Research Interest
We are working in biological physics, a relatively new field of physics 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.
Physics of Active Matter
Active Matter refers broadly to collectives of physical and biological systems, ranging from groups of migrating cells and swarms of birds to animals that convert internal energy into active motion. We are interested in understanding the role of deformability, intrinsic heterogeneity, and surface adhesion in biological collective behavior under various physical environmental constraints using multi-scale simulations.
Microbial Colony Dynamics
Microbial growth on solid surfaces is dictated by single-cell growth determined by underlying genetic networks and cell-cell physical interactions. We aim to develop a multi-scale simulation framework that integrates the dynamics of gene regulatory networks and cell mechanics, driven by physical collisions, to study the surface morphology and spatial expansion of bacterial colonies formed by different types of bacteria.
Image and Data Analysis
Recent advances in experimental and high-resolution microscopy have provided us with unprecedented details of cell shape, mechanics, and cell-cell interaction mechanisms during various biological processes. However, interpreting such large volumes of data requires the development of quantitative data analysis tools that can characterize the data into low-dimensional measures as well as help us develop better theoretical models. We are particularly interested in developing novel algorithms for motion and shape tracking, spatial clustering, image correlations, and distance measurements to characterize experimental datasets.
