CAREER: Modeling and Control of Cellular Response to Dynamic Mechanical Manipulation Using a Dual-Input Platform

点击翻译按钮获取中文摘要

This Faculty Early Career Development Program (CAREER) project will create new models of the way that cells change their shape in response to external forces. The project will include the design of a dual-direction instrument for applying forces to cells -- one that combines a supporting surface that can expand and contract, together with a movable probe that can contact exposed portions of the cell surface. A control algorithm based on the new models will allow scientists and engineers to achieve precise stress and strain trajectories across the cell, and on specific organelles in the cell interior. This award will advance the national health, and promote the progress of the biological sciences. It will provide cell biologists with a new tool for studying the biochemical and mechanical changes that cells undergo in response to external forces, as well as a potential tool for inducing desired cell behaviors, for example, for the synthesis of biomaterials. Integrated educational and outreach activities include the development of new courses in nano-positioning and nano-biomechanics, and an outreach program on the use of nano-biomechanical methods in agriculture.Mechanotransduction is the process of sensing, transmission and response to external mechanical stimuli of living cells. It is essential for maintenance of normal cell, tissue, and organ functioning. This project will build a suite of tools to sense and model cellular mechanotransduction dynamics and 3D dynamic structure, in order to achieve cellular behavior manipulation. The specific objectives of this project are to: (1) build a dual-direction mechanotransduction characterization scheme, which for the first time, uses both scanning probe microscopy and dynamic substrate stimuli and synchronizes optical and biomechanical quantification in biomechanical quantification; (2) create a new optimal excitation force design technique to incorporate and allow linear poroelasticity and nonlinear time-varying viscoelasticity models to be utilized in identification of mechanotransduction dynamics; and (3) formulate a near real-time precision tracking control algorithm to achieve high-efficiency tracking of the varying manipulation force to minimize the initial tracking error and tracking iterations by combining both iterative learning-based control and model predictive control approaches.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.