Use of the Atomic Force Microscope (AFM) and mathematical modeling to understand the mechanobiology of cells and tissues
The NIBIB’s Section on Mechanobiology focuses on the use of the Atomic Force Microscope (AFM) and mathematical modeling to understand the mechanobiology of cells and tissues in physiological relevant environments by applying physical sciences and engineering principles. The primary major research project of our laboratory is to understand the molecular-mechanical regulation of the actomyosin cortex of highly metastatic cancer cells and the solid tumor microenvironment for deciphering self-organization in cancer biology. The second major research project is to determine the anisotropic mechanical properties of developmental and mature inner ear sensory and non-sensory epithelial tissues using a novel noncontact AFM approach. Additionally, the laboratory develops new AFM methodologies to study fast multiparametric and multidimensional cellular and tissue processes, and advances the state-of-the-art AFM imaging methods for high spatio-temporal and quantitative nanomechanical mapping.
We propose two summer projects in which the summer intern can decide which is of more interest;
- Use of a novel quasi-static force spectroscopy method to investigate the contribution of actomyosin activity in F-actin cortex self-organization and mechanical regulation in highly metastatic cancer cells.
- Use of a novel noncontact acoustic frequency-modulated AFM method to investigate the anisotropic mechanical properties of developmental and mature in vitro epithelial monolayers and mouse inner ear sensory and non-sensory tissues.
We are looking for motivated undergraduate or graduate students with background in biomedical or mechanical engineering, biophysics, and/or cellular biology. The candidate must have a basic understanding on physics, mechanics, and programming (preferably MATLAB or R). Lastly, the project goals for the summer intern are to: (i) become familiar with the fundamentals principles of AFM; (ii) perform subsequent analysis of acquired AFM data using MATLAB or R; and (iii) learn scientific writing.