NSF Award
2438345
Flagella and cilia are thin hair-like cellular structures which play an essential role in many basic life processes. By beating rhythmically, flagella and cilia move fluid in the local environment of cells. This biological function enables pulmonary mucus clearance in airways and the transport of ovums from the ovary to the uterus for example. Malfunction of flagella and cilia can lead to a group of serious human disorders, ciliopathies, which cause a heavy economic and disease burden on society. However, despite the ubiquity and importance of flagella and cilia, fundamental biomechanics underlying the fluid transport of beating flagella and cilia are still poorly understood. Particularly, the detailed flow field induced by beating flagella and cilia remains unresolved. Combining state-of-the-art microscopy techniques with data-driven machine learning, the research team aims to address this difficult biomechanical problem. This research will investigate the flow field of healthy flagella as well as those of mutant flagella associated with ciliopathies using synergistic experimental and numerical modeling efforts. A potential solution to remedy the flow deficiency of malfunction flagella will be researched. In addition to the training and research opportunities for undergraduate and graduate students, the project will produce appealing scientific videos and demonstrations to enhance the undergraduate curriculum and enrich outreach activities at the local communities of the two principal investigators. <br/><br/>As a generic model for the morphology and dynamics of flagella and cilia, green algae Chlamydomonas reinhardtii, will be studied in this research program. Optical microscopy will be used to track the three-dimensional (3D) fluid flow around the beating flagella of a single alga at micron scales with sub-millisecond temporal resolutions. Both wild-type and mutant algae of different swimming modes will be investigated. The mechanical efficiency of flagellar dynamics will be analyzed based on the 3D flow field. Moreover, using the experimental flow field as a basis of reference and taking advantage of modern machine-learning algorithms, the team plans to develop a numerical model of maximal simplicity that can quantitatively capture the algal flow. The model will facilitate the study of the optimization and synchronization of flagellar dynamics and the collective dynamics of algal suspensions. Through the collaborative experimental and modeling efforts, the missing link between the flagellar dynamics and the resulting microscopic fluid flow will be revealed by this research.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
