The development of modern life sciences would not have been possible without mice. As the most widely used mammalian model organism, mouse research has driven major breakthroughs in biology through tools like gene editing, optogenetics, electrophysiology, and advanced imaging techniques.
Our laboratory focuses on identifying the smallest molecular and neural units responsible for the sleep–wake cycle in mice. By comparing these components across species, we aim to define evolutionarily conserved minimal circuits and mechanisms that underlie sleep regulation. Keywords: synaptic plasticity, gene editing, electrophysiology, optogenetics, epigenetics

Modern life science—once centered almost exclusively on mouse models—has reached a turning point. As of 2023, many of the powerful tools once limited to mice can now be applied to a wide range of organisms. Following Krogh’s Principle—“For such a large number of problems, there will be some animal of choice…“—we now have the freedom to choose species best suited to the questions we ask.
Our lab leverages social insects, particularly ants, as a model to study how genetically homogeneous individuals achieve striking phenotypic diversity. Through a combination of molecular biology, physiology, and systems-level analysis, we seek to understand how social behavior and division of labor emerge from shared genomes. Keywords: sociality, gene editing, electrophysiology, mass spectrometry, epigenetics

Biological systems are composed of trillions of cells and billions of base pairs, interacting in highly dynamic and nonlinear ways. To make sense of this complexity, we combine data science and mathematical modeling with biological insight.
Our lab analyzes multimodal datasets—including neural activity, gene expression, and behavioral dynamics—to extract underlying principles from seemingly chaotic systems. By building predictive models grounded in time-series analysis, machine learning, and systems biology, we aim to decode the logic of life across scales. Keywords: machine learning, differential equations, time series analysis, bioinformatics