Research Supervisor Connect
How do thousands of bacteria coordinate their movements to give rise to complex structures and collective behavior without any central control? Recent advances in high-precision experimental techniques have enabled experimentalists to simultaneously track thousands of individual bacteria. Understanding these systems requires developing new statistical physics frameworks for active matter guided by real experimental data. This project offers a unique opportunity to discover novel phase transitions and collective effects that are absent in equilibrium systems, collaborate with cutting-edge bacterial tracking datasets from international collaborators, inform future experiments, and develop a new lens through which to understand biological complexity. This work will contribute to the foundations of active matter theory, generating new insights that could inform future bioengineering applications.
Recent breakthroughs in bacterial tracking technology now allow simultaneous observation of thousands of individual cells, revealing collective behaviors that cannot be readily explained by equilibrium statistical physics or traditional active matter theory. Bacterial colonies exhibit non-reciprocal interactions where cells respond asymmetrically to their neighbors, leading to phase transitions and pattern formation absent in equilibrium systems. The newly available experimental data offer an unprecedented opportunity to develop new theoretical frameworks grounded in biological reality.
This project will build statistical physics models directly from experimental observations, using maximum entropy methods and Bayesian inference to extract interaction rules from bacterial trajectory data. Working closely with international experimental collaborators, we will develop theories that capture salient collective phenomena in Myxococcus xanthus colonies, including non-reciprocal phase transitions and exceptional point dynamics.
Cultivating a collaboration with a leading experimental group is a key aspect of this project. Our theoretical models will not only explain existing observations but also predict new collective behaviors that can guide future experiments. This iterative process will establish a new paradigm for understanding biological active matter, contributing fundamental insights while informing strategies for controlling bacterial communities in biomedical and biotechnological applications.
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Project keywords:
statistical physics; physics of living systems; biophysics; active matter; phase transitions; collective behavior; non-reciprocal interactions; data-driven modeling; biofilms; bacterial colonies; Myxococcus xanthus
The opportunity ID for this research opportunity is 3659