Research Interest
We are working in active matter and biological physics, a relatively new field of research that combines the knowledge of statistical physics, mathematics, and biology into one framework to provide a quantitative description of complex biological processes. We are particularly interested in comprehending the physics of collective dynamics in living systems at different scales and levels of complexity. Experimentally, we have a detailed understanding of the dynamics of biological processes at individual scales, ranging from small molecular machines to individual cells to colonies and multi-species ecosystems. Surprisingly, we understand little about how these different scales connect to generate complex biological functions. We aim to investigate the influence of individual cell attributes, such as shape, motility, and mechanics, on transport, structural arrangements, and material properties in large-scale collectives like tissues, growing bacterial colonies, and other self-organizing biological structures crucial for diverse biological functions.
Active Matter
Active Matter refers to groups of living and physical systems—like migrating cells, bacterial swarms, or flocks of birds—where each individual converts energy into its own self-propelled motion. Unlike traditional physics systems that settle into an equilibrium, active matter is alive, moving, and constantly operating far from thermodynamic equilibrium. We want to discover how cell shape changes (deformability), differences between individuals (heterogeneity), and surroundings (noise and geometry) allow these groups to work together and move as one. Since these biological groups encounter obstacles like tight spaces or sticky environments, we use multi-scale computer simulations to map their behavior. By combining simple rules for individual cells with physical interactions and appropriate boundaries, we can predict how beautiful, orderly patterns and collective motion naturally emerge from microscopic rules.
Stochastic Processes and Networks
Stochastic processes and non-linear dynamics provide an indispensable framework for modeling the physics of biological processes, where microscopic fluctuations defy deterministic prediction, and non-linearity tunes precise responses. In cellular environments, low molecule numbers and random biochemical collisions generate significant noise that interacts with feedback loops, cooperative binding, and enzymatic saturation. Researchers analyze these phenomena using the Chemical Master Equation (CME) and stochastic differential equations (SDEs). Beyond intracellular pathways, these concepts govern macroscopic systems; for instance, the Kuramoto model describes phase synchronization in the rhythmic, simultaneous flashing of firefly swarms, while coupled differential equations track predator-prey dynamics and synchronization across space.
Image Analysis and Machine Learning
Recent advances in experimental and high-resolution microscopy provide unprecedented details of cell shape, cell mechanics, and cell-cell interaction mechanisms during crucial biological processes. However, interpreting these massive volumes of complex visual data requires quantitative data analysis tools that can simplify the information into low-dimensional measures and help us build better theoretical models. We want to bridge this gap by developing novel algorithms for motion and shape tracking, automated bio-image analysis, and interaction force predictions between moving cells. By also applying machine learning techniques like reinforcement learning, we aim to understand the decision-making rules cells use to navigate their environments. Ultimately, our goal is to turn raw, beautiful microscope images into precise mathematical rules that explain how living systems move, interact, and organize.