology: Reconstructing intracellular action potentials using a physics-informed deep learning model trained on nanoelectrode array recordings" by K. Rahmani et al., Nat. Comm, 2025. Abstract Intracellular electrophysiology is essential in neuroscience, cardiology, and pharmacology for studying cells’ electrical properties. Traditional methods like patch-clamp are precise but …

In this talk, we discuss the paper "GenAI-Net: A Generative AI Framework for Automated Biomolecular Network Design" by M. Filo et al., arxiv, 2026. Abstract Biomolecular networks underpin emerging technologies in synthetic biology—from robust biomanufacturing and metabolic engineering to smart therapeutics and cell-based diagnostics—and also provide a mechanistic language for understanding complex dynamics in natural …

In this talk, we discuss the paper "TwinCell: Large Causal Cell Model for Reliable and Interpretable Therapeutic Target Prioritisation" by J.-B. Morlot et al., bioarxiv, 2026. Abstract Drug discovery is impeded by the difficulty of translating targets from preclinical models to patients. Here, we present TwinCell, a Large Causal Cell Model (LCCM) capable of generalising …

In this talk, we discuss the paper "A multimodal sleep foundation model for disease prediction" by Rahul Thapa et al., Nature Medicine, 2026. Abstract Sleep is a fundamental biological process with broad implications for physical and mental health, yet its complex relationship with disease remains poorly understood. Polysomnography (PSG)—the gold standard for sleep analysis—captures rich …

In this talk, we discuss the paper "Temporal tissue dynamics from a spatial snapshot" by Jonathan Somer et al., Nature, 2026. Abstract Physiological and pathological processes such as inflammation and cancer emerge from interactions between cells over time1. However, methods to follow cell populations over time within the native context of a human tissue are …

In this talk, we discuss the paper "A multi-agent reinforcement learning framework for exploring dominant strategies in iterated and evolutionary games" by Qi Su et al., Nat. Comm., 2026. Abstract Exploring dominant strategies in iterated games holds theoretical and practical significance across diverse domains. Previous studies, through mathematical analysis of limited cases, have unveiled classic …

In this tallk, we discuss the paper "Discovering network dynamics with neural symbolic regression" by Zihan Yu et al., Nature Com. Science, 2026. Abstract Network dynamics are fundamental to analyzing the properties of high-dimensional complex systems and understanding their behavior. Despite the accumulation of observational data across many domains, mathematical models exist in only a …